LAND USE AND TRANSPORT SIMULATION: APPLYING URBANSIM IN THE GREATER ZURICH AREA

Transcription

1 LAND USE AND TRANSPORT SIMULATION: APPLYING URBANSIM IN THE GREATER ZURICH AREA Michaela BUERGLE Institute for Spatial and Landscape Planning (IRL) and Institute for Transport Planning and Systems (IVT) Swiss Federal Institute of Technology, Zurich ETH Hoenggerberg CH-8049 Zurich Switzerland Tel: Fax: Michael LOECHL Institute for Transport Planning and Systems (IVT) Swiss Federal Institute of Technology, Zurich ETH Hoenggerberg CH-8049 Zurich Switzerland Tel: Fax: Urs WALDNER Institute for Spatial and Landscape Planning (IRL) Swiss Federal Institute of Technology, Zurich ETH Hoenggerberg CH-8049 Zurich Switzerland Tel: Fax: Kay W. AXHAUSEN Institute for Transport Planning and Systems (IVT) Swiss Federal Institute of Technology, Zurich ETH Hoenggerberg CH-8049 Zurich Switzerland Tel: Fax: Abstract: With the collaboration of two ETH Institutes, a transport and land use simulation model is currently set up for the Greater Zurich area. The goal is to combine the land use simulation model UrbanSim with a common transport simulation. In its first part, the paper gives a general outline of the project and its contents. The second part then focuses on data generation and modelling efforts particularly concerning rent prices and the generation of 1

2 household information. A major household survey has recently been conducted and online data retrieved to collect real estate information which is used in a regression model. Moreover, household data has been synthesised using a simulated annealing algorithm. The procedure is described along with its first results. The paper concludes with an outlook on further work steps within the project. Keywords: Integrated land use and transport model, UrbanSim, Zurich, rent prices, household generation, simulated annealing 2

3 LAND USE AND TRANSPORT SIMULATION: APPLYING URBANSIM IN THE GREATER ZURICH AREA 1 INTRODUCTION The interactions between transport systems, changing accessibility and land use are vital for the dynamic changes of land use and consequently for spatial planning. Throughout the world land use transport simulations like ILUMASS (Moeckel R. et al., 2002), ILUTE (Miller E. 2005), and MUSSA (Martínez F. and Donoso P., 2001) have been developed (for an overview see Wegener M., 2004) to quantitatively reproduce, forecast and compare policy and investment outcomes. The project Infrastructure, Accessibility and Spatial Development" constitutes one facet of the research focus The Future of Urbanised Landscapes of the ETH Zurich s network City and Landscape, NSL (NSL, 2005). The goal of the project is to develop and apply an integrated land use and transport model for the Greater Zurich area. The resulting simulation is expected to allow for quantitative forecasting of land use and transport development and impact analysis thus creating a solid foundation for long term investment decisionmaking. The paper starts with a description of project goals, delimitation of the study area and a short description of its characteristics as well as topics to be covered by the research project in section 2. Section 3 gives a short outline of the simulation software used for the project, UrbanSim, and its model structure. Clearly, not all the data needed is necessarily available in Switzerland or elsewhere. Some pieces of information are available only for parts of the simulation area, other data exists but cannot be obtained, all of which leaves the researcher no choice but to invest in data collection and modelling efforts and to make some imputations. Examples of those tasks are given in sections 4 to 6. While section 4 describes a recently conducted household survey, section 5 presents first modelling results of rent prices for which an internet database has been used. In Section 6, efforts to generate households by using methods of simulated annealing are presented. The paper closes with an outlook on next steps and planned extensions of the simulation. 2 PROJECT OVERVIEW AND SIMULATION AREA The simulation represents processes of land use as well as information concerning transport systems at different points in time for the Greater Zurich area. It will integrate these different pieces of information to account for their interactions when making forecasts. A microscopic land use model contemplating single households and jobs in a grid of hectares is combined with a common mesoscopic transport model based on traffic zones. As land use simulation software, UrbanSim, a model developed at the University of Washington, USA (Waddell P., 2002), is applied. Efforts have to be put into the software in order to fully adapt it to Swiss conditions. For 3

4 validation purposes simulation runs emulating the research area s development during the years 1990 to 2000 are conducted. After the successful completion of the validation phase, forecasts will be made for roughly 15 years into the future. In the course of the development and adjustment of the simulation, particular attention is given to the consideration of transport. In the standard UrbanSim set-up, an external transport model has to provide travel times and utilities as input data which is considered for accessibility calculations and consequently in the location decisions of employers, residents and developers. Land use data outputs of UrbanSim can feed back into the travel model for the next model run. It is planned to introduce transport flows and volumes to the simulation system. Consequently, the aim is not only to take into account characteristics of supply resp. accessibility but also traffic flow and speed when modelling location choice. A further step to tighten the connection of land use and transport in the simulation system is the consideration of traffic noise impacts on land prices and location choice. There is no common and appropriate delimitation of the Greater Zurich area available. Consequently, the simulation area of Greater Zurich had to be defined. Commuter data were aggregated into groupings of municipalities called agglomerations. Agglomerations with an outward-commuting percentage towards the city of Zurich of at least 20% of all outward commuters are considered as the simulation area. These are the agglomerations Zurich, Winterthur and Wetzikon. To smooth the delineation of the simulation area, municipalities not belonging to but being (almost) enclosed by those agglomerations have been added. The defined simulation area is illustrated in Figure 1. It comprises 157 municipalities with almost 1.3 million inhabitants. Figure 1 Simulation area (light grey) and study area (dark grey) 4

5 Within the simulation area, a focus is placed on the Glattal (dark grey in Figure 1), which is a suburban area experiencing a very dynamic development not only because of the included airport of Zurich. As such, the area is the ideal setting for an integrated land use and transport simulation. It comprises the eight glow.-communities Bassersdorf, Dietlikon, Dübendorf, Kloten, Opfikon-Glattbrugg, Rümlang, Wallisellen and Wangen-Brüttisellen as well as Zurich s two northern city districts, 11 and 12. Major transport investment projects from that area will be selected and their impacts on land use and transport analysed later on within the project. 3 LAND USE SIMULATION SOFTWARE URBANSIM UrbanSim models the dynamic processes of household and firm location choices, real estate development, and real estate prices. It has been applied in several North American cities and regions. It leaves the user a fair amount of choices with regard to the modelled dimensions in space and time. In practice, the eligible unit is that for which sufficient data is available from regional and national sources. For the research project described here, the spatial unit hectare (100 x 100 metres) was chosen along with one model run per simulated year. On those scales the availability of statistical data is more or less satisfactory, particularly for recent years. UrbanSim is a system comprising several autonomous subsystems, so-called models. Therefore, UrbanSim might be better described as an urban simulation system, consisting of a software architecture for implementing models and a family of models implemented and interacting with this environment. These models all share a common database by means of which they can exchange information. The common database comprises geographical information, type definitions, characteristics concerning households and jobs etc. Each of the subsystems accessing the database represents an actor or a process, e.g. location choice, land price development or development activities. The models therefore calculate events like construction of buildings or relocation of households. For each subsystem an estimation model with associated parameters is given that controls modifications of the base data set in each simulation cycle. The database is given externally to start with. In the course of the simulation process it is modified by the different models. The final state of the database after processing the last year cycle as well as the states at the end of each simulated year constitute the simulation results. UrbanSim allows for the definition of different development scenarios, e.g. regarding population totals, changed development types or changes in regional accessibility. These scenarios provide the means to examine case studies. For more information and literature about UrbanSim, see Waddell (2005). 5

6 4 HOUSEHOLD SURVEY OF LAND AND RENT PRICES Land and rent prices have a strong impact on the location choice of households as well as businesses. Although land price data is available from administrative offices, the level of disaggregation is not suitable for modelling prices at a grid level of hectares. As private institutions like banks or consultancies are rather reluctant to release such data, the missing information on housing markets has been collected in a recently conducted household survey and by querying an internet database as described in the next section. The survey provided an additional opportunity to match some other data requirements like household relocation probabilities or the rate of home-based jobs. The universe for drawing the sample of the survey consisted of those Swiss municipalities in which at least 5% of the out-commuters commute to Zurich. Another constraint was to proportionally represent the spatial types defined by ARE (Swiss Federal Office for Spatial Development) in the sample. The municipalities and Zurich districts belonging to the Glattal area made part of the sample by definition. The questionnaires were posted to 9330 households in 21 municipalities plus additional four districts of Zurich in February and March The mailing was staggered over a period of two weeks. After two weeks those households which had not answered yet were sent a reminder, after another two weeks a postcard containing a few selectivity questions followed. This postcard will serve to grasp possible response bias among the households which have returned questionnaires compared to those who have only sent back the postcard respectively. As regards the content of the survey (see NSL, 2005b for full questionnaire), most of the questions aim at the interviewees dwellings situation and properties as well as the price of housing. Besides characteristics of the house itself, attributes of the building s surroundings matter as well. These comprise e.g. the availability of recreation spaces and shopping facilities in the neighbourhood. Although availability can be easily associated using GIS (Geographic Information Systems) techniques, the frequency of use and consequently the individual value as well as the attractiveness particularly of the recreation spaces is difficult to determine. To collect data on those two topics, maps were created for each surveyed municipality. Those coloured maps display a choice of areas or facilities in the neighbourhood. The respondents were asked to indicate the frequencies of visits to the given locations. Figure 2 gives an impression of the type of maps used for the survey. To complement the information about dwellings and their surroundings, some questions deal with socio-economic characteristics like household size, place of work or education for all of the household s members as well as mobility tool ownership. This information is needed on the one hand to enhance the explanatory power of the objects information, but can also serve as a base for response analyses by regarding data of those households which have sent back only the postcard. 6

7 Figure 2 Map displaying shopping facilities in Dietlikon as used in the questionnaire Returned questionnaires 36.0% Ref usal 1.8% Death 0.3% Not Eligible 3.1% No Response 58.7% 0% 20% 40% 60% 80% 100% Share (n=9330) Refusal: refused by telephone call or by returning unfilled questionnaire Death: addresses of people who died Not eligible: recipient unknown or has moved Figure 3 Questionnaire response rates 7

8 Figure 4 Questionnaire response rates by municipality Overall, the response rate of the questionnaires is fairly high with 36 percent as can be seen in Figure 3. From those who didn t send back the survey, almost 19 percent sent at least the postcard. Analysis of the return rate by municipality revealed that particularly the share of foreigners in the community affected the return rate of questionnaires negatively while the presence of a supportive cover letter of a municipality official increased it. Data entry is underway. The results will be used to model land prices by hedonic regression for the entire simulation area (see for example Orford S., 1999, for a similar approach). To enrich the data obtained by the survey, geographical coding of the addresses and the use of GIS make it possible to add information like accessibility, topography, view or sunshine duration which can be considered in the hedonic models as well. The sampled household data thus facilitates the area-wide modelling of average land prices against factors like location or configuration on a grid of hectares and the drawing of conclusions on households location choice behaviour. 8

9 5 Analysing and modelling rent prices from an internet database In order to have another data source and to be able to verify modelling results of the household survey, data from the online real estate database comparis ( has been retrieved from the webpage for the whole of Switzerland in autumn 2004 and spring For the analysis, 1184 dwelling units in the simulation area with bid rent prices have been used. A broad set of additional locational attributes were associated to those properties through GIS techniques after they had been geocoded. Data at municipality level was obtained from the Swiss Federal Statistical Office. Moreover, accessibility measures were used, which are based on data from the transport models of the Canton Zurich, including regional accessibility to population measures. Table 1 shows the variables considered in the final model which proved to be most significant as shown in Table 2. The full list of tested variables can be found in the Appendix. 9

10 Table 1: Significant variables in the model Variable Type 1 Mean Median Max Min Std. Structural attributes Total area [m 2 ] C Dwelling unit is a house Locational attributes (property level) Inverse distance to next rail station [m] Log distance to next lake (>1km 2 ) [m] B C C Sunshine index C Locational attributes (traffic zone level) Regional public transport accessibility index C Locational attributes (municipality level) Share of inhabitants with university degree [%] 1 B = binary; C = continuous C

11 Table 2: Determinants of rents in the Greater Zurich area (dependent variable: monthly rent) Variable Coefficient Sig T Constant Structural attributes Total area [m 2 ] Property is a house Locational attributes (property level) Inverse distance to next rail station [m] Log distance to next lake (>1km 2 ) [m] Sunshine index Locational attributes (traffic zone level) Regional public transport accessibility level Locational attributes (municipality level) Share of inhabitants with university degree [%] Total cases: 1184 R-Squared: F-Statistic: Work is underway to expand the regression with a multi-level approach which takes into account spatial factors more appropriate. The benefit of this approach is that it can explicitly model the multilevel nature of locational externalities (Orford S., 1999, p. 37f). 11

12 6 Generating synthetic household data The household data required for running the simulation needs to be generated synthetically as there is no correlated statistical data about households available at hectare level in Switzerland. Because 1990 was chosen as the base year for test runs of UrbanSim for the Zurich area, information dating from that year is being used to generate the required households. The main data source is the Swiss census 1990 which contains summary information concerning e.g. the number of households per size, the number of employed persons or the number of children, all of them at hectare level. Additionally, a 5% sample from that census, the PUS (Public Use Sample) is available. It comprises correlated data relating to single persons but has a very coarse spatial resolution at canton level. Several of the attributes included in the PUS relate to households. The simulation area outlined in section 2 comprises around households. The task of finding a set of households that matches constraints like household size, number of children or employed people provided by census data is a complex combinatorial problem for which an exhaustive search in solution space is computationally not feasible. Heuristic optimisation techniques were therefore considered. Research has been conducted on a very similar problem in the British context by Williamson (1998). Here, three optimisation techniques hill climbing, simulated annealing and genetic algorithms were compared. Simulated annealing resulted in the best match to correlated samples (Williamson P., 1998, p. 802) and was therefore adopted for the Swiss context. Simulated annealing is an algorithm that was developed in analogy to the physical process of controlled annealing of solid material to arrive at an arrangement of atoms with low energy. The algorithm is a variation of hill climbing in that it improves step by step a randomly selected initial solution. In addition it contains a probabilistic component that will accept worse solutions under certain conditions to escape from local optima. These conditions are determined by the temperature parameter which is initially set to a relatively high value and gradually cooled down in the course of the process, thus rendering the acceptance of solutions that are worse than the current constellation less and less probable. The 5 percent PUS serves as the universe from which to draw the initial selection as well as replacements for elements of this selection. Census information for hectares is used for the fitness function expressed as the squared difference in numbers of children and employed. The household sizes are automatically preserved by initially drawing households of the sizes given in the census and consecutively replacing them with households of the same size. Until a previously determined level of conformance has been achieved, single households from the selection are replaced by elements of the universe. The modified selection is kept if it features a better fitness or if a randomly drawn number is smaller than the expression given in equation 1 (see Williamson P., 1998, p. 797). 12

13 p( E) = exp (- E/T) (1) with E difference in fitness between previous and modified selection T temperature parameter householdgenerator reduce := number of swaps at each temperature anneal := rate of temperature decline t l := lower temperature bound for each hectare xy do - temperature parameter T := t xy, swap-counter swaps := 0 - get an initial household selection hsample from PUS that contains as many households of each size as stated in census for xy - add up all workers and children of hsample and calculate the squared difference from census information for xy as e 1 - do if e 1 is lower than a set quality measure q: break else if T < t l : break else if maximum number of loops has been exceeded: break else: e 1 := error of hsample hsample := hsample with one household replaced by a randomly drawn one e 2 := error of hsample if e 1 > e 2 : hsample := hsample, swaps := swaps + 1 else: - rd := random number >=0.0 and < 1.0 drawn from a uniform distribution - if rd < exp((e 1 - e 2 )/ t): hsample := hsample, swaps := swaps + 1 fi fi if swaps >= reduce: T:= T - anneal * T, swaps = 0 fi fi - while (true) - add the households in hsample to database end for each Figure 4 Simulated annealing algorithm applied for household generation The algorithm used for generating the households is outlined in Figure 4 and has been applied to the study area (see section 2) with encouraging first results. An evaluation of the generated households shows conformity to census data regarding the number of employed persons and the number of children for almost 90% of the populated hectares. The correlation structure of household size and age of head of the household given by the PUS is largely preserved in the selection as demonstrated in table 3. One of the next steps will be to find characteristics of hectares that are difficult to fit. This seems beneficial for several reasons. On the one hand it improves the overall fit systematically by finding more appropriate parameters, on the other hand it avoids the selection of parameters that trade off the improvement of a few special cases for a globally worse fit. 13

14 Table 3: Comparison of correlation between household size and age of head of household in PUS (grey) and generated households (white) Number of persons in household or more Age of head ,6 14,3 10,9 5,2 3,7 4,1 24,5 14,8 10,7 5,2 3,9 5, ,3 21,2 36,8 56,9 63,0 64,7 23,2 21,2 34,7 55,8 61,1 58, ,0 36,8 45,0 36,5 31,8 29,4 24,2 37,3 47,0 37,7 33,5 35, ,1 27,7 7,2 1,3 1,5 1,7 28,1 26,6 7,6 1,4 1,5 1,7 One factor which makes error determination difficult is the occurrence of collective households such as asylums or prisons. The census only states the number of such households per hectare but not the respective household sizes and shares of employed persons or children. When not taking into account hectares with collective households, the proportion of matching hectares rises to over 95%. Most of the remaining mismatches seem to occur in hectares with a large number of inhabitants. However, work is underway to achieve perfect matches. Parameters that might be adjusted to arrive at better results are the initial temperature, the number of replacements that are performed at each temperature level and the amount by which the current temperature is reduced when going to the next level. The maximum number of loops introduced into the generation for avoiding dead end states was hardly ever reached and does not appear to have an impact on the overall quality of the outcome. 14

15 7 SUMMARY AND OUTLOOK Applying an integrated land use and transport simulation to the Greater Zurich area opens up opportunities to assess consequences of major transport infrastructure projects and land use policies. Nevertheless, data requirements are considerable and shouldn t be underestimated. So far, all data requirements of UrbanSim could be satisfied. Nevertheless, substantial modelling efforts are necessary beyond those for parameters used in equations of UrbanSim s models. Particularly the described household survey with its depth of information will open excellent opportunities for modelling rent and property prices in the Greater Zurich area. As regards household generation, the method of simulated annealing has proved to be a reasonable approach in order to accomplish the task. After completion of the generation of households with a satisfying level of conformance to census data, imputations on household income and availability of cars for each household have to be made. The Swiss census contains no data regarding those aspects but results of the Swiss National Travel Survey might be used in combination with the generated household attributes to attain the information needed for the simulation. As aforementioned, one goal of the project is to tighten the connection of land use and transport in the simulation system. In order to consider impacts of traffic volumes, speed and noise on location choice, a higher level of spatial detail is required, either by using smaller grid cells or by transforming georeferenced buildings from regular grids to existing and extrapolated building footprint polygons (from hectare to parcel). The project s completion is scheduled for winter 2006/

16 ACKNOWLEDGEMENTS The described project is part of the Swiss Federal Institute of Technology s Network City and Landscape (NSL) research focus The Future of Urbanised Landscapes. It is supported by a grant of ETH Zurich, which is gratefully acknowledged. a) Books and Books chapters REFERENCES Wegener, M. (2004) Overview of Land Use Transport Models. In: D.A. Hensher et al. (eds.): Handbook of Transport Geography and Spatial Systems, Chapter 9, Oxford, Orford, S. (1999) Valuing the built environment GIS and house price analysis, Aldershot, b) Journal papers Waddell, P. (2002) UrbanSim: Modeling Urban Development for Land Use, Transportation and Environmental Planning, Journal of the American Planning Association, Vol. 68 No. 3, Williamson P., Birkin M. and Rees P. (1998) The estimation of population microdata using data from small area statistics and samples of anonymised records, Environment and Planning A, Vol.30 No. 6, c) Papers presented to conferences Martínez, F. and Donoso P. (2001) MUSSA: a land use equilibrium model with location externalities, planning regulations and pricing policies. 7th International Conference on Computers in Urban Planning and Urban Management (CUPUM 2001), Hawaii, July 18-21, Moeckel, R., Schürmann C. and Wegener M. (2002) Microsimulation of Urban Land Use, Proceedings 42nd European Congress of the Regional Science Association, Dortmund, August 27-31, d) Other documents and webpages Miller, E.J. (2005) Integrated Land Use, Transportation, Environment (ILUTE) Modelling System. < [accessed in February 2005]. NSL (2005) NSL homepage < [accessed in February 2005] NSL (2005b) Project homepage Infrastructure, Accessibility and Spatial Development < [accessed in May 2005] Waddell (2005) UrbanSim homepage < > [accessed in April 2005] 16

17 APPENDIX Table 3: Descriptive statistics Variable Type 1 Mean Median Max Min Std. Rent per month [sfr] C Structural attributes Total area [m 2 ] C Number of rooms C Dwelling unit is a house B Dwelling unit is at ground floor level B Locational attributes (property level) Distance to next public transport stop [m] C Distance to next rail station [m] C Distance to next lake (>1km 2 ) [m] C Inverse distance to next public transport stop C Inverse distance to next rail station C Inverse distance to next lake (>1km 2 ) C Log distance to next public transport stop C Log distance to next rail station C Log distance to next lake (>1km 2 ) C Lake (>1km 2 ) within 500m B Lake (>1km 2 ) within 200m B Lake (>1km 2 ) within 100m B Lake (>1km 2 ) within 50m B Share of open space within 200m radius [%] C Sunshine index C Height above sea level [m] C Locational attributes (traffic zone level) Regional car accessibility index C Regional public transport accessibility index C

18 Locational attributes (municipality level) Share of dwelling units occupied by owner [%] C Share of buildings constructed before 1991 C Share of buildings constructed before 1971 C Share of inhabitants whose first language is C not German [%] Tax level index C Share of inhabitants with university degree [%] C Share of vacant dwelling units [%] C Share of vacant rented dwelling units [%] C