Coordinated Land Use and Transportation Planning A Sketch Modelling Approach

Transcription

1 Coordinated Land Use and Transportation Planning A Sketch Modelling Approach by Marcus J. Williams A thesis submitted in conformity with the requirements for the degree of Masters of Applied Science Department of Civil Engineering University of Toronto Copyright by Marcus J. Williams 2010

2 Coordinated Land Use and Transportation Planning A Sketch Modelling Approach Abstract Marcus J. Williams Masters of Applied Science Department of Civil Engineering University of Toronto 2010 A regional planning model is designed to facilitate coordinated land use and transportation planning, yet have a sufficiently simple structure to enable quick scenario turnaround. The model, TransPLUM, is built on two existing commercial software products: the Population and Land Use Model (PLUM); and a four-stage travel model implemented in a standard software package. Upon creating scenarios users are able to examine a host of results (zonal densities, origin-destination trip flows and travel times by mode, network link flows, etc) which may prompt modification of a reference land use plan and/or network plan. A zonal densityaccessibility ratio is described: an index which identifies the relative utilization of a zone and which could serve as a coordinating feedback mechanism. The model was implemented for a pilot study area the Winnipeg Capital Region. Development of a baseline scenario is discussed. ii

3 Acknowledgments There are many people who helped make this project happen. First, I would like to acknowledge Pille Bunell (Royal Roads University) and Arne Elias (Centre for Sustainable Transportation) for connecting me with staff at the City of Winnipeg. Everyone I worked with at the City went out of their way to contribute: Dianne Himbeault, Michelle Richard, David Houle, Bill Menzies, Bjorn Radstrom, Phil Wiwchar, Doug Hurl, Brett Shenback and of course Susanne Dewey-Povoledo. Susanne saw the value in this project from the onset, became the internal champion and assembled the necessary cross-departmental team for buy-in and implementation. Virgil Martin, a PLUM user extraordinaire at the Region of Waterloo, has always been supportive of PLUM s use in other municipalities and provided valuable advice during this project. whatif? Technologies, through NSERC s Industrial Postgraduate Scholarship, provided not only financial support but also software and expertise. I have Robert Hoffman, Bert McInnis (my industry supervisor), Michael Hoffman and Shona Weldon to thank for this support. The friends I have made at the University of Toronto are too numerous to name. They have helped me through courses and thesis roadblocks. I have shared many meals and drinks with them and look forward to sharing many more. It is my good fortune that Eric J. Miller has trouble saying no, and therefore agreed to take me on as a graduate student. Despite the great demands on his time, Eric always finds time for his students. His guidance, support, wisdom and patience throughout this project were invaluable. His instigation of Friday afternoon visits to O Grady s is appreciated; his knowledge of Star Trek episode plots is most impressive. Finally, my entire family has been extremely supportive of my decision to return to school and for that I am eternally grateful. To my wife Mary thank you and now I can return to being a full-time husband. To my daughter Emma thank you for waiting until the completion of my coursework to be born! iii

4 Table of Contents Acknowledgments... iii Table of Contents... iv List of Tables... vii List of Figures... viii Chapter 1 Introduction Introduction... 1 Chapter 2 Literature Review Literature Review Introduction The Urban Transportation Modelling System (UTMS) Integrated Urban Models / Land Use Models Spatial Interaction / Lowry-type Models Spatial Input-Output Models Microeconomic-based Urban Models Other Sketch Models: Rule-based, GIS and Public Engagement... 9 Chapter 3 Problem Statement Problem Statement and Approach Problem Statement Modelling and Implementation Approach Chapter 4 Pilot Study Area Winnipeg, Manitoba Pilot Study Area Winnipeg, Manitoba Overview Data Sources Chapter 5 TransPLUM Description iv

5 5 TransPLUM Description Overview Overview of the whatif? Modelling Platform PLUM Description Population and New Dwelling Demand Land Use Plan and Allocation Mechanism Employment PLUM s Suitability for Sketch Planning Travel Model Description Travel Model Platform - TransCAD General Travel Model Information Trip Generation Trip Distribution Mode Split Trip Assignment Travel Model Outputs Returned to whatif? Platform Travel Model s Suitability for Sketch Planning TransPLUM run-time performance Chapter 6 Baseline Scenario Baseline Scenario Population, Dwellings and Employment Land Use Plan and Allocation Travel Chapter 7 Coordination Approaches Coordination Approaches Feedback Paradigms v

6 7.2 Land Utilization the Density-Accessibility Ratio Concepts Provisional Results Chapter 8 Conclusion Conclusion Summary of Contributions Evaluation Future Work and Improvements Generic Model Specific Winnipeg Implementation References Appendix A: Survey Trip Purpose to Model Trip Purpose Mapping Appendix B: Trip Distribution Validation Scatterplots Appendix C: Trip Mode Classification Rules Appendix D: Mode Choice Model Estimation Results Appendix E: Availability Restrictions on Transit and Walk-Bike Modes vi

7 List of Tables Table 5-1: Key time and geographic model informants Table 5-2: Key population and dwelling demand model informants Table 5-3: Key employment informant Table 5-4: Trip generation driver definitions. The unit of population is persons; the unit of employment is jobs Table 5-5: Base-year AM peak trips made by Winnipeg residents Table 5-6: Base-year AM peak-hour trip generation rates, in trips per driver unit. Drivers are defined in Table Table 5-7: Calibrated inverse function gravity parameters by trip purpose Table 6-1: Summary of inputs to baseline capacities calculation Table 6-2: Total baseline scenario capacities for the entire Winnipeg Capital Region Table A-1: Survey trip purpose to model purpose mapping where zone of trip origin is the home zone of the trip maker Table A-2: Survey trip purpose to model purpose mapping where zone of trip origin is note the home zone of the trip maker Table C-3: Modes recorded in 2007 Winnipeg Area Travel Survey Table C-4: Mapping from surveyed modes to modelled modes Table E-5: Modelled availability restrictions on the transit mode Table E-6: Modelled availability restrictions on the walk-bike mode vii

8 List of Figures Figure 3-1: TransPLUM conceptual system diagram Figure 4-1: Map showing location of Winnipeg, Manitoba. Source: openstreetmap.com Figure 4-2: Map of the 327 traffic zones comprising the study area (Winnipeg Capital Region). Zones within the City boundary are hatched; outer ring zones are shaded. Source: City of Winnipeg, Public Works Department Figure 4-3: 2008 Winnipeg Capital Region road network Figure 5-1: TransPLUM detailed system diagram Figure 5-2: Top-level model diagram in the whatif? platform Figure 5-3: Example of the whatif? standardized model logic diagram the Population calculator (sub-model) Figure 5-4: Native data visualization tools within the whatif? Platform Figure 5-5: Total person-trips by time of day. 8-9AM peak hour is shaded in red. Source: 2007 Winnipeg Area Travel Survey Figure 5-6: Observed and predicted trip length distributions for AM peak home-to-work trips. 42 Figure 5-7: Observed AM peak-hour mode shares by trip purpose. Source: 2007 Winnipeg Area Travel Survey Figure 5-8: Mode share vs. trip distance for AM peak hour home-to-work trips. Source: 2007 Winnipeg Area Travel Survey Figure 5-9: Predicted AM peak-hour mode shares by trip purpose Figure 5-10: Base-year scaled-symbol auto flow map Figure 6-1: Comparison of Winnipeg TransPLUM baseline scenario to the Conference Board s demographic and economic forecasts viii

9 Figure 6-2: Example stand-alone capacity calculation, shown for the major redevelopment component of residential reurbanization. pz is the geographic index PLUM zone; dt is the index for dwelling type Figure 6-3: Thematic density maps of Winnipeg TransPLUM baseline scenario. All densities are calculated using gross zonal areas Figure 6-4: Projected capacity deficits for the baseline scenario Figure 6-5: Baseline mode share projection, AM peak hour Figure 6-6: Baseline total person travel time over time by mode, AM peak hour Figure 6-7: Baseline auto travel times from various zones to zone 201 (Winnipeg CBD), AM peak Figure 6-8: Thematic employment accessibility maps of Winnipeg TransPLUM baseline scenario. Accessibility is measured in number of jobs accessible within 30 minutes during the AM peak hour Figure 7-1: Planner feedback scheme based on zonal utilization Figure 7-2: Thematic map of utilization indicator from Winnipeg TransPLUM 2006 base year. AM peak hour accessibilities used Figure 7-3: Example of two zones with median utilization values Figure 7-4: Example of downtown zone with low utilization value Figure 7-5: Example of a low-density suburban zone near City boundary Figure 7-6: Zonal utilization indicator values for the baseline scenario, projected over time Figure A-1: 3D barplot of trip frequency by survey trip purpose. AM peak hour trips only Figure B-2: Predicted vs. observed trip flows for super-zone (17 x 17) interchanges ix

10 1 Chapter 1 Introduction 1 Introduction There is a relationship between urban land use and transportation, two of many layers in an urban system. Land use patterns where people live, work, shop and play influence travel patterns and the evolution of transportation infrastructure. At the same time, transportation systems provide accessibility and influence where people engage in activities, and also how urban form changes. This circular relationship occurs in a complex, dynamic manner. Land use and transportation planning in North America have in many respects operated independently of each another, ignoring their natural link. The reasons for this are complex and historically rooted. There are institutional and professional dichotomies (Meyer and Miller, 2001) which silo what should be an integrated urban analysis into separate, weakly-linked agencies. Perhaps, more fundamentally, this disjointed approach is a result of the dominant paradigm in which near-ubiquitous automobile-based mobility has loosened the bonds of the relationship (Miller et al. 1998, 6). The last few decades have begun to show major cracks in the automobile-based planning paradigm as metropolitan areas grapple with issues of congestion, energy, emissions, etc. Therefore, there is a pressing need for a return to a coordinated planning approach. Computable models have long had a role as planning support tools in the urban domain. Urban travel models are standard fare in transportation planning departments and, to a much lesser degree, formal land use models are employed by regional planning organizations. Much criticism has been levied against the practice of urban modelling arguably the most influential is Lee s Requiem for Large-Scale Models (1973). While this project does not directly adopt Lee s framing of the problem, it identifies and addresses two broad concerns regarding the operational state of urban modelling tools. The first concern is the poor support for coordinated (or integrated) land use and transportation planning offered by many of the tools in common use. The second concern is that many operational models (integrated or not) are highly complex. The result is a large effort required to generate and evaluate scenarios, which restricts the feasible breath of scenario analysis a planning organization can cover within real-world time and resource constraints.

11 2 The purpose of this thesis is to develop an operational model to facilitate coordinated land use and transportation planning, yet have a sufficiently simple structure to enable quick scenario turnaround. This project uses the Winnipeg Capital Region in Manitoba, Canada, as a pilot study area. Winnipeg was deemed a suitable pilot area due to its medium-sized population and its relative isolation from other large urban centres, simplifying the needed representation of external factors. The project received support through an NSERC Industrial Postgraduate Scholarship (IPS1) along with the participation of an industry partner, whatif? Technologies Inc. The report is organized as follows. Chapter 2 provides a review of past and current land use and transportation modelling tools. Chapter 3 establishes a role for this research in the context of existing tools and their rate of adoption. Chapter 4 offers background information on the pilot area, the Winnipeg Capital Region, and the sources of available data for the model. Chapter 5 describes the model developed in detail, while Chapter 6 describes outlines the construction of a baseline scenario. Chapter 7 discusses an approach to coordinating land use and transportation planning within the context of the developed model. Chapter 8 summarizes this project s research contribution, evaluates its success and identifies areas for future work and improvement.

12 3 2 Literature Review 2.1 Introduction Chapter 2 Literature Review Soon after the birth of electronic computers, starting in the 1950 s, urban systems analysts began harnessing the new information processing capabilities to create projections of alternate future states via computable mathematical models as long-range planning aids. This modelling approach was first applied to the urban transportation sub-system 1, most notably by the Chicago Area Transportation Study which assembled and developed the methods which became the basis for the standardized and enduring framework known as the urban transportation modelling system (UTMS) or the four-stage model (Black, 1990; Johnston, 2004). Urban transportation planning was and continues to be the most active application area of modelling within the urban systems analysis domain. Yet on the heels of the early transportation modelling work there was a significant research effort occurring in models of urban land use, starting in the 1960 s, which forecast future configurations of urban form and the corresponding spatial distributions of population and economic activity. Many of these land use models were designed to interact with transportation models in that their spatial allocation processes were influenced by transportation measures (e.g., zonal accessibilities) and their outputs could serve as drivers for transportation models. This can be seen as early recognition of the strong land use - transportation relationship on the part of professionals, along with a desire to formally incorporate the link in the planners toolkit. Despite these efforts land use models never achieved the prominence of UTMS but rather experienced a decline and near-total abandonment in the 1970 s (Meyer and Miller, 2001), which may be attributed to the following factors: 1 This report considers only the land use and transportation components of urban systems. Other sub-systems exist (e.g., water distribution, hydrological, etc.) and are subject to their own modelling disciplines.

13 4 U.S. federal transportation funding and legislation which required formal transportation analysis, but no such requirement for land use (Meyer and Miller, 2001; Miller et al., 1998; Johnston, 2004) A dominant laissez faire market-driven development environment in North America, with no perceived need to plan land use (Miller et al., 1998) A general disillusionment with the rational model of planning and the style of models built on that premise, along with the perceived failure of these models to address policy questions (Lee, 1973). Therefore, until quite recently few regions employed formal models to project land use inputs to their transportation models. There has been a revival in the field and a 2009 survey suggests that half of large- and mid-sized U.S. metropolitan planning organizations (MPOs) are engaged in land use modelling (Lee, 2010). Yet there is still a sense among practitioners that land use models are immature with respect to institutional integration and operational policy decision support (Kockelman, 2009). In Canada, operational land use models are rarities; the preparation of land use projections is most often an ad-hoc, judgment-based process which produces a single, fixed forecast. This inhibits feedback of projected transportation conditions to land use plans, and is prone to producing disjoint land use and transportation plans. 2.2 The Urban Transportation Modelling System (UTMS) For transportation planning professionals the urban transportation modelling system (UTMS) or the four-stage model is a universally-understood framework. Generally, it accounts for: person trip flows within a region by origin, destination, purpose and mode; vehicle or passenger volumes by network link; and travel times by network link and origin-destination (O-D) pair (or interchange). Although it has undergone significant advancement over the past 50 years and there are variations in its application, its structure remains largely unchanged. The four stages of the UTMS are:

14 5 1. Trip generation, in which the number of trip ends (productions and attractions) by zone and trip purpose are projected, driven by some unit(s) of zonal activity (e.g., households, population, employment) and their characteristics. 2. Trip distribution, in which trips by zone of origin are distributed to destination zones. The standard approach employs a gravity model in which the trip flow for a given O-D pair is positively influenced by levels of activity contained in the two zones, and negatively influenced by the zone-to-zone impedance (travel time and/or cost). 3. Mode split. Here, the total trip flow between each O-D pair is split among the various modelled modes (e.g., auto-drive, auto-passenger, transit, walk, bike, etc.) based on some combination of modal and trip maker attributes. 4. Trip assignment, in which the modal O-D demands are loaded onto their respective networks, traverse actual routes and yield flow rates on individual network links. Some variation on the ordering of the above stages exists specifically trip distribution and mode split as well as varying methodological sophistication of individual stages and inter-stage feedback (i.e., equilibration). Further discussion of the UTMS is found in Ortuzar and Willumsen (2001) and Meyer and Miller (2001). It should be noted that UTMS is: static, as it represents travel over a particular time period with a single state; and trip-based, as its primitive unit of travel demand is a point-to-point trip. The limitations these features impose on travel analysis have spurred much research in dynamic and activity/tour-based methods (Jones, 1990). At present, however, the four-stage model remains the dominant framework for operational transportation policy analysis and planning. 2.3 Integrated Urban Models / Land Use Models The term integrated urban model describes a model which brings together urban form and travel analysis, and is sometimes used interchangeably with land use model. This is potentially confusing because within the group of land use models the degree of integration with transportation varies considerably. Some include, or connect to, fully-blown transportation models; others incorporate transportation-related measures in a much more indirect, static manner. The following Sections present models which range from deep-integration

15 6 to stand-alone land use projection. The sections are: spatial interaction or Lowry-type; spatial input-output; microeconomic-based; and various other sketch models employing rule-based methods and/or GIS platforms, several of which are oriented towards public engagement. The following sections draw from various reviews of integrated urban models: Hunt et al. (2005), Kosterman and Petit (2005), Miller et al. (1998), Miller (2009), Southworth (1995) and Wegener (1995) Spatial Interaction / Lowry-type Models From a historical perspective the Lowry model (Lowry, 1964) is generally considered the most influential land use model the causal logic and spatial interaction concepts it employs are widely used in subsequent generations of land use models (Horowitz, 2004). The Lowry model is premised on the notion that a region s basic employment employment that serves markets outside the region acts as an anchor which determines the distribution of regional population and service-based (i.e., local) employment. The nature of the distribution is such that: population is concentrated in areas with high accessibility to employment, and servicebased employment is concentrated in areas with high accessibility to population and employment. Spatial interaction of this type is described as gravity distribution, similar to the gravity-based trip distribution procedure used in four-stage travel models but working with population and employment rather than trip ends. The original Lowry model was specified as a sequence of equations to be solved through an iterative procedure; however, it was later reformulated by Garin (1966) as a matrix-based procedure which allows a direct solution (Meyer and Miller, 2001). The Lowry model can be run stand-alone but it is also well-suited to being connected to a travel forecasting model (Horowitz, 2004). In this configuration The Lowry model provides population and employment distributions based on assumed travel impedances to the travel model, which calculates updated impedances to be fed back into the Lowry model. This loop is iterated until equilibration. A widely used Lowry-type integrated urban model is the Integrated Transportation and Land-Use Package (ITLUP), which contains the Disaggregate Residential Allocation Model (DRAM) and the Employment Allocation Model (EMPAL), developed by Putman (1995).

16 7 Lowry-type models are inherently static equilibrium-based, although they can be made quasidynamic by adding increments of basic employment at successive points over a planning horizon (Meyer and Miller, 2001). Due to the fact that Lowry-type models re-construct a city based on projections of basic employment, they are poor at taking base-year development into consideration. However, relative to subsequent generations of urban models they have relatively modest data requirements Spatial Input-Output Models Based on the legacy of the Lowry model is a family of integrated models which further articulates interactions among employment sectors and households, giving rise to activity location and transportation demand. Of this family the MEPLAN package developed by Echenique (1990) appears to have had the most extensive regional application. MEPLAN employs a spatial input-output structure which accounts for producers and consumers (called factors) of goods and services, their interactions, and intensities (or technical coefficients). Households are included in this structure as both producers and consumers. As producers they supply labour to employers (resulting in work trips); as consumers they require goods and services (resulting in shopping, service, delivery, etc. type trips). Land and floorspace are considered non-transportable producer-type factors, serving households and employers. Exogenous consumption and production similar to the basic employment of the Lowry model serve as the starting point for expanding intermediate economic activity according to the inputoutput coefficients. Production factors are allocated to zones using discrete choice models which take into account zonal production costs (including land prices) and travel impedances to zones of consumption. Land prices are determined endogenously through an iterative procedure which aligns land demand (elastic to price) with land supply, specified by zonal capacity constraints. Although each MEPLAN state is fundamentally a static equilibrium, the model provides a simulated dynamic through the variation of exogenous consumption and land constraints over a sequence of time periods. Furthermore, delayed behavioural responses are represented though selected time lags for example, activity location at time period t is influenced by travel model impedances from the previous period, t-1.

17 8 A characteristic of MEPLAN which is telling of its fundamentally integrated nature is the fact that a distinct trip distribution stage is not required for its travel model component trip distribution is a direct result of its core input-output social accounting structure. There are two models which are direct descendents of MEPLAN: TRANUS (Modelistica, 2007); and PECAS (Hunt and Abraham, 2003) which, according to a recent survey of U.S. MPOs (Lee, 2010), has an estimated market share of 9% (of the MPOs with land use models) Microeconomic-based Urban Models Much research in integrated urban model over the last two decades has been directed towards an increasingly detailed representation of urban land markets, the relevant actors and the application of microeconomic theory governing their behaviour. In addition, some of the resulting models present dynamic, non-equilibrium based frameworks for evolving urban form. This section discusses a selection of these models. MUSSA ( Modelo de Uso de Suelo de Santiago ) developed by Martinez (1996) is based on bid-choice theory (Alonso, 1964; Ellickson, 1981) in which individual firms or households bid for space up to a maximum value, or willingness to pay. Firms and households try to maximize the difference between their willingness to pay and the rent they actually pay (consumer surplus); and landlords rent to the highest bidder. The model assumes a static equilibrium in which supply equals demand, all households are assigned dwellings and geographically located, and capacity constraints are not exceeded. Households are finely disaggregated. The land use model equilibrates in conjunction with a connected four-stage travel model. Another static equilibrium model with a strong microeconomic orientation is METROSIM (Anas, 1995), which has been applied to Chicago and New York City. UrbanSim (Waddell et al., 2003) is an integrated model which has an estimated market share of 15% of U.S. MPOs (Lee, 2010), representing the urban model with the single largest installation base. In many respects UrbanSim is influenced by MUSSA: buyers bid based on their willingness to pay and attempt to maximize their surplus; sellers attempt to maximize price paid. However, the equilibrium assumption is relaxed and the building stock is evolved through a dynamic disequilibrium. While many of the actors in UrbanSim are highly disaggregated (e.g.,

18 9 households), workplace choice is made in a connected travel model. In other words, place-ofresidence to place-of-work linkages are not integrated across the land use and travel sub-models. There have been several major research efforts into true agent-based microsimulation frameworks in which individual persons, households, firms, buildings, dwellings, vehicles, etc. evolve and interact explicitly in a dynamic, non-equilibrium, integrated framework. Examples of such models are ILUTE (Salvini and Miller, 2005), PUMA (Ettema et al., 2007) and ILUMASS (Strauch et al., 2003). These models offer the potential to explore and simulate the behaviour of urban socio-economic systems at an extremely fine level of detail and fidelity; to date they have been exercised in academic, rather than operational planning environments Other Sketch Models: Rule-based, GIS and Public Engagement There are many examples of lightweight or sketch urban planning support tools at least relative to the model classes presented in the preceding Sections which employ less data-intensive and/or less theory-rich approaches in favour of some combination of: rapid scenario turnaround, impact analysis, visualization and community engagement / consensus building. The California Urban Futures (CUF) land use model (Landis, 2001) is an example of a rulebased approach. A detailed spatial database consisting of environmental, market and policy layers is processed to define a collection of irregular developable land units. These units are scored and sorted according to the potential profitability attributed to their development. Regression-based projections of population growth, at the municipal level, are allocated to the developable land units in sequential order according to their profitability ranking. A subsequent generation of the model, CUF II, replaces the profitability-driven allocation process with statistical state-change models applied to 1-hectare grid cells. The Ohio-based What if? software package (Klosterman, 2001) not to be confused with the whatif? Modelling Platform used in this project 2 is similar to the CUF model in that it adopts a 2 The What if? urban planning support system ( is developed by Richard E. Klosterman, Professor Emeritus at the University of Akron. whatif? Technologies Inc. ( is an Ottawa-based consulting firm and developer of the whatif? Modelling Platform used in this project. The two firms

19 10 rule-based allocation method, but is oriented towards a user-friendly GIS interface for determining the relative suitability of locations for development. UPlan (Walker et al., 2007) is another GIS-based land use allocation model which operates at a highly resolved geographic scale 50 x 50 m grid cells. Each cell is assigned a composite development attractiveness value based on proximity to transportation and other infrastructure. Exogenous population and employment growth projections drive the demand for new land development which is allocated to cells based on their attractiveness. There are several software tools geared towards visualization of land use scenarios and impact assessment for public engagement: Index (Allen, 2001), Community Viz (Kwartler and Bernard, 2001), PLACE3S (Hanson and McKeever, 2009), and MetroQuest (2010). Community Viz is noted for its ability to generate 3D bird s-eye views of potential land use scenarios. PLACE3S and MetroQuest offer web-based access through which members of the public can directly explore scenarios and impacts. These packages are generally built on GIS platforms. The tools listed in this section are generally not tightly integrated with travel models; rather they are used as stand-alone packages which accept travel-related measures from or output land use results to travel models, but do not explicitly close the land use-transportation loop. One exception is UPlan, whose design allows (but does not require) a direct interface to travel models. and their platforms are not related; the similar product names were independently trademarked in the U.S. and Canada in the s.

20 11 Chapter 3 Problem Statement 3 Problem Statement and Approach 3.1 Problem Statement Chapter 2 briefly describes the history and current state of integrated land use and transportation modelling tools. While significant research and development effort has been invested in these tools large-scale integrated models (Sections ), and also land use-oriented sketch models (Section 2.3.4) there appears to be a dearth of contemporary tools with both an integrated and sketch orientation. This observation matters because it identifies a largely underserved segment of model offerings, which, if filled could provide better planning support to regions. Large-scale integrated models by definition provide an integrated view of the land use - transportation planning problem but require large volumes of data and significant human resources to operate, often making them ineffective for multi-scenario analysis within the budgets and time constraints of real-world planning initiatives. In a recent survey of Canadian planning agencies, Hatzopoulou and Miller (2009) cite a lack of resources as one of the major challenges facing institutions with respect to urban modelling. Sketch-type land use models, on the other hand, are suited to quicker scenario turnaround and are less resource intensive but generally provide a partial analysis, failing to adequately address the land use - transportation link. Therefore, this project attempts to develop a model to enable quick-turnaround, yet coordinated land use and transportation scenario analysis at the regional scale. It aims to combine a judgment and scenario-based process with the rigour of a dynamic, quantitative accounting framework. A point of note regarding word choice in the above statement: coordinated is used here, and also in the title of this report rather the more common qualifier integrated found in the literature within this context. Integrated is sometimes used in a specific model-structure sense to describe models in which location choice (e.g., residential) and trip destination (e.g., for home-to-work trips) are generated from the same underlying relationships (e.g., place-of-work to place-of-

21 12 residence linkages). While this specific, technical meaning of integrated does not describe this project s chosen sketch approach, outlined in the following Section 3.2, the broader meaning of the term is certainly applicable to this project s goals. Ultimately it was felt that coordinated conveys much of the same holistic intent as integrated without the specific model architecture implications. 3.2 Modelling and Implementation Approach Several premises guided the development of the solution: 1. The model can be constructed largely based on existing methods and software technology; as such, much of the project can be viewed as an exercise in design and integration, as opposed to more basic research into model sub-components. 2. Interface matters. As far as possible the model interface should be transparent with respect to model structure, data and assumptions. Scenarios should be readily created, debriefed, compared to each other and modified. These attributes enhance the credibility of any planning model, and also the level of productivity offered. 3. The core should be largely agnostic with respect to behaviour in essence it should be an accounting framework upon which users can construct interchangeable, alternate future scenarios (Gault et al., 1987). Therefore, the chosen approach builds on a pre-existing land use model and platform the Population and Land Use Model (PLUM) developed by whatif? Technologies Inc. and connects to a conventional 4-stage travel model. The combined solution is called TransPLUM.

22 13 Strategic Policy Controls Land Use Plan Population and Land Use Model (PLUM) population & employment allocations Multi-modal Network Plan 4-stage travel model Planner Feedback Figure 3-1: TransPLUM conceptual system diagram Figure 3-1 presents a conceptual diagram of the approach, which exposes two main classes of policy control variables to the user: the Land Use Plan and the Multi-modal Network Plan. The Land Use Plan is a geographically-explicit set of parameters which reflects a zoning (type and intensity of development) and phasing (relative sequencing of development) scheme. When the plan is applied to a projected stream of regional development in PLUM, the result is a timevarying land use projection and an associated population and employment allocation. The allocation is passed to the travel model, which in combination with an evolving Multi-modal Network Plan projects a sequence of future system travel states. The configuration described above constitutes a core, a-cyclical framework for projecting future land use and travel states. The dotted-line connection labeled Planner Feedback in Figure 3-1 represents the discretionary capability of the user to adjust a reference land use - transportation plan combination in response to its expected outcome. This connection offers a means of coordinating land use and transportation plans, but intrinsically it neither enforces nor prescribes a coordination scheme. In this sense, the core approach can be thought of as descriptive rather than normative.

23 14 PLUM is implemented on the whatif? Modelling Platform which also serves as the integrating platform and primary user interface for TransPLUM, due to its model structure diagrammatics, multi-dimensional array language, data visualization and scenario management capabilities.

24 15 Chapter 4 Pilot Study Area Winnipeg, Manitoba 4 Pilot Study Area Winnipeg, Manitoba 4.1 Overview One of the project s goals is that the model structure is applicable to an arbitrary region, consistent with the commercialization guidelines of the NSERC Industrial Postgraduate Scholarship. It was determined that the participation of a pilot region would be beneficial a key factor in the market-readiness of the product with respect to data availability but also with respect to the credibility gained from working with a real client. As a result the City of Winnipeg, Manitoba (shown on a map in Figure 4-1) was engaged as a project partner and pilot municipality. Winnipeg was deemed to be a suitable choice due to its medium size it is the 7 th largest Canadian municipality by population (Statistics Canada, 2010). Also, its relative isolation from other large urban centres means that it approximates a closed system with respect to commuter travel, simplifying the representation of externally generated travel demand. The 2006 Census of Canada population count for the City of Winnipeg is 633,000. The larger Winnipeg Capital Region the City plus 15 surrounding towns and rural municipalities has a count of 732,000 (Statistics Canada, 2007). It is this Capital Region which defines the study area. The rationale for this choice is: the Capital Region represents most of the catchment area for trips to and from the City; and much of the land expected to absorb future regional development falls outside City boundaries but within the Capital Region. A map of traffic zones comprising the study area is shown in Figure 4-2. During the 1990 s the region experienced low and even negative population growth rates. The last decade has shown modest population growth, and from 2009 to 2031 approximately 220,000 additional people are projected for the region by the City of Winnipeg - Office of the CFO (2009). The City and surrounding municipal governments face the challenge of managing this growth with respect to built form, but also with respect to sustainable transportation infrastructure. Currently, private automobile is the dominant mode of travel, and public transit is provided by a conventional bus transit system. Construction is underway on a bus rapid transit

25 16 corridor in the Southwest quadrant of the city; however, there is active debate as to the extent and type of rapid transit coverage which should be built for the rest of the city. Winnipeg, Manitoba Figure 4-1: Map showing location of Winnipeg, Manitoba. Source: openstreetmap.com.

26 17 Figure 4-2: Map of the 327 traffic zones comprising the study area (Winnipeg Capital Region). Zones within the City boundary are hatched; outer ring zones are shaded. Source: City of Winnipeg, Public Works Department. 4.2 Data Sources The major data sources relevant to the Winnipeg model are described as follows: 2006 Census custom tabulations in Winnipeg traffic zones. The City of Winnipeg obtained from Statistics Canada a variety of population, dwelling, household and employment data from the 2006 Census, custom-tabulated to the City s traffic zone system (zones shown in Figure 4-2). Some zones are consolidated in order to minimize data suppression for cross-tabulated datasets, but population and employment count totals

27 18 are provided in the full non-consolidated traffic zone geography. These data provide key base-year distributions for the model. CANSIM and historical Census data standard Census geographies. Statistics Canada s CANSIM is a key source of historical time-series data. In particular it contains age-profiled population, migration, fertility, mortality and employment data. Typical CANSIM dissemination geographies are relatively coarse Census Metropolitan Areas (CMAs) and Provinces therefore CANSIM datasets are subject to scaling and adjustment in preparing estimates for the study area. Historical Census data, available at 5-year intervals, provide periodic control points in the assembly of a historical demographic database. Used at the Census Subdivision (CSD) geography the Census data can be aggregated to directly match the study area. These data are important for historical analysis or calibration of the regional population model City of Winnipeg Assessment Parcel Database. For this project the City made available its GIS-based parcel database of approximately 207,000 records. Key attributes are predominant parcel use (of which there are 119 codes) and number of dwelling units. Also available is the related Commercial and Industrial Buildings database which includes attributes for building footprint area, number of stories, year built and construction type. However, as its name suggests, it excludes several types of place-ofemployment buildings such as schools, hospitals, libraries and (surprisingly) hotels. Both these datasets are confined to properties and buildings within City boundaries, leaving a data gap for the outer ring rural municipality zones. The parcel dataset offers an alternate or supplementary source of housing stock data to the traffic-zone tabulated 2006 Census data. The Commercial and Industrial Building database provides a partial source of employment floorspace data. The fact that individual parcels are provided as discrete geo-referenced objects offers great flexibility in tabulating this data to an arbitrary zone system. However, the assessment database was not used in the final model presented in this report due to discrepancies with Census data 3 and insufficient time in which to address them. 3 A common challenge for land use analysts. Also noted by Martin (2010), another PLUM user.

28 Winnipeg Area Travel Survey. The City commissioned an origin-destination travel survey which was conducted in the Fall of It sampled over 15,000 households in Winnipeg and within a 100 km radius, representing approximately 4.4% of the City s households. It provides complete coverage of the Capital Region study area, but with one caveat: it represents trips made within, to or from Winnipeg but not those occurring exclusively within the outer ring. Results of the survey are described by itrans Consulting Inc. (2009) road network. Winnipeg s Public Works Department maintains a detailed GIS-for- Transportation road network within the TransCAD software environment, shown in Figure 4-3. It includes highway, arterial and local road classifications; and also link attributes such as speed limit, free-flow speed, vehicle capacity and volume counts (on selected links). The network extends beyond City boundaries to cover the study area. In addition, Public Works maintains a database of proposed future road improvements in the same format transit data. Winnipeg Transit, operator of the City s bus-only public transit service, maintains a detailed database of geo-coded stops, routes and schedules. In addition, its bus fleet is equipped with automatic vehicle locator (AVL) and automatic passenger counter (APC) technology which enables the collection of detailed ridership and utilization records. Transit data from the Fall 2007 booking was selected for use in this project due to its coincidence with the 2007 Winnipeg Area Travel Survey. The database is described by Winnipeg Transit (2006).

29 Figure 4-3: 2008 Winnipeg Capital Region road network 20

30 21 5 TransPLUM Description 5.1 Overview Chapter 5 TransPLUM Description Section 3.2 introduced the modelling and implementation approach of TransPLUM. This section provides a richer description of the platform and the model. Figure 5-1 below presents a further articulated system diagram than the conceptual Figure 3-1, showing the main sub-components of PLUM, the 4-stage travel model and the primary information flows among the components. Items in the upper half of the diagram constitute PLUM (with the exception of the Multi-modal Network Plan policy control); the lower half represents a 4-stage travel model. This diagram serves as an important reference throughout chapter 5.

31 22 Regional Population Projection Households Employment Base Dwellings Stock New Dwellings Required Strategic Policy Controls New Employment Space Required Base Employment Stock Allocate New Dwellings Residential Land Use Plan Employment Land Use Plan Allocate New Employment Space Residential Greenfield & Reurbanization Development Multi-modal Network Plan Employment Greenfield & Reurbanization Development Geographically Distributed Population Planner Feedback Geographically Distributed Employment PLUM 4-stage travel model Trip Generation Trip Distribution Mode Split Base Multi- Modal Network Trip Assignment Figure 5-1: TransPLUM detailed system diagram

32 Overview of the whatif? Modelling Platform Before delving into the specifics of TransPLUM it is worth providing some description of the whatif? Modelling Platform, PLUM s native modelling environment and the integrating platform selected for the implementation of TransPLUM. Figure 5-2 shows a partial view of TransPLUM s top-level model organization diagram in the whatif? software platform. Shaded boxes represent sub-models, or calculators. The white boxes, the oval-shaped node and connecting arrows simply provide a hierarchical organizational structure for the calculators and have no bearing on the model s logical content. Many elements in the implementation-level Figure 5-2 map to blocks in the system diagram, Figure 5-1. Figure 5-2: Top-level model diagram in the whatif? platform Figure 5-3 shows the internal structure of the Population calculator, an example of the standardized whatif? model logic diagrams. Note: Vertical cylinders represent stock variables, horizontal cylinders represent flow variables and hexagons represent parameter variables.

33 24 Rectangles represent procedures and contain readily-viewable code for transforming input variables into outputs. The code employed is a multi-dimensional array manipulation language called TOOL. The names of the data objects are followed by square brackets which contain a list of codes. These codes, termed informants, identify classifying dimensions across which a variable is defined and can be used across multiple variables. For example, the informant a is a classifying age sequence, in this case from 0 to 100+ in single years of age. The stock-type variable population indexed with [s,ts,a] is a 3-dimensional array object defined across sex, time and age. Figure 5-3: Example of the whatif? standardized model logic diagram the Population calculator (sub-model) The population variable in Figure 5-3 has several associated shaded tags. These indicate that population is a shared variable, used in other calculators within TransPLUM, and these tags can be used to navigate directly to those calculators.

34 25 Figure 5-4 (a), (b) and (c) show the data visualization options built into the platform graph, table and geographic displays instantly accessible by clicking on variables in the model diagram. Figure 5-4 (d) shows a comparison of two scenarios within a graph display. In addition to these native display tools the platform provides data interchange capability with other standard analytical tools such as spreadsheets and GIS software. (a) Graph display (b) Table display (c) Geomap display (d) Scenario comparison display within graph Figure 5-4: Native data visualization tools within the whatif? Platform The platform natively supports scenario management. Each variable can be assigned multiple instances (or assumptions); therefore, a scenario is the specification of a particular instance for each variable in a model.

35 26 The platform offers an integrated scripting environment for calculations which occur outside the hard coded model logic in the diagrams, useful for pre- and post-processing tasks. These scripts, known as views, are written in the same TOOL language contained in the diagram s procedure boxes. The nature of developing and modifying models in the whatif? Modelling Platform is one of drag and drop diagram operations, coding and informant specification. This flexibility was used to customize the pre-existing PLUM structure to the Winnipeg application, as well as extend the logic to wrap around a travel model. 5.3 PLUM Description The Population and Land Use Model (PLUM) 4 is an operational model developed by whatif? Technologies Inc. 5 in close cooperation with the Region of Waterloo, Ontario, where the model actively supports growth management policy analysis. PLUM was commissioned in 2000 and has since evolved through numerous versions (Martin, 2009); it has also been applied to the Region of Peel (Ontario, Canada) and the State of Victoria, Australia (Baynes et al., 2009), in modified form. Much of PLUM s structure is adapted from the firm s earlier work on the broader Waterloo Region Planning Framework (Bish and Hoffman, 1993). In the following description, where project- and Winnipeg-specific requirements resulted in notable variations from other PLUM implementations the model will be referred to as Winnipeg PLUM. PLUM generates regional population and employment projections and in conjunction with userspecified land use policies it produces land use projections and associated population and employment allocations. The fundamental time and geographic informants (or dimensions) used by Winnipeg PLUM are listed in Table 5-1. Simulation time is the time horizon over which the model projects urban states. Historical time is the period over which the model captures internally-consistent historical demographic data. Both the simulation and historical time periods have respective 4 This is different from the legacy Projective Land Use Model (PLUM) by Goldner (1968). 5 whatif? Technologies Inc. is an Ottawa, Ontario based modelling consultancy and developer of the whatif? Modelling Platform.

36 27 starting points, or base years. PLUM Zone is the geographic zone system in which the land use model operates. These informants are shared with the travel model a convenient design decision for bridging the data connection. Table 5-1: Key time and geographic model informants Informant Name Description Simulation time 2007 to 2056 in one-year steps Historical time 1992 to 2006 in one-year steps Base year 2006 Historical base year 1991 PLUM Zone The primary geographic system; the 327-zone traffic zone system shown in Figure 4-2 The following Sections describe the flow of model logic presented in the PLUM portion of the system diagram in Figure 5-1. In these sections italicized text generally refers to specific boxes in the diagram Population and New Dwelling Demand PLUM s sequence of calculations starts with Regional Population Projection. A population cohort-survival model generates a population forecast for the entire regional study area (i.e., no geographic disaggregation) over the model s 50-year simulation time horizon. Variables are stratified by age and sex, and the model accounts for the standard components of change: immigration, emigration, births and deaths. The cohort-survival method used by PLUM evolves the population one year at a time from a known starting point (the base year) by shifting the population of each age-and-sex cohort forward by a year, subject to assumed age-and-sex specific mortality rates (hence the survival label). Births are calculated using assumed mothers age-specific fertility rates. Assumed age-and-sex stratified immigration and emigration flows are added to and subtracted from the regional population. Note that the cohort-survival model diagram is shown as the example in Figure 5-3. Next, the population projection is split between population in collectives (e.g., nursing and retirement homes) and population in dwellings, using exogenous age-and-sex related propensities. Within Households, a household formation rate is applied to the population in dwellings to yield a projection of households by household size. In Winnipeg PLUM, households are treated as

37 28 equivalent to dwellings 6 and so a projection of total regional dwellings required (i.e., total demand) is available. The calculation of New Dwellings Required incorporates the projected total demand for dwellings and the Base Dwellings Stock by dwelling type. Required assumptions include the mix of new dwelling types and base stock removal rates (i.e., demolition rates). An accounting procedure determines the stream of new dwellings needed to keep the total stock supplied commensurate with the total stock demanded, and the composition of that stream is set by an assumed mix of new dwelling types. Table 5-2: Key population and dwelling demand model informants Informant Name Age Household size Dwelling type Description 0 to 100+ in single-year-of-age increments 1 to 6+ in single-persons-per-household increments Set: - Single detached - Semi-detached or duplex - Row house or townhouse - Apartment, up to 4 storeys - Apartment, 5 or more storeys The population portion of PLUM is calibrated such that the same model structure used for simulation is run over historical time to generate an internally-consistent historical time series. This historical series is useful for trend analysis (e.g., shifting fertility by mothers age, increasing retirement age). In the case of Winnipeg PLUM the closure error method of population calibration is used. In this method the population cohort-survival model is sequentially run on 5-year historical segments, and the resulting year-5 age-sex stratified population is compared to the observed Census count for the same year. The difference termed the closure error is reduced to zero (within a specified tolerance) by iteratively adjusting inputs to the population model. The available Statistics Canada data on births and deaths were taken to be more reliable than the available immigration and emigration data for the study area. Therefore, net immigration was treated as the free variable and adjusted so that the error converged to zero. 6 The PLUM structure allows for a non-unity household-to-dwelling formation rate (e.g., recreational homes, multihousehold dwellings) but in practice this is set to one.

38 29 While PLUM also allows for calibration of the dwelling demand model structure, there were challenges in reconciling various CANSIM and historical Census datasets for the Winnipeg Capital Region against the 2006 Census custom-tabulated data. This led to the decision to override the model s base year dwelling stock with the custom-tabulated data, rather than use the evolved stock from the historical model. Key population and dwelling informants are presented in Table Land Use Plan and Allocation Mechanism At the heart of PLUM is an allocation mechanism which takes a regional projected stream of New Dwellings Required and distributes it to the model s geographic zone system, over the simulation time horizon, according to a user-specified Residential Land Use Plan. For each zone in the study area the land use plan specifies two main variables: Capacity, also called mature state, is a measure of a zone s potential for development. It is stated in number of dwelling units and answers the question How many dwelling units could this zone contain if fully built out? On its own, capacity does not determine when or even if a zone will experience development. Priority is a ranking parameter which controls when a zone accepts development, relative to other zones. Zones with higher priority receive development before those with lower priority. Multiple zones can be assigned the same priority level, in which case they receive development simultaneously in proportion to their available capacity. Both of the above variables are judgement-based policy controls which can be informed by alternate zoning schemes, density targets and phasing assumptions. The allocation mechanism also provides additional tweaking parameters for finer control of the process, such as a zonal fill speed regulator, if desired. The reader will recall from Section that the New Dwellings Required demand is stratified by dwelling type (see Table 5-2 for dwelling type categories). Before this demand is geographically allocated an additional classification is applied a split between greenfield and reurbanization type development. Greenfield development is that which occurs on previously

39 30 un-serviced land; reurbanization occurs in already-developed areas, typically through infill or redevelopment. The split is applied as an exogenous share variable, by dwelling type. In Winnipeg PLUM, the resulting new dwellings demand stream is classified 10 ways (5 dwelling types by 2 development types) and in fact there are 10 corresponding independent allocation processes and land use plans. There are two distinctions between the greenfield and reurbanization allocation processes worth noting: Reurbanization dwelling stocks are pre-filled with base-year dwelling counts; at the first simulation time period their available capacities equal the difference between their capacities and their base year levels. In other words, reurbanization capacity includes the base stock level. By definition, no greenfield dwellings exist in the base year greenfield development is a future-only model concept and as such greenfield dwelling stocks begin the simulation period empty. The reurbanization allocation process accepts projected dwelling removals from the base stock (by zone and by dwelling type); this means that new available capacity may become available due to removals. The greenfield allocation process does not allow for stock removals. Should the allocation process not have sufficient capacity to meet demand, this condition is reported via a deficit output variable. A non-zero deficit implies an infeasible scenario, to which the user may respond by adjusting the demand stream and/or the planned zonal capacities. In the final step of the allocation process the Geographically Distributed Population is calculated. This is achieved by applying an estimate of persons per dwelling unit (by dwelling type, by zone) to the already-allocated dwelling units to yield estimated population by zone. This estimated population is then uniformly scaled so that its total matches the control total from the Regional Population Projection. Note that the allocated population is not stratified by age and sex; it is provided as total population by zone. PLUM can also account for recently-built stock i.e., development which occurs since the most recent census count, monitored through municipal building records although this was not utilized in the pilot version of Winnipeg PLUM.

40 Employment The preceding Sections and describe the population (top) and residential (left side) components of the PLUM system diagram in Figure 5-1. The right side of the diagram represents employment projection and allocation which essentially parallels the residential process, as suggested by the symmetrical diagram layout. Where the residential process allocates dwellings and population, the employment process allocates employment-related floorspace and jobs. The Employment box takes a projection of population in dwellings and applies an age-and-sexbased employment participation rate to yield a projected regional labour force. Two subsequent share variables are used to project employment (i.e., jobs within the Capital Region), taking out live in, work out workers and adding in live out, work in workers. The same accounting procedure used to determine New Dwellings Required on the residential side is used for New Employment Space Required. In this case the variable being determined is the stream of new employment needed to keep the total employment supplied (i.e., base employment stock plus net employment flow) commensurate with the total employment projected. The new employment required is converted to new employment-related floorspace required using an assumed average space per new employee, by employment type. Other assumptions include the mix of new employment types and stock removal rates for base employment. New employment space is geographically allocated using the same mechanism as the residential side, using the same land use plan controls: capacity (stated in square feet of floorspace) and priority. Again, the greenfield-reurbanization distinction is made. PLUM can explicitly allocate population related employment i.e., retail and service employment which serves local communities, and therefore follows residential development although this was not utilized in the pilot version of Winnipeg PLUM. The final step of the employment allocation process is the calculation of employment (jobs) by zone. The key employment sector informant is presented in Table 5-3.

41 32 Table 5-3: Key employment informant Informant Name Employment sector Note: Italicized sectors are assumed not to require built space. Description Set: - Industrial - Warehouse and logistics - Retail - Office - Education - Service - Primary - Work at home - No fixed place of work PLUM s Suitability for Sketch Planning Every model, by definition, embodies some combination of abstraction, simplification and aggregation. These design decisions represent limitations of which model users should be aware, but these may also be seen as features which make a particular model appropriate for certain types of analysis. This sentiment is elegantly captured in the widely-quoted statement, All models are wrong; some models are useful. (Box, 1979). This section outlines the main features of PLUM which may be viewed as appropriate for regional long-term sketch planning Treatment of markets PLUM does not include a formal representation of markets and prices in the land development process, thereby side-stepping significant model complexity. Rather, it employs a commandand-control land use plan to allocate development in space and time. This approach may be justified by the view that a regional planning authority (ostensibly) shapes urban form via official plans, policies, zoning by-laws, secondary plans, etc., and that it ultimately grants or denies approval to individual development proposals. The major caveat here is that a land use plan and projected urban state from PLUM may not be supported by actual market conditions (e.g., consumer preferences, developer incentives) and hence may be infeasible. In practice, the land use plans provided to PLUM are judgement-based and are implicitly informed by expert knowledge of a regional economy and market conditions.

42 33 Another perspective on the market-agnostic, physical-accounting orientation of PLUM versus market-driven land development models is a complimentary one. PLUM offers a means to quickly explore alternate physical trajectories of an urban system, unconstrained by econometric behavioural models. These alternate paths can be screened with respect to physical impacts (e.g., land consumption, transportation energy and emissions) and serve as references for subsequent behavioural analysis concerned with incentivizing towards or away from paths identified as more or less physically desirable Treatment of time PLUM is a dynamic framework; in the case of Winnipeg PLUM it operates at a single-year time step. This temporal resolution adds more data richness and complexity compared to a static equilibrium modelling approach. However, the benefit of a time-explicit approach may be considered to outweigh the data-management overhead cost, especially as it is handed by the platform s underlying stock-flow tools. Whereas Lowry-type models forecast a future state at some indeterminate point, without using base or earlier-than-forecast land use patterns (Horowitz, 2004), PLUM evolves the system starting from a known base state Treatment of uncertainty PLUM s core structure is deterministic. There are no probability distributions associated with assumptions and land use control variables. In principle this could be achieved through the creation of a stochastic layer, but would entail significant additional operational complexity. In practice, uncertainty is addressed through user judgement and scenarios, assisted by the scenario management capability of the platform Other notable abstractions PLUM involves other abstractions germane to a sketch planning approach, including: Vacant dwellings and employment space are not explicitly modelled. All built space is considered occupied at every point in time. Also, time lags between demand and supply response are not modelled the creation of new supply is instantaneous and coincident with demand. The rationale is that issues of shorter-term market dynamics and cycles are

43 34 not crucial for a model with a long-term strategic orientation (i.e., years), and may therefore be abstracted over. Dwelling types and employment sectors (Table 5-1 and Table 5-2 respectively) represent aggregations which span Winnipeg PLUM s demand and supply processes. The 5-group dwelling type categorization captures distinct types quite well, and also maps nicely to Statistics Canada s Census dwelling types. The 9-group employment sector categorization classifies both employment (in jobs) and employment space (in square feet of floorspace). This classification bridge between employment and built space is a convenient structural simplification for the model, but presents both conceptual and practical challenges 7. While PLUM s stock-flow-based allocation provides stickiness for dwellings and employment space in zones, at each time period population and employment are assigned to zones de novo. In practice this is ameliorated by projecting the relative zonal attractors estimated persons per dwellings, and estimated space per employee so that they do not vary rapidly Familiarity of model concepts to professionals A final point regarding PLUM s suitability for sketch planning and broader adoption speaks to the familiarity of the model s concepts to practising land use planners. PLUM was developed in consultation with regional land use planners (Bish and Hoffman, 1993; Martin, 2009) and as a result it embodies many concepts and procedures that are well understood by the planning profession (e.g., cohort-survival population models, land capacity analysis, development priorities and phasing). The combination of structural model transparency, data transparency and conceptual familiarity may be viewed as mitigating common black box resistance to model adoption. 7 An example of a conceptual challenge is accounting for a worker classified as industrial who actually works in an office position. A practical challenge is mapping to the employment sector classification from both the North American Industry Classification System (NAICS) used in the Census and also municipal building assessment codes.

44 Travel Model Description The lower half of Figure 5-1 represents a four-stage travel model which accepts population and employment time-series projections from PLUM, and also a user-specified evolving Multi-modal Network Plan. The following Section discusses considerations for the choice of transportation modelling platform, TransCAD. Section provides general information about the travel model. Sections describe the individual stages of TransPLUM s four-stage travel model, intertwining model structure and methods with a description of the base-year travel context within the Winnipeg area Travel Model Platform - TransCAD In the case of Winnipeg a pre-existing operational travel model was not available; therefore a new 4-stage model was developed for TransPLUM. Caliper Corporation s TransCAD transportation modelling package was selected as the implementation platform for two main reasons: 1. TransCAD is a widely used, modern transportation modelling package which provides the various 4-stage procedures in a customizable, scriptable environment. TransCAD also includes a native GIS interface for creation and editing of multi-modal networks. 2. A detailed base road network covering the study area is maintained by the Winnipeg Public Works department in the TransCAD environment and was made available to this project (see Section 4.2). The decision to implement the travel model on a separate platform, and not natively in the whatif? platform, has drawbacks. The first is a loss of transparency: calling a single compiled TransCAD script from the whatif? platform hides the internal logic of the 4-stage model (unless the user is willing and able to work directly with the TransCAD script). The second drawback is

45 36 a partial loss of scenario management as the tracking of variable instances and scenarios does not automatically extend from the whatif? platform into the TransCAD script 8. Variables are tracked if they are explicitly exported to and imported back from TransCAD but implementing this on every variable internal to the 4-stage model represents significant development and operational overhead. The option of implementing a 4-stage travel model directly in the whatif? platform was feasible, and in fact a simple whatif?-based travel model does exist 9. However, it was ultimately decided that for the pilot version of TransPLUM the benefits of using a mature and feature-rich thirdparty transportation modeling environment outweighed the costs of developing data interface logic and the partial loss of transparency and scenario management within the travel model. Future development on TransPLUM could include various degrees of cracking open the travel model within the whatif? platform. There is another perspective on the hard interface between the whatif? platform and TransCAD. In the pilot version of TransPLUM, the data bridge is quite narrow and comprises: population and employment zonal totals to TransCAD; and origin-destination trip flows and travel times back to the whatif? platform. These are standard 4-stage model inputs and outputs and so it is conceivable that another common transportation modelling package could be swapped in for TransCAD General Travel Model Information As stated in Section 5.3, the travel model operates at the same geographic zone system used by PLUM the 327-zone traffic zone system covering the Winnipeg Capital Region study area shown in Figure 4-2. The decision to have the land use and travel model use a common 8 TransCAD provides some diagrammatic and scenario management functionality; however, a cursory review of the product documentation suggests a less natural implementation than that of the whatif? platform. Furthermore, the proposition of implementing scenario management on two independent platforms for the same model seems cumbersome. 9 Bish and Hoffman (1993) describe the Waterloo Regional Planning Framework, developed in the whatif? Modelling Platform, which includes a transportation module. However, the readily-available transportation functionality is limited. For example, the only traffic assignment routine currently available is all-or-nothing, and there is little built-in network editing capability.

46 Number of trips 37 geographic dimension is a major convenience which precludes the need for tedious mapping procedures. The model represents passenger travel in the Capital Region during the 8:00-8:59 AM peak hour of a typical Fall weekday, consistent with the 2007 Winnipeg Area Travel Survey dates. Modelling only the AM peak hour was done for simplicity, but travel models for additional periods could be developed. The surveyed number of person-trips by time-of-day Winnipeg residents is shown in Figure 5-5, with the AM peak hour highlighted Time of day Figure 5-5: Total person-trips by time of day. 8-9AM peak hour is shaded in red. Source: 2007 Winnipeg Area Travel Survey. The other informant shared by the travel model and PLUM is the simulation time dimension (Table 5-1). This 50-year time horizon in one-year steps is the temporal frame in which Winnipeg PLUM operates; it also defines the sequence of travel model runs. To be clear, PLUM is a dynamic model in which the system state at a given year partially depends on the previous year. In contrast, the 4-stage travel model is a static-equilibrium model representing a particular

47 38 time period, in this case the AM peak hour. A travel model run for a given year is independent of runs for all other years. Strictly speaking, the simulation time dimension is not a property of the travel model; but it is convenient that the sequence of independent travel model runs is made temporally coincident with the land use model s output stream. Finally, it should be noted that there is a one-year gap between the 2007 travel survey and the 2006 Census the Winnipeg TransPLUM base year but the survey is treated as representative of the 2006 base Trip Generation The trip generation procedure calculates the number of trip ends (productions and attractions) by zone and trip purpose, for the AM peak. In TransPLUM this is achieved by multiplying: trip generation rates, where a rate is the number of trips generated per unit driver; and zonal driver variables, where the definition of a driver varies by trip purpose and whether it is for production or attraction. The driver variables are defined in Table 5-4. Table 5-4: Trip generation driver definitions. The unit of population is persons; the unit of employment is jobs. Trip Purpose Trip end type Production Attraction Home to work Population Employment Home to school Population Education Employment Home to other Population Population + Employment Non-home based Population + Employment Population + Employment Trip generation rates for the base year were calculated using: trip data from the 2007 Winnipeg Area Travel Survey; and zonal population and employment levels from the custom-tabulated 2006 Census data (see Section 4.2 for a description of the datasets). The mapping from the survey s trip purposes to modelled purposes in provided in Appendix A. The reader will recall that the survey represents trips made within, to and from the City of Winnipeg but not those made exclusively in the outer ring of the Capital Region thereby excluding a portion of the travel activity for outer-ring residents. Therefore, several considerations were made in the selection and tabulation of records for base year generation rates:

48 39 Only AM peak trips made by residents of the City were included in trip rate numerators. Only population and employment within City boundaries were used in trip rate denominators 10. This effectively applies the dominant city-based generation rates to the entire study area, but this was determined to be preferable to including only partial travel activity from outer-ring residents. The number of AM peak trips made by Winnipeg residents, broken down by trip purpose, is presented in Table 5-5. Based on a total population of 632,965 people and employment of 311,824 jobs (of which 24,835 are in the education sector), the estimated base-year trip rates are provided in Table 5-6 below. Table 5-5: Base-year AM peak trips made by Winnipeg residents. Trip Purpose Trip end type Number of Trips Percent of Trips Home to work 55, % Home to school 46, % Home to other 31, % Non-home based 17, % Total 151, % Table 5-6: Base-year AM peak-hour trip generation rates, in trips per driver unit. Drivers are defined in Table 5-4. Trip Purpose Trip end type Production Attraction Home to work Home to school Home to other Non-home based This likely under-estimates the absolute attraction rates where City-resident-only trip totals are divided by Citybased employment totals, and some portion of the City-based jobs are filled by non-city residents. However, productions and attractions are subsequently balanced with productions held fixed and so attraction rates are relative.

49 40 The initial trip generation procedure results in zonal production and attraction vectors whose totals do not match; this is followed by a balancing procedure in which the productions are held fixed and the attractions are uniformly scaled. For reasons of technical convenience, the first part of TransPLUM s trip generation procedure is implemented on the whatif? Modelling Platform. An unbalanced production-attraction table is then passed to the TransCAD travel model script which performs trip balancing. The whatif?- based trip generation rates are exogenous and may be varied over time Trip Distribution In this step the balanced production and attraction trip ends serve as the origin (row) and destination (column) totals for origin-destination (O-D) trip matrices one for each of the model s four trip purposes populated by a doubly-constrained gravity distribution procedure. Individual O-D matrix cells representing trip flows from one zone to another are commonly referred to as interchanges. For a given trip purpose, the number of trips predicted from origin zone i to destination zone j is given by: T AO B D f c ) ( 5.1 ) ij i i j j ( ij where O i and D j are origin and destination trip end totals respectively; f(c ij ) is an impedance function of travel cost, c ij ; and A i and B j are balancing factors solved through a standard iterative procedure described by Ortúzar and Willumsen (2001). TransPLUM uses auto zone-to-zone travel time in minutes as the measure of travel cost. The functional form selected for impedance is the inverse power function: f b ( ) ( 5.2 ) c ij c ij where b is a parameter whose value is found to produce the closest match between predicted trip length distribution and the observed trip length distribution from observed base-year O-D matrices. The inverse power function was chosen due to its simple functional form but also due

50 41 to its performance, based on visual inspection of observed and predicted trip length distribution charts, relative to other impedance functions such as the exponential. Table 5-7: Calibrated inverse function gravity parameters by trip purpose Trip Purpose Calibrated parameter value, b Home to work 1.32 Home to school 2.56 Home to other 2.09 Non-home based 1.95 TransCAD includes standard procedures for applying gravity distributions, and also for calibrating their parameters. Table 5-7 presents the calibrated inverse function parameter values for Winnipeg TransPLUM; Figure 5-6 shows the observed 11 and predicted trip length distributions for the Home-to-Work trips. Validation was performed on distribution procedure by defining 17 superzones and tabulating the interchange trip flows from the observed survey data and from the predicted gravity distribution, for each trip purpose. The observed and predicted interchange flows were used in a linear regression, yielding goodness-of-fit R 2 values of 0.64, 0.69, 0.77 and 0.67 for home to work, home to school, home to other and non-home based trips respectively. Scatterplots of the predicted vs. observed super-zone interchange flows are provided in Appendix B. It should also be noted that trip distribution is the first step in the so-called outer loop of the 4- stage model in which the Trip Distribution Mode Split Trip Assignment sequence is iterated, updating link travel times until convergence. The inner loop refers to a standard iterative procedure used in trip assignment, described in Section Neither loop is shown in the TransPLUM system diagram Figure 5-1 for simplicity. 11 These distributions are based on modelled base-year zone-to-zone travel times (as trip durations are not recorded in the travel survey). Travel times are modelled by assigning base-year O-D matrices from the survey to base-year networks.

51 Frequency Frequency 42 Observed Trip Length Distribution Auto Travel Time (min) (a) Predicted Trip Length Distribution Auto Travel Time (min) (b) Figure 5-6: Observed and predicted trip length distributions for AM peak home-to-work trips.

52 Mode Split The mode split step takes the four O-D trip matrices one for each trip purpose from the preceding distribution step and splits each into three separate matrices for the modelled modes of travel: auto, transit and walk-bike. In total twelve O-D matrices are created: four trip purposes by three modes. Home to Work w alkbike, 7% transit, 9% other, 0% Home to Other w alkbike, 6% transit, 4% other, 1% auto, 84% auto, 89% Home to School Non Home Based w alkbike, 9% transit, 1% w alkbike, 31% other, 0% auto, 44% transit, 23% other, 2% auto, 90% Figure 5-7: Observed AM peak-hour mode shares by trip purpose. Source: 2007 Winnipeg Area Travel Survey.

53 Mode Share 44 Figure 5-7 shows the AM-peak mode shares by trip purpose from the 2007 travel survey 12. With the exception of home-to-school trips, auto is the dominant mode with 84-90% share auto bike other transit walk Trip Distance (km) Figure 5-8: Mode share vs. trip distance for AM peak hour home-to-work trips. Source: 2007 Winnipeg Area Travel Survey. In formulating a mode-split model it can be informative to plot mode share against trip distance from survey data 13, as shown in Figure 5-8 for home-to-work trips. The plot reveals that the walk mode is sizable for trips less than 2 km. Auto share rises to around 90% approaching the 6 km trip distance. Transit share appears to peak around 15-20%, starting at 2 km, and gradually declines with increasing distance. The bike mode is a trace element, rarely having more than a few percent share at any distance. 12 The rules used to classify the survey s individual trip records as one of the three modelled modes are provided in Appendix C. 13 Survey trip records include a point-to-point straight-line distance field.

54 45 A standard random utility multinomial logit model is used to perform the mode split in TransPLUM. This model assumes that a trip maker t selects the available mode i which offers the greatest utility. Utility, U it is defined as U ( 5.3 ) it V it it where V it is the systematic or observable utility and ε it is random utility, an error term. Using the assumption that the error terms for all trip makers are identically and independently distributed with the Type I Extreme Value distribution, then the probability of a trip maker t selecting a mode i is given by P it j e V it e V jt ( 5.4 ) In TransPLUM the mode choice models are estimated using micro trip record data but applied at an aggregate zone-to-zone, or trip interchange level. Excellent treatments of discrete choice modelling are provided by Ben-Akiva and Lerman (1985) and Train (2009). The set of equations ( 5.5 ) represents the systematic utilities of the three modes, for Winnipeg TransPLUM s home-to-work mode split model. V V V auto transit walkbike TT TT auto transit isdistgt 6km distifgt 6km walkandwaittimepertripdist transit walkable walkdist bikeable bikedist ( 5.5 ) Estimated parameter values are included in the equations, all of which are significant at the 95% confidence level. The detailed estimation results are provided in Appendix D. Key points regarding this model are: A generic total travel time variable TT in minutes is included in the auto and transit modes with a negative parameter, suggesting that the greater a mode s travel time for a given trip interchange, the less attractive the mode becomes. In practice travel time variables are almost universally included in mode choice models and their parameter signs are expected to be negative to be considered valid.

55 46 The utility equation for the combined walk-bike mode is based entirely on trip distance. The dummy variable walkable takes a value of one if a trip has a straight-line distance of less then 2 km; otherwise it is zero. The variable walkdist is the straight-line trip distance in kilometres if walkable is one; otherwise it is zero. Along with its positive parameter, walkable acts as an alternative specific constant for the walk component of the walk-bike mode. The negative walkdist parameter decreases the attractiveness of walking with increasing distance, in much the same manner as a negative travel time parameter does with increasing time. For the bike component of the walk-bike mode the dummy bikeable and distance bikedist variables act in the same way as their walk-mode counterparts. However, bikeable takes a value of one for trip distances greater than or equal to 2 km and less than 10 km. The variable walkandwaittimepertripdistance in the transit utility equation is a measure of walking and waiting intensity, in minutes per kilometre. It reflects the unattractiveness of a short-distance transit trip with a relatively large walk-and-wait time component. By the same token a traveller would be more amenable to the same walkand-wait time if it were associated with a longer-distance trip. The auto utility equation contains a pair of distance-based variables isdistgt6km and distifgt6km similar to those in the walk-bike equation but with a greater-than 6 km threshold. Interestingly, the distifgt6km parameter has a positive sign, correlating increasing trip distance with greater auto attractiveness. One possible behavioural interpretation of such a correlation is that the further travellers venture away from home, the less comfortable they are relying on transit, as proposed by Marshall and Grady (2006) to explain positive distance parameters in a mode choice model developed for the Washington DC region. In Winnipeg TransPLUM the inclusion of this pair of distancebased variables in the auto utility was found to be a factor in the estimation of a negative travel time parameter (see the first point in this list regarding the importance of negative travel time parameters); without the distance-based variables, positive travel time parameters resulted. Furthermore, inclusion of other common mode-choice model variables (e.g., costs, origin and destination zone densities) resulted in positive travel time parameters. This experience appears consistent with earlier research in Winnipeg area

56 47 mode split models (Hurl, 1996) in which mode share showed low sensitivity to modal travel time. Project time constraints prevented the development of individual mode split modes for the other three trip purposes home to school, home to other, and non home based and so the home-towork model specification was reused and estimated using survey data for the other purposes. The resulting travel time parameters either had positive signs, or were statistically insignificant; for model application these parameters were set to zero. Estimation results for all the trip purposes are provided in Appendix D. The predicted base year mode shares are shown in Figure 5-9. Compared to the observed mode shares shown in Figure 5-7 for home-to-work trips, the dominant auto mode is over-predicted by about 3%. This variance is not surprising given the relatively coarse interchange variables available and the lumping together of auto-drive and auto-passenger. A further step not performed in this project would be a mode split model calibration step in which alternative specific constants are adjusted for closer matching of observed and predicted aggregate shares. Model performance for the other trip purposes is not very good; as such there is room for improved specification of these models. The availability of the transit and walk-bike modes are restricted to interchanges which meet certain maximum time and distance criteria. These criteria are provided in Appendix E. It is also worth noting that zone system definition is an extremely important factor in the accuracy of the projected mode shares. Standard transportation modelling practice involves grouping, or abstracting, all the activity points in a zone into a single point, or centroid. Centroids are then connected to various transportation networks via virtual links called centroid connectors. Large zones imply coarse spatial aggregation insofar as they group large areas of activity into single representations of network accessibility. Modelled walk times or distances along centroid connectors are especially sensitive to zone size: naturally these affect the walk mode, but also the walk-time component of transit, which in turn impact projected mode shares. As can be seen in Figure 4-2, zone sizes in the Winnipeg TransPLUM zone system generally increase moving outwards from the city centre. Incidentally, much of the Winnipeg Capital Region s growth is anticipated to occur in larger zones near the city boundary. One of the suggested improvements in Section 8.3 is to define smaller zones in these growth-prone areas.

57 48 Home to Work Home to Other walkbike, 7% t ransit, 6% walkbike, 4% t ransit, 2% aut o, 87% aut o, 94% Home to School Non Home Based walkbike, 4% t ransit, 2% walkbike, 26% aut o, 58% t ransit, 16% aut o, 94% Figure 5-9: Predicted AM peak-hour mode shares by trip purpose. A further cautionary note is offered with respect to the mode split model presented in this section, along with a vision for a looser coupling between the TransPLUM framework and mode split models. The mode split model presented here was developed using base-year survey data, and included variables from the limited TransPLUM outputs available. The resulting model is strongly distance-based; it embeds a rigid dependence on observed historical correlations between mode share and trip distance. The model is not without merit in the TransPLUM context: one would expect it to pick up some modal shifting associated with land use change, such as intensification, through changing trip length distributions. However, the model is not well equipped to reflect substantial mode share change which may result from transportation level-of-service changes, such as investment in a regional rapid transit network. Projecting mode shares and developing mode split models is as much an art as it is a science and alternate plans

58 49 may be served by different mode split models. As such, the mode split step in the context of the overall TransPLUM framework may be seen not as a hard-wired model with its associated parameters but rather as a placeholder for exogenous mode shares (by interchange, by trip purpose). In this approach mode shares could be generated by different formal models, such as the one presented in this section; or they could be judgment-based, much like PLUM s land use plan described in Section The key point is that the mode split step could move to being managed at the scenario level enabling easier swapping in and out of mode split assumptions or models rather than being hard-coded into the underlying TransPLUM framework. The practical implementation of such an approach relates to issues of cracking open the travel model within the whatif? platform, discussed in Section In the current pilot implementation of Winnipeg TransPLUM the mode split model is executed by fixed logic within a TransCAD script Trip Assignment In the trip assignment stage the O-D matrices from the preceding mode split stage are consolidated across trip purposes to give total O-D trip demand matrices by mode. The modal demands are loaded onto their respective networks; they traverse actual routes and ultimately yield flow rates on individual network links. Trip assignment is performed to predict usage on specific network segments and in the case of networks modelled with capacity constraints, to predict the impact of travel demand on network performance. In Winnipeg TransPLUM the auto road network is capacitated, the transit network is un-capacitated and the walk-bike trips are not assigned (they are assumed to follow the road network but not suffer congestion effects). The reader will recall from Section that the four-stage model represents a static-equilibrium state and so the projected flow rates are indicative of the entire trip assignment period in this case the AM peak hour Auto assignment Prior to auto trip assignment, the consolidated auto O-D trip matrix, in person-trips, is converted to vehicle-trips with a single factor calculated from the travel survey: vehicle-trips per person-trip. The matrix is assigned to a capacity constrained road network assuming deterministic user equilibrium conditions (Wardrop, 1953), an industry-standard procedure supported by all major transportation modelling software packages including TransCAD (Caliper Corporation, 2008). User equilibrium is the network condition in which all routes connecting an

59 50 O-D pair offer the same travel time, and a user is not able to select one route over another to gain travel time savings. The assignment requires specification of a volume delay function which relates individual link travel times to the vehicular traffic volumes serviced by those links. Winnipeg TransPLUM uses the common Bureau of Public Roads volume delay function with the recommended default parameters. The base-year road network used is that described in Section 4.2, the 2008 network provided by Winnipeg s Public Works Department. Although the model network represents a 2008 road configuration it was selected as the best available representation of the 2006 base-year network as modifications were minimal during the intervening period. It contains highway and arterial links, but also local roads. It has approximately 35,000 links and 11,000 nodes more detail than ideal for a regional sketch model. There are several reasons why a detailed network representation is not desirable in this context: The greater a network s detail, the more effort required for its creation. Defining future networks down to the local-road level presents a significant obstacle to generating multiple alternative future network plans. Detailed plans of subdivision and local street layouts are generally not known for longterm future development. Trip assignment through centroid connectors does not necessarily benefit from the use of local road networks. For these reasons it would have been preferable to define a more aggregate representation of the base-year road network and strip out local roads, but project time constraints precluded such an exercise. A further complicating factor was the use of the detailed road network as the basis for transit route definitions some of which occur on local roads and is discussed in the following section. As a result, network definition detail is listed as an area for improvement in Section 8.3. Figure 5-10 shows the base year auto flow map resulting from trip assignment, including volume-to-capacity link colouring.

60 51 Figure 5-10: Base-year scaled-symbol auto flow map Trip assignment is sometimes referred to as the inner loop of the four-stage model due to a standard iterative procedure used to solve user equilibrium trip assignment. In TransPLUM, auto trip assignment is also the final step of the outer loop introduced in Section In the outer loop the auto assignment results are used to recalculate zone-to-zone travel times (an impedance matrix) which is fed back into the trip distribution procedure, followed by mode split and trip assignment. TransCAD s implementation of the Method of Successive Averages is used here as opposed to direct feedback in which assignment results are averaged with those of previous outer loop iterations and fed back to trip distribution until convergence (Caliper Corporation, 2008) Transit assignment TransPLUM employs a non-capacity constrained transit network a common approach for transit assignment. This is justified by the assumption that public transit systems offer large

61 52 passenger capacities, and can be scaled up to meet demand as required (e.g., by adding more vehicles to a route). As Winnipeg s existing transit system is bus oriented, the base-year network is comprised of bus routes defined over the underlying road network used for auto assignment. The operational route definitions from Winnipeg Transit are available at the stop-by-stop level; however, this is seen as too detailed for a sketch model for reasons similar to those discussed regarding auto network detail in Section Therefore, transit route points coinciding with nodes in the road network are used to define stops, or access nodes in the transit route system (TransCAD terminology). The actual transit assignment procedure used is the TransCAD-specific Pathfinder method, similar to the assignment procedures found in other transportation modelling packages such as EMME/2 and TRANPLAN. The key features of the Pathfinder method in the context of TransPLUM are: it consolidates overlapping routes into trunks to reflect concentrated service corridors; and it selects multiple transit paths between O-D pairs, and allocates trips to alternate paths based on their levels-of-service (Caliper Corporation, 2008). With few exceptions, Winnipeg transit base-year bus routes run in mixed traffic and are therefore susceptible to delays due to auto congestion. In practice, modelling transit vehicle delay due to auto congestion is a relatively advanced four-stage model feature; and there are other factors at play: route schedules, vehicle dwell times due to boardings and alightings, etc. Based on anecdotal knowledge and a cursory analysis of average route speed statistics from Winnipeg Transit, the transit network link travel times were set to twice those of the auto link free-flow speeds. A final note on the transit assignment step in TransPLUM is that it is optional. In a non-capacity constrained network, determining O-D impedance matrices (travel time, in the case of TransPLUM) can be independent from assigning trip volumes to routes and links. Therefore, unless ridership projections by route are specifically required, only transit impedances need be

62 53 calculated for the mode split step. Furthermore, if transit assignment is required it can be performed outside the outer loop, as transit impedances are fixed Travel Model Outputs Returned to whatif? Platform Following the completion of every TransCAD travel model run for a given year, O-D trip flow and travel matrices (by mode, by trip purpose) are passed back to the whatif? platform for scenario management and analysis alongside the corresponding PLUM data Travel Model s Suitability for Sketch Planning While there are several valid criticisms of the four-stage model it remains the dominant travel modelling approach, well understood by transportation planning professions. There is a body of evolving methods designed to address the shortcomings of four-stage travel models (e.g., activity-based models, microsimulation) but it was decided that the complexity and data requirements of these approaches would be excessive for the sketch orientation of this project. Furthermore, the design of a four-stage model is sufficiently flexible to accept relatively aggregate population and employment distributions, as is the case with TransPLUM. The PLUM outputs and travel model inputs are aligned mapping and disaggregation procedures are not required for the data transfer. 5.5 TransPLUM run-time performance Comprehensive, rigorous performance testing was not carried out on Winnipeg TransPLUM. However, a full run of the connected PLUM and TransCAD travel model for the baseline scenario (described in Section 6) took approximately 1 hour and 40 minutes on the reference system 15. The PLUM portion of the run time is very small less than 5%. A significant portion (~25%) is spent on dis- and re-assembly of large multi-dimensional data arrays across the 14 Should the transit network link speeds be made dependent on the auto network link speeds, this would no longer be the case and transit assignment would have to occur within the outer loop. 15 The test system was a mid-range 2006-era laptop PC with: a dual-core Intel T GHz, 1GB RAM, and Windows XP. The whatif?-based PLUM ran on a virtualized (VMware) Linux server. TransCAD 5.0 ran natively on Windows.

63 54 whatif-transcad interface via flat files. There is significant potential for run time reduction in re-engineering the data interface, but also in the use of more modern and powerful hardware. However, even though a 1 hour and 40 minute run time does not represent real time analysis, it does offer an advantage over more complex integrated urban models whose run times are often measured in days. In this regard TransPLUM s performance is consistent with goal of a sketchtype model capable of rapid scenario analysis and turnaround.

64 55 6 Baseline Scenario Chapter 6 Baseline Scenario The previous chapter describes: development of Winnipeg TransPLUM s structure and its constituent sub-models; preparation of historical demographic time-series data, and geographically distributed base-year stocks for the PLUM component; and calibration of the travel model using base-year survey data. With these tasks completed, TransPLUM is able to accept future assumptions and policy controls in order to produce scenarios projections of future urban states. This section describes the creation of a first baseline TransPLUM scenario. Before proceeding there are two important caveats to be stated: 1. The baseline scenario presented here does not represent an official forecast from the City of Winnipeg. It has not been reviewed or vetted by City staff; rather, it is a preliminary synthesis of assumptions and interpretations of several consultant reports. It is not intended to serve as a basis for policy and planning decisions without further collaborative review. 2. Due to project time constraints, an evolving multi-modal network plan was not prepared for the baseline scenario. Instead, the fixed base-year networks were used for the entire simulation time horizon. Thus, while the baseline scenario projects population growth, economic growth and land use change, transportation infrastructure is not expanded. This was a project resource limitation not a model limitation. TransPLUM does include the logical structure to accept evolving multi-modal network, in one-year steps. In spite of these caveats and limitations, constructing the baseline scenario is an important step in model shake down and testing. Also, as the name implies, it provides a baseline or reference from which to construct and compare new scenarios. While it is common to attribute labels or themes to scenarios (e.g., business as usual, smart growth ) it is difficult to assign such a label to this baseline scenario. It incorporates projections

65 56 and assumptions from several recent consultant reports prepared for the City of Winnipeg. These reports include: Long-Term Demographic and Economic Forecast for Winnipeg s Census Metropolitan Area (Conference Board of Canada, 2007) City of Winnipeg Residential Land and Infill Strategy Draft (Office for Urbanism, 2009) City of Winnipeg Comprehensive Employment Lands Strategy (Altus Clayton, 2008) City of Winnipeg Commercial Land Strategy (Altus Group Economic Consulting, 2009) Downtown Winnipeg Employment Study (Altus Clayton, 2009) The forecast time horizons used by these reports generally extend 25 years, using the 2006 Census year as a base and projecting out to Therefore many of the results which follow also use this timeframe. The reader will recall, however, that TransPLUM s simulation time horizon extends 50 years, ending at Population, Dwellings and Employment In constructing the baseline scenario, PLUM s net immigration was approximately matched to that of the Conference Board s population forecast, resulting in the total population projection shown in the graph in Figure 6-1 (a). Note that the baseline projection is slightly greater than the Conference Board s between 2-4% larger over the period shown. This is expected as the Conference Board s projection covers the Winnipeg Census Metropolitan Area (CMA), whereas TransPLUM covers the larger Winnipeg Capital Region, which had 3.5% more population than the CMA in the 2006 base year. Other population variables are held constant at their base-year values The projections for these other population variables (fertility- and mortality- related) could certainly be adjusted to reflect historical trend analysis. However, holding these variables fixed is not an unreasonable approximation given their slow rate of change and the relative insensitivity of the total population to them, versus projected immigration levels.

66 dwelling units jobs persons households 57 Figure 6-1 (b) compares household projections, which appear consistent with the difference observed from the population comparison. Figure 6-1 (c) and (e) compare housing starts and new jobs respectively. These two projections are of particular interest because they are drivers of urban land development in TransPLUM. Population Households 1,200,000 1,000, , , , , , , , , , , ,000 0 Conference Board CMA (a) TransPLUM baseline 100,000 50, Conference Board CMA (b) TransPLUM baseline Housing Starts New Jobs 6,000 6,000 5,000 5,000 4,000 4,000 3,000 3,000 2,000 2,000 1, Conference Board CMA (c) TransPLUM baseline , Conference Board City Wpg (d) TransPLUM baseline 2029 Figure 6-1: Comparison of Winnipeg TransPLUM baseline scenario to the Conference Board s demographic and economic forecasts. Over the full forecast period the Conference Board s total projection of housing starts is approximately 14% greater than the TransPLUM baseline. Two likely sources of difference are:

67 58 The baseline scenario assumes no dwelling unit removals (demolitions) and therefore does not create new replacement housing stock, which may be present in the Conference Board s projection. TransPLUM does not model vacant dwellings (see Section ) it makes the dwelling stock exactly commensurate with regional households and the Conference Board s projection likely accounts for vacant units. This also seems to explain the baseline projection s drastic dip in 2007, where it appears that the household level is catching up to the built dwelling stock. These issues deserve further investigation. However, TransPLUM s projection of housing starts is sufficiently close to a third-party forecast to be considered adequate for the baseline scenario. The difference in projections for new jobs is similar to that of housing starts, but more pronounced. Not only is the Conference Board s total projection almost 17% larger than the TransPLUM baseline, but the Board s projection covers just the City of Winnipeg, rather than the CMA. As was the case with dwellings, the baseline scenario assumes no regional job losses and so replacement jobs are not added into the flow of new jobs. Further investigation into these differences is sure to improve upon TransPLUM s baseline scenario but may also call into question some of the assumptions used by third-party forecasts, and highlight the need to perform more sensitivity and scenario analysis. The baseline scenario includes projections of the shares of new dwellings and employment space by type, from consultant reports listed above. The portion of these demands directed to redevelopment (verses greenfield) was determined through a judgment-based iterative process in which deficits are largely balanced, out until The resulting portions are in the 25-50% range, depending on dwelling/employment type. 6.2 Land Use Plan and Allocation The main land use plan control variables capacity and priority, described in Section are specified for the baseline scenario, guided by the land strategy documents listed in the

68 59 introduction to this section. In addition, GIS-based layers from a draft urban structure map 17 are used to overlay development areas with individual TransPLUM zones. A stand-alone sequence of calculations was developed to prepare zonal capacities by type, outside the formal TransPLUM structure, as a separate whatif?-based model. Figure 6-2 is an example of one such calculation; the resulting capacity is specified in dwelling units by zone by dwelling type. 17 Part of the OurWinnipeg official plan update.

69 60 Table 6-1 summarizes all the land development types, factors and studies used in the preparation of the baseline capacities. Figure 6-2: Example stand-alone capacity calculation, shown for the major redevelopment component of residential reurbanization. pz is the geographic index PLUM zone; dt is the index for dwelling type.

70 61 Table 6-1: Summary of inputs to baseline capacities calculation. Greenfield Studies used: Residential Land and Infill Strategy OurWinnipeg Urban Structure (draft) Reurbanization Studies used: Residential Land and Infill Strategy OurWinnipeg Urban Structure (draft) Residential Employment Development Types: New communities Factors: Gross areas Land conversion factors Net area shares to dwelling types Net densities by dwelling type Studies used: Employment Lands Strategy (ELS) Commercial Land Strategy (CLS) Development Types: Unserviced large parcels, ELS Potential commercial inventory, CLS Factors: Gross areas Land conversion factors Lot coverage ratios Floorspace shares to employment sectors Development Types: Infill Major redevelopment Downtown Factors: Gross areas Land conversion factors Net area shares to dwelling types Net densities by dwelling type Studies used: Employment Lands Strategy (ELS) Commercial Land Strategy (CLS) Downtown Employment Study (DES) Development Types: Vacant/underutilized serviced parcels, ELS Existing commercial inventory, CLS Major office job space, DES Factors: Gross areas Land conversion factors Lot coverage ratios Floorspace shares to employment sectors Table 6-2 presents the total baseline scenario capacities for the study area. The reader will recall that the reurbanization capacities include the already-built base. Due to challenges in working with the building assessment floorspace data partial data, category mismatch issues with the Census industrial classification the employment floorspace base used is synthetic, calculated from base jobs and space per employee assumptions. Future efforts could be directed to reconciling assessment floorspace data with census employment data.

71 62 Table 6-2: Total baseline scenario capacities for the entire Winnipeg Capital Region. Residential (dwelling units) Employment (sq. ft. floorspace) Greenfield Reurbanization Single 73, ,836 Semi 2,178 16,201 Row 9,073 10,736 Apartment Low 18,537 73,058 Apartment High 13,066 39,062 Industrial 7,163,227 52,436,297 Warehouse / Logistics 13,350, ,530,835 Retail 5,797,581 63,692,030 Office 5,732,014 57,840,876 Education 432,900 17,585,750 Service 8,947,617 57,078,031 Development priorities for the baseline scenario are based on phasing assumptions gleaned and interpreted from the listed consultant reports, supplemented by informal interviews with City planning staff. Three distinct priority levels are specified for greenfield development, for both residential and employment. Two levels are used for employment reurbanization. All residential reurbanization is lumped together into a single priority level 18. Allocation results are presented in Figure 6-3 in the form of thematic density maps for selected horizon years: 2007, 2016 and This is the same assumption used by PLUM users at the Region of Waterloo, Ontario. They note the nature of re-urbanization, in practice, tends to be very spotty and sporadic [PLUM assumes] re-urbanization will happen everywhere in proportion to the identified potential. (Martin, 2009)

72 63 Persons per acre Jobs per acre Persons and Jobs per acre Figure 6-3: Thematic density maps of Winnipeg TransPLUM baseline scenario. All densities are calculated using gross zonal areas.

73 64 The baseline scenario projects deficits, shown in Figure 6-4, which represent insufficient planned capacity to meet expected demand. During the development of the baseline scenario demand and supply variables were adjusted manually, over several iterations to push the onset of deficits back further in time. On the demand side this involved shifting some development from reurbanization to greenfield; on the supply side, the planned densities of certain dwelling types were increased greenfield dwelling units deficit dwellunit / year reurbanization dwelling units deficit dwellunit / year X10 3 X time in years time in years (a) (b) Legend: Single 1, Semi 2, Row 3, Apartment Low Density 4, Apartment High Density greenfield non population related employment space deficit sqft / year 1.20 reurbanizaition non population related employment space deficit sqft / year X X time in years time in years (c) (d) Legend: Industrial 1, Warehouse/Logistics 2, Retail 3, Office 4, Education 5, Service 6 Figure 6-4: Projected capacity deficits for the baseline scenario.

74 share 65 With the exception of education-based employment space, deficits in the baseline scenario do not occur until the year 2028 towards the end of the planning horizon used by the various third-party studies which informed the baseline capacity assumptions. Growth capacity for education-related employment was not provided in the baseline scenario due to a lack of information available regarding expansion plans for educational facilities. This education-related deficit is left as an open issue to be resolved in further scenario development. 6.3 Travel This section presents key travel model results from the baseline scenario. Much of what follows is based on O-D trip flow matrices (post mode split) and travel time matrices. Figure 6-5 shows the baseline projected mode share for all trips. As discussed in Section 5.4.5, the mode split model over-predicts auto trips in the base year. However, focusing on the rate and direction of change, one observes only a small shift in shares over time approximately 2% increase in auto mode share, from 81% to 83% over 25 years, and matching total decreases in transit and walk-bike shares. The provisional conclusion is that the baseline land use projection, on its own, implies a gradual increase in auto share. Mode Share auto transit walkbike year Figure 6-5: Baseline mode share projection, AM peak hour.

75 66 Figure 6-6 shows total person travel time over time, by mode. Person travel time increases over time for all modes, but the auto mode shows the greatest absolute and proportional increase total travel time trippurp=total minute * person 1 Legend travelind/traveltimetot/34 scenario 36 1 auto 2 transit 3 walkbike X time in years Figure 6-6: Baseline total person travel time over time by mode, AM peak hour. Figure 6-7 shows baseline AM peak auto travel times from various zones to the Winnipeg central business district (CBD), represented by zone 201. Graph (a) displays the travel times for all 327 zones to zone 201. The general trend is a gradual increase over time, exemplified by graph (b), a typical zone. Graphs (c) and (d) are examples of zones showing marked increase in auto travel time during the simulation horizon. In both these cases the zones of origin are not serviced by major roads, yet are projected to experience significant growth thus the projected demand outstrips the existing road capacity. The reader will recall that the baseline scenario uses a static base-year road network. Therefore, while these sharply increasing travel times are intuitively consistent with the baseline assumptions, they should not be considered realistic projections.

76 travel times mode=auto, time=2007 minute 193 travel times mode=auto, time=2007 minute X X time in years time in years (a) All zones to 201 (b) Zone 5302 to travel times mode=auto, time=2007 minute 1 travel times mode=auto, time=2007 minute X X time in years time in years (c) Zone 2710 to 201 (d) Zone 5900 to 201 Figure 6-7: Baseline auto travel times from various zones to zone 201 (Winnipeg CBD), AM peak. Figure 6-8 shows accessibility plotted on the Winnipeg zone map for different modes, for three projection years. The accessibility measure for a given zone is the number of jobs accessible within a specified threshold time (30 minutes is used here). For all but a few zones, employment accessibility increases over time. As the baseline scenario does not include network improvements, the increasing zonal accessibilities are due to allocated employment growth.

77 68 Auto employment accessibility Transit employment accessibility Walk employment accessibility Figure 6-8: Thematic employment accessibility maps of Winnipeg TransPLUM baseline scenario. Accessibility is measured in number of jobs accessible within 30 minutes during the AM peak hour.

78 69 Chapter 7 Coordination Approaches 7 Coordination Approaches This section returns to the notion of land use and transportation coordination introduced in Section 3.2, represented graphically as the dotted lines labeled Planner Feedback in Figure 3-1 and Figure 5-1. Section 7.1 defines the term feedback in the context of the TransPLUM framework. Section 7.2 describes the development of a land utilization indicator intended to assist TransPLUM users with coordination. 7.1 Feedback Paradigms It is worth making a distinction between the term feedback as often used in dynamic systems modelling, and feedback used in the context of TransPLUM s planner feedback. Generically, feedback describes a situation in which some aspect of a system s state is observed, and that observation is subsequently used in the control of a process which ultimately feeds back to impact said system s state. Used in the dynamic systems modelling field, feedback usually refers to model structure which formalizes and endogenizes a feedback process using algorithms, mathematical equations and parameters. This is done to represent some aspect of system s behaviour be it physical, economic or social. In fact, this type of endogenous feedback is present throughout TransPLUM and a good example is that of the regional population cohort-survival model. The absolute number of births which the model projects for the time period t is calculated based on the regional population of women of child-bearing age from the previous time period, t-1. Starting at some future time period (e.g., t+15) the population of women of child-bearing age will have been influenced by the births at period t, thus completing the population births population feedback loop. This feedback structure is baked into TransPLUM s population model and its purpose can be characterized as one of prediction, at least within the context of a given scenario. In contrast, planner feedback, while it adheres to the generic definition of feedback, does not prescribe formal mathematical statements representing controller behaviour, although it does not

79 70 preclude such formality. Rather, the dotted-line planner feedback represented in Figure 3-1 and Figure 5-1 represents the discretionary capability of the model user to adjust a reference land use - transportation plan combination in response to their expected outcome. This feedback operates at a layer above the core TransPLUM framework; its purpose can be characterized as one of iterative expectation, control and learning 19. Possible planner feedback responses are: Adding network capacity to a reference plan in order to mitigate projected delays on specific links. This is a concept long-familiar to transportation planners in the context of four-stage models and network design. Removing planned network capacity increases of a particular mode, to areas well serviced by other modes. This too is a concept familiar to transportation planners in activities such as transit route rationalization. Increasing planned densities of specific areas to take advantage of planned transportation infrastructure and high levels of service. Decreasing planned densities of specific areas, anticipating of poor levels of transportation service. The default planner feedback mechanism is judgment and trial-and-error based. In practice, setting and adjusting the rich multi-dimensional land use controls manually, cell-by-cell, is timeconsuming and therefore helper scripts (called views in the whatif? platform) may be created to partially or fully automate feedback operations. Broad brush adjustments can be made and scenarios created, through views, and subsequently refined manually if required. They key point here is that planner feedback mechanisms are flexible, interchangeable, and no single feedback method is prescribed by TransPLUM. The following Section 7.2 proposes a particular feedback helper a land use utilization indicator. Due to time constraints, this project did not explore automated methods of adjusting the properties and topologies of evolving multi-modal networks. 19 In the control theory literature this is sometimes referred to as a second-order cybernetic system.

80 Land Utilization the Density-Accessibility Ratio This section proposes an indicator to relate the outcomes of land use and transportation plans, and to serve in their coordination Concepts The indicator is premised on the following line of thinking. If a zone is endowed with a given level of accessibility, is there an appropriate corresponding density level (or range of density levels) for that zone? If there is, let it be referred to as the normative zonal density. Then, if the zone s actual density is greater than its normative density it may be considered over-utilized. The converse also applies if the zone s density is less than its normative density it may be considered under-utilized. This indicator sets the stage for coordination of land use and transportation plans via a planner feedback scheme, shown in Figure 7-1. If zone is over-utilized Consider Decreasing planned zonal density and/or Increasing transportation service to/from the zone If zone is under-utilized Consider Increasing planned and/or Decreasing transportation service zonal density to/from the zone Figure 7-1: Planner feedback scheme based on zonal utilization. This conceptual foundation raises several practical, inter-related questions: 1. What should the measures of zonal density and accessibility be? 2. How are normative densities determined? What is the functional form that produces normative density, given accessibility? 3. How should zonal densities and accessibilities be related to indicate the degree of over/under- utilization. What is the functional form?

81 72 4. How exactly are policy controls (land use plans, network plans) adjusted in response to over/under utilization? How is the nature and magnitude of a density and/or network adjustment determined? This project does not engage in a rigorous exploration of these questions, which poses a significant research effort in its own right. However, questions 1, 2 and 3 are provisionally addressed in the remainder of this section. Question 4 is not addressed further beyond the simple scheme laid out in Figure 7-1 except to say that the starting point for policy control feedback is purely judgment based. Some degree of planner feedback automation is plausible, perhaps even to the extent that an iterative feedback view could be run to equilibration. Returning to question 2 above, it would seem that the specification of an absolute normative density as a function of accessibility should be backed by empirical multi-regional comparative research, but also normative models of urban structure. The analysis would be subjective and the results would almost inevitably be contentious. Therefore, for the purpose of this project, a relative normative density is used, in which relative applies to zones within the study area, the Winnipeg Capital Region. How relative normative densities are defined will soon become clear. A provisional answer to questions 1 and 3 (units of measure, utilization function) is as follows. For a zone i, let density i population i employment grossarea i i ( 7.1 ) accessibility i = number of accessible jobs from zone i within t minutes ( 7.2 ) and let utilizatio nratio i density i / densityben chmark accessiblity / accessbilitybenchmark i ( 7.3 ) The equations above are considered provisional for several reasons. First, zonal density ( 7.1 ) is defined as the sum of population and jobs divided by gross zonal area, a crude density measure used in other growth management settings (Ministry of Public Infrastructure Renewal, 2006). Population and jobs are weighted equally, but perhaps a non-equal weighting might be better

82 73 suited to this purpose. ( 7.2 ) offers a simple employment-accessibility measure, the same described in Section 6.3 and used in Figure 6-8. It could be made more specific (e.g., accessibility to school enrollment) or more general (e.g., to include residential activity). There are other more sophisticated accessibility indices which weight zonal activities using continuous impedance functions, as opposed to the hard all or nothing time threshold t; perhaps these are worth experimentation in this context. Finally, accessibility can be defined over one or multiple modes. The proposed utilization indicator ( 7.3 ) is a ratio of scaled zonal density to scaled zonal accessibility. Scaling is accomplished through density and accessibility benchmark constants whose values are arbitrary, but for this project are chosen so that the median utilization value equals 1 in the base year. The benchmark values imply the normative zonal densities, and the benchmarks are set with respect to the Winnipeg base-year zonal utilizations hence the relative nature of the normative densities described above. Ultimately, the open questions discussed above regarding the formulation of the utilization indicator can only be addressed through experimentation and review with planning professionals and experts, in order to best align the utilization outputs with professional judgment. After all, the indicator is intended to be a professional judgment aid and so that is the standard against which it should be calibrated Provisional Results This section describes a first attempt at applying the utilization indicator proposed in the preceding Section to Winnipeg TransPLUM s 2006 base year, using AM peak accessibilities. The accessibility metric used is that of ( 7.2 ), but transit-based, and the value of the threshold time t is set at 30 minutes. The density and accessibility benchmarks are set at 10 persons and jobs per acre, and 16,330 transit-accessible jobs within 30 minutes, respectively. These benchmark settings result in the median zonal utilization indicator having a value of 1. Thus a zone with utilization value above 1 may be considered over-utilized with respect to transit accessibility; and vice-versa, below 1 may be considered under-utilized.

83 74 The distribution of zonal utilizations is skewed. Naturally, half of the zones have utilization values less than 1, but the mean value is 2.63 and the maximum is Zones without transit accessibility 20 are excluded. Figure 7-2: Thematic map of utilization indicator from Winnipeg TransPLUM 2006 base year. AM peak hour accessibilities used. The utilization results are mapped in Figure 7-2. Overall, the emergent pattern can be described as over-utilized in the downtown area, under-utilized in the mature inner ring and over-utilized near the City boundaries. Examples of specific zones, their densities, accessibilities and utilization values are provided as follows. 20 According to the modelled restrictions on the transit mode presented in Appendix E.

84 75 Zone 472 den: acc: 148,815 util: 1.01 Zone 3515 den: acc: 41,724 util: 1.03 Figure 7-3: Example of two zones with median utilization values. Figure 7-3 shows two zones, both with utilization values close to one (the median utilization). One zone is within the downtown area and contains primarily commercial-use buildings (zone 472); the other (zone 3515) is approximately 7 km outside the CBD and contains residential and employment land uses. This comparison demonstrates that, according to this utilization metric, zones with markedly different densities, land use types and locations can produce similar utilization values due to varying zonal accessibilities. However, the fact that both these two zones correspond to middle of the pack utilization levels does not provide any intuitive interpretation of the metric.

85 76 Zone 462 den: acc: 125,685 util: 0.28 Figure 7-4: Example of downtown zone with low utilization value. The zone highlighted in Figure 7-4 does provide some intuitive confirmation of the metric. Here, zone 462 is located within the downtown area and therefore has accessibility to a large number of jobs via transit. It contains the Manitoba Legislative Building, surrounded by sprawling grounds, and therefore shows a relatively low density. The result is a utilization indicator value of 0.28 suggesting relative under-utilization. Of course, it is the role of the planner to interpret such results in the context of existing zonespecific uses. This example is not indented to suggest that the site of an important civic building should be redeveloped to contain high-density office towers!

86 77 Zone 4802 den: acc: 1,310 util: Figure 7-5: Example of a low-density suburban zone near City boundary. A final example of the utilization indicator is shown in Figure 7-5. This zone, near the edge of the City, contains mainly low-density residential development. In the context of poor transitbased accessibility to jobs it yields a utilization value of 16.72, suggesting relative overutilization.

87 78 X density-accessiblity ratio mode=transit, LUAct=popAndEmp time in years Legend travelind/denaccessrat/34 scenario Figure 7-6: Zonal utilization indicator values for the baseline scenario, projected over time. Up to this point the utilization indicator has been presented as a static concept, applicable to snapshots of urban form. However, within the context of a dynamic integrated urban model such as TransPLUM, the indicator can be applied to an evolving time-series projection of a City, as shown in Figure 7-6. This adds another dimension to the indicator, extending its interpretation to include the direction, magnitude and rate of change of zonal utilizations under specific assumptions and policies. It would appear that the concept of a utilization indicator based on the ratio of zonal density to accessibility is fairly unique one, at least in the context of a planning support model such as TransPLUM. An example of the ratio is found in the literature (Heikkila and Peiser, 1992) but in this case the measured used was the inverse of utilization accessibility over density as a means to generate land rents.

88 79 Chapter 8 Conclusion 8 Conclusion 8.1 Summary of Contributions This project has resulted in the development of a sketch model, TransPLUM, to support coordinated land use and transportation planning at the regional scale a generally overlooked but important segment of urban models offered. The model was implemented using the Winnipeg Capital Regional as a pilot study area, and a baseline scenario was created. Two areas of innovation are notable. First is the general application of commercially available modelling software to design, configure and integrate a tool with a focus on rapid analysis, model transparency and scenario management. Second, more specifically, is a proposed utilization indicator a density-accessibility ratio which identifies the relative utilization of a zone and might serve as a coordinating mechanism. 8.2 Evaluation The tool was developed to enable coordination of regional land use - transportation plans, and to enable rapid scenario analysis. With respect to the coordination objective, the tool accepts independent land use and multimodal network plans, and uses a deterministic model structure to project outcomes. The responsibility for coordination is left in the hands of the user, to interpret projected land use and travel patterns, and to adjust the plans with a view towards increased efficiency, compatibility and desirability. Fundamentally the tool does not ensure coordination but rather provides an environment for assembling, managing and visualizing land use and transportation plans sideby-side, thereby extending the perception of planners and increasing the likelihood of coordinated plans. Compared to the disjoint manner in which many regional planning authorities operate, this tool represents a significant advancement, both technically and from an institutional integration perspective.

89 80 While it is premature to evaluate the effectiveness of the proposed utilization indicator, it looks to be a promising means of helping planners balance land use and transportation plans. With respect to the objective of enabling rapid scenario analysis, or sketch modelling, experience from this project is not sufficient to gauge the level of success. A baseline scenario was constructed for this project, and in doing so several intermediate scenarios were created incrementally and rapidly. However, it is the ability to create significantly different scenarios which is of greater interest. Alternate land use plans in TransPLUM can be specified in a quick broad brush manner through judicious groupings of zones, development types and their assigned policy controls. However, the ability to quickly sketch network plans is dependent on a sufficiently aggregate representation of base and future networks a criterion not satisfied in the pilot Winnipeg TransPLUM due to time constraints. The reality is that while TransPLUM offers much structure geared towards simplified, quick planning, it is not a silver bullet there is still significant effort required in preparing even strategic-level network inputs. 8.3 Future Work and Improvements This final section lists several areas for further research and development on TransPLUM. It is divided into work related to the generic TransPLUM structure, and that related to the specific Winnipeg TransPLUM implementation. It is also worth noting that many of the design decisions made in this project revolve around trade-offs between parsimonious structure versus disaggregation and comprehensiveness. Proposed model improvements tend to be biased toward increased complexity, as is the case with several items listed here. Nevertheless, the original sketch goals of the model should not be forgotten in the consideration of these items Generic Model Further research and development areas related to the generic TransPLUM model are: More extensive exploration and testing of the utilization indicator. Cross-regional comparative analysis could be particularly useful in determining standards for densityaccessibility benchmarks.

90 81 Building on a better-understood utilization indicator, automated feedback mechanisms to TransPLUM s land use plum could be developed, which would be balance-seeking. Tighter software integration between PLUM and the travel model (TransCAD) could be developed, with respect to: data transfer efficiency; and also travel model transparency (i.e., cracking open the travel model logic within the whatif? platform). Consideration of urban freight movement model structure. Building aspects of dynamics and inertia into the travel model such that trip distribution for a given time point is influenced by prior distributions (i.e., lasting impacts of established land use and travel patterns) Specific Winnipeg Implementation Further research and development areas related to the specific Winnipeg TransPLUM implementation are: A more comprehensive travel model validation exercise, and comparison to more detailed travel models. Mode split model calibration. Specification of a refined zone system in particular for larger zones near the City s boundary. A review of the model s data categories, and mappings from Census and municipal data sources. In particular, a review of employment-related floorspace categorization in the City s assessment database would be useful. Research into technical best-practices for specifying evolving multi-modal networks using TransCAD. Preloading observed and estimated truck flows onto the road network. Creation of several substantively-varying land use transportation scenarios for the Winnipeg Capital Region. In particular, assumptions and policies for the surrounding

91 82 rural municipalities should be developed in collaboration with the local governments. Also, growth of educational facilities should be researched and incorporated in the scenarios. Developing mode split models for all model trip purposes, other than home-to-work.

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96 87 Appendix A: Survey Trip Purpose to Model Trip Purpose Mapping Table A-1: Survey trip purpose to model purpose mapping where zone of trip origin is the home zone of the trip maker. ORIGIN_TZ == HOME_TZ HBW HBS HBO NHB [1] Work (usual) 1 [2] Shopping 1 [3] Work-Related (other than usual) 1 [4] School 1 [5] Drive Someone Somewhere 1 [6] Other 1 [7] Return Home 1 [8] Social/Recreation 1 [9] Work on the Road / itinerant workplace / no fixed address 1 [10] Restaurant (Eat In) 1 [11] Pick Someone up 1 [12] Medical/Dental 1 [13] Restaurant (Take-Out) 1 [14] Refused [15] Don't Know Table A-2: Survey trip purpose to model purpose mapping where zone of trip origin is note the home zone of the trip maker. ORIGIN_TZ!= HOME_TZ HBW HBS HBO NHB [1] Work (usual) 1 [2] Shopping 1 [3] Work-Related (other than usual) 1 [4] School 1 [5] Drive Someone Somewhere 1 [6] Other 1 [7] Return Home 1 [8] Social/Recreation 1 [9] Work on the Road / itinerant workplace / no fixed address 1 [10] Restaurant (Eat In) 1 [11] Pick Someone up 1 [12] Medical/Dental 1 [13] Restaurant (Take-Out) 1 [14] Refused [15] Don't Know

97 Drive Someone Somewhere Medical/Dental Other Pick Someone up Restaurant (Eat In) Restaurant (Take-Out) Return Home School Shopping Social/Recreation Work-Related (other than usual) Work (usual) Work on the Road / itinerant workplace / no fixed address NHB HB HB NHB Figure A-1: 3D barplot of trip frequency by survey trip purpose. AM peak hour trips only.

98 OD_Pred_SUP$HBO OD_Pred_SUP$NHB OD_Pred_SUP$HBW OD_Pred_SUP$HBS Appendix B: Trip Distribution Validation Scatterplots OD_Pred_SUP$HBW_obs OD_Pred_SUP$HBS_obs OD_Pred_SUP$HBO_obs OD_Pred_SUP$NHB_obs Figure B-2: Predicted vs. observed trip flows for super-zone (17 x 17) interchanges.

99 90 Appendix C: Trip Mode Classification Rules This appendix describes the rules used to classify individual trip records from the Winnipeg Area Travel Survey (WATS) into the TransPLUM s three modelled modes: auto, transit and walkbike. Table C-3 lists the WATS modes recorded and Table C-4 shows the mapping from WATS modes to modelled modes. Due to the fact that each trip record includes up to five mode fields, a simple mode precedence scheme is applied after the mapping: 1. If any of the five mode fields are of type transit then the trip is classified as transit, else 2. If any of the five mode fields are of type auto then the trip is classified as auto, else 3. If any of the five mode fields are of type walkbike then the trip is classified as walkbike, else 4. The trip is classified as other, which is ignored Table C-3: Modes recorded in 2007 Winnipeg Area Travel Survey 1 car driver 2 car passenger MODE 3 Winnipeg Transit bus 4 intercity bus 5 other transit 6 private transportation service 7 school bus 8 water taxi / ferry 9 Taxi 10 Handi-Transit 11 Bicycle 12 Walk 13 motorcycle/moped 14 other mode 15 don't know 16 Refused

100 91 Table C-4: Mapping from surveyed modes to modelled modes Modelled mode to auto transit walkbike car driver Winnipeg Transit bus bicycle Surveyed mode car passenger school bus walk from taxi other transit motorcycle/moped

101 92 Appendix D: Mode Choice Model Estimation Results Model1: Home-to-work Inputs Total Cases 2664 Cases with bad or missing choice value Cases with missing attribute values 12 Valid Cases 2647 Choice Distribution 5 transit : % auto : % walkbike : % Maximum likelihood reached at iteration 15 Parameter Estimate Std. Error T Test ASC_TRANSIT traveltime walkable walkdist bikeable bikedist isgt6km distifgt6km wwtperdist Log-likelihood at zero Log-likelihood at end (LL(zero) - LL(end)) Asymptotic rho squared Adjusted rho squared Model2: Home-to-school