Accessibility Analyst: an integrated GIS tool for accessibility analysis in urban transportation planning

Transcription

1 Environment and Planning B: Planning and Design 2004, volume 31, pages 105 ^ 124 DOI: /b305 Accessibility Analyst: an integrated GIS tool for accessibility analysis in urban transportation planning Suxia Liu, Xuan Zhu National Institute of Education, Nanyang Technological University, 1 Nanyang Walk, Singapore ; xzhu@nie.edu.sg, suxia1006@hotmail.com Received 28 January 2003; in revised form 15 April 2003 Abstract. The authors present an integrated GIS tool, Accessibility Analyst, for accessibility analysis in urban transportation planning, built as an extension to the desktop GIS software package, ArcView. Accessibility Analyst incorporates a number of accessibility measures, ranging from catchment profile analysis to cumulative-opportunity measures, gravity-type measures, and utility-based measures, contains several travel-impedance measurement tools for estimating the travel distance, time, or cost by multiple travel modes along actual travel routes, and interoperates with GIS data-management and data-integration, spatial-analysis, network-analysis, surface-modelling, and spatial-visualisation functions. Undertaking accessibility analysis with use of Accessibility Analyst allows the user to take full advantage of a GIS to produce spatial distributions of accessibility over a region. It can be applied to a wide range of issues in urban transportation planning, such as for studies on the relationship between transportation and land use, evaluation of transportation network efficiency, transportation infrastructure planning, and for impact assessments relating to transportation policies. 1 Introduction Accessibility is an important characteristic of urban areas and a crucial link between transportation and land use. As urban transportation planning is increasingly being considered as an integral element of overall urban land-use planning, accessibility is becoming a key element in analysing the efficiency of transportation systems, in predicting travel demand in programming transportation investments, and in evaluating planning policies in the urban transportation planning process (Gutiërrez et al, 1998; Handy and Niemeier, 1997; O'Sullivan et al, 2000; Polzin, 1999; Tolley and Turton, 1995). Accessibility can be broadly defined as the ease with which activities at one place may be reached from another via a particular travel model. It is dependent on the spatial distribution of potential destinations relative to the starting point of an individual, the performance of the transportation system in connecting spatially separated locations, the characteristics of the individual regarding whether he or she can make use of the transportation system, and the magnitude, quality, and character of the activities found there, as well as the times at which the individual is able to participate in the activity and whether the activity is available. Therefore, accessibility analysis encompasses spatial and socioeconomic aspects, requires extensive data, and involves a large amount of computation. Over the past decade, many researchers have turned to geographical information systems (GIS) technology for accessibility analysis (Arentze et al, 1994; Geertman and van Eck, 1995; Gutiërrez et al, 1998; Juliao, 1999; Kwan, 1998; O'Sullivan et al, 2000; Shen, 1998; van Eck and de Jong, 1999). As standard GIS lack accessibility functions, previous efforts were focused on building specific accessibility measures for particular applications into stand-alone modelling programs with direct or indirect access to the GIS database and spatial analysis functions. Most of the GIS-based accessibility analysis efforts are made via the loose or tight coupling approach. With the loose coupling approach, an accessibility modelling program is integrated with a GIS

2 106 S Liu, X Zhu software package via data exchange by using either an ASCII or a binary data format without a common user interface. Arentze et al (1994) and Shen (1998) adopted this approach. The advantage of loose coupling is that it involves minimal programming efforts, but data exchange between the accessibility modelling program and the GIS software package can be tedious and error prone. Tight coupling embeds an accessibility modelling program with a GIS package via either GIS macro or conventional programming. This approach can achieve a higher level of integration and can allow the accessibility modelling program to access the GIS database directly and share the same user interface with the GIS. Miller (1991), Geertman and van Eck (1995), Kwan (1999), and Weber and Kwan (2002) applied this approach. In addition to the efforts in integrating GIS with accessibility measures for specific applications, a dedicated modelling software package called Flowmap was developed for analysing and displaying interaction or flow data for geographical research ( It is the only software package available with a set of accessibility measures embedded that does not involve any commercial GIS software packages. However, the datamanagement, network-analysis, and mapping capabilities of Flowmap are in no way comparable to those available in commercial GIS. In this paper, we present an integrated GIS tool, Accessibility Analyst, which was built by integrating a number of well-established accessibility measures with a desktop GIS package, ArcView (ESRI, 1996a), using the tight coupling approach. It provides an integrated and flexible desktop GIS environment for accessibility analysis for a wide range of applications in urban transportation planning. The remainder of the paper is organised as follows. In section 2 the concepts and measures of accessibility are reviewed. In section 3 the application of GIS in accessibility analysis is discussed. In section 4 the functionality of Accessibility Analyst is described. Through a case study, the use of Accessibility Analyst is demonstrated in section 5 to analyse accessibility by the MRT (mass rapid transit) system in Singapore. In section 6 the strengths and limitations of Accessibility Analyst are discussed. The paper is concluded in section 7 with a discussion of the future development of Accessibility Analyst. 2 Accessibility measures Accessibility is determined by the pattern of land use, the nature of the transportation system, and the characteristics of the traveller (Geertman and van Eck, 1995; Handy and Niemeier, 1997; Hanson, 1986; Jones, 1981). Travel cost, time, distance, and the choice of travel mode are all important. The closer the origin and destination to the main transportation system the higher the level of accessibility. The wider the variety of modes for travelling between a given origin and a particular destination the greater the accessibility. In addition, the less time and money spent in travel the more places that can be reached within a certain budget and the greater the accessibility. In order for the concept of accessibility to be useful for evaluation of the need for and effectiveness of transportation and land-use planning policies it needs to be translated into measures of accessibility. Different accessibility measures have been developed over the past four decades for various analytical and evaluative purposes. They can be broadly grouped into four types: opportunity-based measures, gravity-type measures, utility-based measures, and space ^ time measures. Opportunity-based measures are concerned with the number of opportunities (or destinations) available within a certain distance from an origin (Breheny, 1978). An opportunity-based measure can simply be to find the nearest destinations to an origin and calculate their distances, which in this paper is called `catchment profile analysis', or to count the number of destinations or opportunities available within a specified distance from an origin, which is called a `cumulative-opportunity measure'

3 Accessibility Analyst 107 (Wachs and Kumagai, 1973). A cumulative-opportunity measure can be expressed in general terms as 8 X < M j ; if d ij 4 L, A i ˆ j (1) : 0 ; if d ij > L, where A i is the accessibility of origin i, M j is the attractiveness at destination j for a given set of opportunities, d ij is the travel distance (time or cost) from origin i to destination j, and L is a given distance (time or cost) limit. The attractiveness of a destination can be a function of the number of opportunities found there, such as number of employment oppotunities, number of retail and service outlets, and number of industrial activities and recreational opportunities. Gravity-type measures are derived from the gravity model of spatial interaction, which suggests that the potential of opportunity between two places is positively related to the sizes of the attractiveness of the places and negatively related to the travel impedance between them (Hansen, 1959; Linneker and Spence, 1992). The gravitytype measures are also called `potential models' (Geertman and van Eck, 1995; Rich, 1978). They are commonly used to measure the intensity of possible interaction between social or economic groups at different locations and have been interpreted variously as providing a measure of the influence of one place or group on another, as a generalised measure of concentration or density (for example, of population), as an index of the nearness of groups to a location, and as an indicator of accessibility of groups in different places relative to each other (Rich, 1978). They can be used to determine the aggregate centrality of places in relation to population, industry, employment, or services in the surrounding area (Geertman and van Eck, 1995). The general mathematical equation of a gravity-type measure, or potential model, can be written as ˆ X M j f d ij, i ˆ 1, 2,.::, m; j ˆ 1, 2,.::, n, (2) A i j where f(d ij ) is the impedance function, m is the number of origins, and n is the number of destinations. The most frequently used impedance functions are the inverse power function, d a ij, and the negative exponential function, exp( ad ij ). Here, a is a constant representing the effect of distance decay on the accessibility. Gravity-type measures or potential models have been used for many applications and their use has a long history (Hansen, 1959; Jones, 1981; Wilson, 1971). However, Shen (1998) identified a major limitation with gravity-type measures when he used such a model in the study of the employment accessibility of low-wage workers living in Boston's inner-city neighbourhoods. He found that to make a gravity-type measure valid, either the demand for the available opportunities must be uniformly distributed across the space or the available opportunities must have no capacity limitation. In reality, however, the above conditions may not be met. In particular, the first condition is seldom met in urban areas as population, companies, services, and other activities and facilities are distributed unevenly. Therefore, when he applied a gravitytype measure to employment accessibility it generated an unreasonable and misleading result because of the fact that each job is for only one worker at any time and the spatial distribution of workers who are suitable for the available jobs is not uniform. He concluded that, because opportunities exist in locations with different levels of demand potential, accessibility to each of the opportunities is determined partly by the demand potential for the particular location of the opportunities. As a result of this, in his analysis of employment accessibility, he introduced into the gravity-type

4 108 S Liu, X Zhu measure a demand potential for each location and developed a new measure, which is expressed as A i where D j ˆ X j M j f d ij D j, (3) ˆ X N k f d kj, i, k ˆ 1, 2,.::, m; j ˆ 1, 2,.::, n, (4) k where D j is the demand potential for destination j, and N k is the demand for opportunities (for example, the population seeking employment opportunities) at origin k. We call this measure a `double constrained potential model'. It can be applied to situations where competition for available opportunities exists and where accessibility to these opportunities is influenced by the demand potential for the particular location of opportunities (Shen, 1998). The accessibility values returned from the gravity-type measures discussed above cannot be interpreted easily (Geertman and van Eck, 1995; Handy and Niemeier, 1997). There is no clear sense of what constitutes a high or low accessibility value. Geertman and van Eck (1995) developed a modified potential model that provides a measure of aggregate accessibility that gives a value in meaningful units. This model can be written as A i ˆ X j M j dij a 1 X k M k d a ik. (5) The results will be accessibility values measured in the same unit as d ij and can be interpreted as weighted-average distances or travel costs or time to all destinations. Utility-based measures relate accessibility to the notion of consumer surplus in microeconomic theory and apply random utility theory to model the behaviour of and net benefits to different users of a transportation system (Ben-Akiva and Lerman, 1979). Utility-maximising choice behaviour implies that the benefit or consumer surplus received by an individual is the maximum utility of a choice set. Assuming that an individual p assigns a utility to each destination choice in a choice set C p, and then selects the alternative that maximises his or her utility, accessibility can be defined as the denominator of the multinomial logit model (Small, 1992): A p ˆ ln X exp u pq, (6) q 2 C p where u pq ˆ v pq bc pq, (7) where u pq is the benefit associated with opportunity q, to individual p, v pq is the value of making the trip to take opportunity q, c pq is the travel cost for individual p to travel to opportunity q, and b is a cost-sensitivity parameter. This type of accessibility measure indicates the desirability of the full choice set C p (Small, 1992). The final type of accessibility measure is the space ^ time measure. An important aspect of space ^ time measures is their emphasis on the range and frequency of the activities in which a person takes part and whether it is possible to sequence them so that all can be undertaken in the possible path (Jones, 1981). In other words, good accessibility should include not only good spatial or locational accessibility but also temporal accessibility. Most space ^ time measures of individual accessibility are based on Ha«gerstrand's (1970) time ^ geographic framework.

5 Accessibility Analyst 109 The space ^ time framework recognises that activity participation has spatial and temporal dimensions and that these dictate the necessary conditions for virtually all human interactions (Burns, 1979; Ha«gerstrand, 1970). The fundamental construct of space ^ time accessibility measures is the space ^ time prism, which is the set of locations in space ^ time that are accessible to an individual given the locations and duration of fixed activities, a time `budget' for flexible activity participation, and the travel velocities allowed by the transportation system (Ha«gerstrand, 1970). Miller (1991) carried out a requirement analysis for computing network-based space ^ time prism measures using GIS. He further formulated these measures by reconciling the space ^ time approach with other theoretical frameworks for accessibility measurement and developed computational procedures for calculating these measures within network structures by using a GIS (Miller, 1999). Kwan (1998) developed a computational algorithm using GIS to calculate space ^ time measures that reflect different dimensions of the accessibility experience of individuals. Weber and Kwan (2002) developed a geocomputational algorithm for implementing space ^ time measures in GIS by taking into account traffic congestion and business hours. Instead of looking at travel time separated from time spent on activities, Dijst and Vidakovic (2000) examined the relation between travel time and stay time and operationalised this relation with the concept `travel time ratio'. Dijst et al (2002) developed a theoretical and methodological space ^ time framework based on the concept of action spaces within which the opportunities for transport mode change of different types of households in various areas can be analysed. In general, the space ^ time framework is a detailed approach allowing many different types of people to be considered and requiring the complexity of real-world transportation networks to be handled. As a result, it involves large amounts of data and intensive computation (Jones, 1981; Kwan, 1999). 3 Accessibility analysis and GIS Accessibility analysis involves developing or selecting appropriate accessibility measures according to the purpose of the analysis and the nature of the planning issues, specifying and calculating the accessibility measures, and presenting and interpreting the analytical results. An accessibility measure may be specified in terms of the spatial unit for analysis, the socioeconomic groups whose accessibility is to be assessed, the type of opportunities, the mode of travel, the origins and destinations, the attractiveness of the destinations, and the travel impedance. The spatial unit for accessibility analysis can be a zone (such as census track), a building block, a household, or an individual. The socioeconomic groups often refer to population groups defined according to their socioeconomic characteristics, such as household income, employment status, occupation, and gender. Opportunities can be retail outlets, job opportunities, schools, child care centres, etc. The travel mode can be walking, use of a private car, and public transport (buses and trains). The origins and destinations are places from which and to which accessibility will be measured. The attractiveness of a destination is usually measured based on those characteristics of a potential destination that are important to the destination choice, for example, the number of establishments (for instance, the number of retail shops), or the physical size (for instance, the gross floor space and parking area of shopping centres), or the economic size (for instance, the number of jobs). Travel impedance, representing the spatial separation between an origin and a destination, is commonly measured in terms of travel distance, time, or cost, estimated by straight-line distance or network distance (along actual travel routes) or distance as calculated by network models that simulate travel demand and congestion

6 110 S Liu, X Zhu levels (Wickstrom, 1971). When using a gravity-type measure or potential model, the value of the distance-decay parameter a also needs to be specified. An integral part of accessibility analysis is the collection, manipulation, and analysis of spatial and nonspatial data. GIS, with an integrated database of basic socioeconomic, transportation, and land-use information, provides many useful functions to support accessibility analysis. The strength of a GIS for accessibility analysis lies in its capabilities for modelling real-world transportation networks based on the network data model, deriving and generating data for computing accessibility measures through the spatial and network analysis functions, mapping the calculated accessibility values, and linking the accessibility values with other georeferenced socioeconomic and infrastructure data (Miller, 1991; van Eck and de Jong, 1999). For example, reclassification, buffer-generation, and map-overlay functions available in GIS can be used to define the spatial unit for accessibility analysis by generating zones represented as regular or irregular spatial shapes and patterns (for example, traffic-analysis zones, administrative regions, equally sized grid cells, and hexagonal zones), and aggregate or disaggregate socioeconomic and land-use data based on the spatial unit. GIS can also be used to define origins and destinations as points or zones and to link them with the data about their socioeconomic characteristics. The spatial-measurement, proximity-analysis, and network-analysis functions in GIS can support the measurement of travel impedance by calculating the straight-line distance and shortest path. In addition, GIS surface modelling and spatial visualisation functions can be used to facilitate the presentation and interpretation of the results of accessibility analysis. Moreover, use of GIS provides flexibility for the calibration of accessibility measures by varying the parameter values and incorporating different datasets. However, the standard tools in current GIS have limitations in investigating accessibility problems. For example, the buffer-generation function can be used to create zones around a given set of trip origins to exclude activities beyond a specified traveldistance limit. All locations within a certain buffer zone are considered to have the same level of accessibility to the activities available in the zone, without considering the presence and quality of the transportation network. Network analysis in GIS is used mainly to find the shortest path over the transportation network between two locations, to find the smallest set of paths that connect a set of locations, and to find all locations that are within a given cost, time, or distance from a specified location. However, it is often assumed that travel is by private car over the road network and that all activity sites are located exactly on the network. Therefore, the network-analysis function measures accessibility only for specific nodes on the road network, not for any point over the study area. Thus, buffer generation and network analysis are not able to give a general overview of accessibility in a region (Geertman and van Eck, 1995). In addition, accessibility measurements in standard GIS are actually distance measurements. They do not take into account the socioeconomic characteristics, the level of demand for, and attraction of the activity sites. As an integrated GIS tool, Accessibility Analyst developed in this research aims to overcome some of these limitations. 4 Functionality of Accessibility Analyst Accessibility Analyst was developed as an extension to ArcView, using ArcView Avenue programming language. It is integrated into the ArcView GIS environment (Version 3.2). Accessibility Analyst provides a set of accessibility measures and allows users to select the accessibility measures suited to their needs, to specify the selected measures, and to implement the specifications. Together with other ArcView extensions, including

7 Accessibility Analyst 111 ArcView GIS environment Spatial Analyst Network Analyst Accessibility Analyst 3D Analyst Patch Analyst Figure 1. Software components of Accessibility Analyst. Spatial Analyst, Network Analyst, 3D Analyst, and Patch Analyst, it provides an integrated GIS environment for accessibility analysis (figure 1). Here, the ArcView GIS environment provides functions for collecting, managing, manipulating, and mapping the data required for accessibility analysis. Spatial Analyst (ESRI, 1996b) provides tools for spatial analysis and modelling, including buffer generation, proximity analysis, neighbourhood and zone analysis, terrain modelling (for example, contour generation and slope), and map algebra. Network Analyst (ESRI, 1996c) provides functions for basic network analysis, such as finding the shortest path, finding the closest facility, and identifying a service area around a site. 3D Analyst (ESRI, 1996d) is used to create surface models and to visualise data in three Figure 2. The user interface of Accessibility Analyst.

8 112 S Liu, X Zhu dimensions (3D). Patch Analyst (Rempel et al, 1999) is used to generate tessellated hexagons, which can be used as the spatial unit for accessibility analysis. Accessibility Analyst provides four groups of functions: data preparation, travel-impedance measurement, accessibility measurement, and visualisation. In figure 2 we show the user interface of the Accessibility Analyst extension. 4.1 Data-preparation functions Four functions in this group are designed for preparing spatial data for accessibility analysis. The Get Polygon Centroid function calculates the geometric centres (centroids) of zones representing origins or destinations. These centroids can be used as the point references to the origins and destinations so that the distances between them can be calculated. In some cases, point features (such as building blocks) are grouped together according to some criteria (such as estate or street names), with each group representing an origin or a destination. The centroid of a group of points is used as a reference point for the corresponding origin or destination in accessibility analysis. The Get Points' Centroid function is used for calculating the geometric or weighted centre of a group of points. The Find Nearest Point on Network function locates the points on the road or transportation network closest to the origins or destinations. 4.2 Travel-impedance measurement functions All measures of accessibility incorporate the travel impedance between an origin and a destination, which is generally represented in terms of distance, time, or cost. Accessibility Analyst provides functions to measure both straight-line distance and network distance (that is, the distance along the actual travel routes) and to estimate travel time and cost based on the distance. All functions for measuring travel impedance use map data, containing the point references to the origins and destinations, and output the travel impedance values in matrices called origin ^ destination matrices, or OD matrices (de Jong and van Eck, 1996; Geertman and van Eck, 1995; Shen, 1998). Six functions are available in Accessibility Analyst for measuring travel impedance (see figure 2). The Straight-line Distance Matrix function calculates the straight-line distance from each origin to every destination. It does not take into account the effects of the transportation network. The Shortest-Path Distance Matrix function uses the Network Analyst functions for finding the best route to calculate the distance over the shortest path along the transportation network for every OD pair. To use this function, origins and destinations must be points located exactly on the transportation network. In many cases, the origins and destinations (for example, homes and parks) are not located on the transportation network. Therefore, in such cases the Shortest-Path Distance Matrix function cannot be used directly to calculate the shortest network distance between an origin and a destination. In general, the travel distance between an origin and a destination can be considered to consist of three parts (figure 3): (1) d 1, the travel distance from the origin to point OB (the nearest point on the road network to the origin for travel by car or by foot, or the nearest transit stop on the transit network to the origin for travel by bus or train); (2) d 2, the shortest network distance by the specified travel mode from point OB to point DA (the point on the road network nearest to the destination for travel by car or walking, or the transit stop nearest to the destination for travel by bus or train); and (3) d 3, the travel distance from point DA to the destination. The Network Distance Matrix function is designed to calculate the total travel distance from each origin to every destination based on the measurements of these three distances. The Network Time Matrix function calculates the travel time between origins and destinations based on the three parts of distance. Each part may involve only a single

9 d 3 Destination Accessibility Analyst 113 Origin d 1 OB d 2 DA Figure 3. The three `parts' of distance, d 1, d 2,andd 3. OB is the origin boarding point and DA is the destination alighting point. travel mode. This function is based on the distance data generated by using the distance matrix functions described above and the average travelling speed of a particular travel mode for each part of distance. The result is an OD matrix containing the travel time for each OD pair. The Network Cost Matrix function is specially designed for Singapore. It calculates travel cost according to the adult Farecard fares on air-conditioned buses, LRT (light rapid transit), and MRT in Singapore. Farecard is a stored-value card issued by Singapore's TransitLink Company that can be used on buses, MRT, and LRT. The adult fares are based on distance travelled ( The Matrices Operation function is designed for mathematically manipulating OD matrices. It allows users to perform mathematical operations on two OD matrices with the same origins and destinations. The mathematical operations it supports include calculation of the minimum or maximum value, and sum, difference, product, and division. It also supports a link operation. The link operation allows users to add two OD matrices when the destinations in the first OD matrix are the origins in the second matrix. None of the travel-impedance measurement functions in Accessibility Analyst takes into account the speed limits of network links and the effect of turn restrictions and directionality. In other words, they do not use the cost attributes (such as speed limits, travel directions, and turns) associated with each transportation network link to calculate the network distance, travel time, and cost. 4.3 Accessibility measurement functions This group of functions supports accessibility measurement by providing several accessibility measures, including a catchment profile analysis, a cumulative-opportunity measure, a potential model, a modified potential model, a double constrained potential model, and a utility-based measure. All accessibility measurement functions use the point map data

10 114 S Liu, X Zhu layers representing the reference points to the origins and destinations, and OD matrices generated by the travel-impedance functions as inputs. All these functions, except the Catchment Profile Analysis function, produce an output table with the following fields: origin identification number (ID), total distance (time or cost) from one origin to all destinations, calculated accessibility value, and normalised accessibility value. The Catchment Profile Analysis is used to find the nearest destinations for each origin. If an origin has more than one nearest destination, the function will calculate the straight-line distances between the origin to all its nearest destinations. The destination that has the shortest straight-line distance to the origin is selected as its nearest destination. The result of the function is a table, called the catchment profile table, containing the origin IDs, the nearest-destination IDs, and the distances (time or cost values) between each origin and its nearest destination. Cumulative-Opportunity Measure implements cumulative-opportunity measures represented as in equation (1). Potential Model implements the gravity-type measures expressed in equations (2) and (4). Double Constrained Potential Model, Modified Potential Model, and Utility Based Measure implement equations (3), (5), and (6), respectively. 4.4 Visualisation functions Accessibility Analyst utilises ArcView mapping, surface modelling, and charting capabilities to display accessibility analysis results. All accessibility values derived by the accessibility measurement functions are stored in tables, as described above. These tables can be linked or joined with the attribute tables of relevant origin or destination map data layers for mapping, interpolation, and 3D visualisation. In addition, the standard charting functions of ArcView can be used to make charts to present those tabular data visually. Adding to the standard ArcView visualisation functions, Accessibility Analyst provides two special visualisation functions for accessibility analysis: a location profile chart and a cumulative profile chart. The Location Profile Chart function is designed for calculating and displaying the percentage of origins within a certain range of distance (time or cost) to a particular destination. The Cumulative Profile Chart function can be used in two situations. In the first situation, it deals with a single destination, just as the Location Profile Chart function does. It calculates and displays the cumulative percentages of origins within different ranges of distance (time or cost) to a particular destination. In the second situation, the Cumulative Profile Chart function is used to make cumulative charts, based on catchment profile tables, that describe the cumulative percentages of origins within different ranges of distance (time or cost) to their nearest destinations. 5 A case study Accessibility Analyst has been used in several applications (Liu, 2002) in which the size of the OD matrix has been up to , with more than 4000 network links. This case study, drawn from a larger study on the impact of the public transport system on accessibility in Singapore, illustrates the use of Accessibility Analyst in measuring accessibility by the Singapore MRT system. Public transport is, and in the foreseeable future will be, the major mode of transportation in Singapore. The government intends to provide a comprehensive range of integrated public transport services to meet the increasing demand for economic development and at the same time to control private car usage. In order to achieve this goal, a hierarchical system with defined roles for each transportation element has been designed. The MRT system is to serve the heavy transit corridors (that is, heavily used corridors) primarily for long-haul travel whereas the LRT system will serve `light' corridors (that is, less heavily used corridors) and provide feeder services to the existing MRT network. Scheduled buses are to serve the

11 Accessibility Analyst 115 light corridors and complement the rail networks. At the same time, to cater for an increasing number of trips resulting from a larger and increasingly mobile population, existing road and rail networks are being expanded and new MRT lines are being constructed. The basic MRT system was constructed as two main lines: the North ^ South Line from Marina Bay to Jurong East via Woodlands, and the East ^ West Line from Pasir Ris to Boon Lay, with a total route length of 83 km and 48 stations (figure 4). The new Changi Airport MRT extension branches off from the main East ^ West Line and consists of two stations: Expo Station and Changi Airport Station. Construction began on the Changi Airport MRT extension in January 1999, and Expo Station was opened for revenue service on 10 January 2001; the Changi Airport Station started operation in 8 February Another new MRT line, the Northeast Line, is currently in the final phase of civil works. It has a total length of 20 km and comprises sixteen stations. The third new line, the Circle Line, started construction in early 2002 and is expected to be completed in The Circle Line is to link all radial transit lines running into the city. It will interchange with the North ^ South Line, East ^ West Line, and Northeast Line. According to the Singapore Census 2000 ( html#census), 88% of households live in public housing estates that are planned, provided, and managed by the Housing and Development Board (HDB), a government statutory board. Each HDB public housing estate is called an HDB town. There are twenty-six HDB towns in Singapore. For the purpose of preparing the detailed plans, called development guide plans (DGPs), for Singapore's long-term physical development, Singapore was divided into fifty-five planning areas, or DGP zones, fifty one of which are located on the main island. The DGP zones are further divided into subzones. The size of each DGP zone and its subzones varies depending on the land uses and existing physical separators such as expressways, rivers, and major open spaces. In the case study New MRT station N New MRT line Basic MRT station Basic MRT line LRT line Figure 4. The mass rapid transit (MRT) and light rapid transit (LRT) systems in Singapore. miles

12 116 S Liu, X Zhu presented here we measure the level of accessibility to the working population living in the HDB towns by MRT attained by each DGP zone and investigate the effects of the new MRT lines (the Changi Airport MRT extension and the Northeast Line) on the accessibility. We aim to show which DGP zones can reach a large working population. We use a potential model, as expressed in equation (4), to calculate accessibility values for all DGP zones [that is, the demand potentials for the DGP zones (destinations)]. The demand for working opportunities from an HDB town is represented by its total working population obtained from HDB (2000); the travel impedance between the HDB town and the given DGP zone is measured as travel time; the impedance function takes the form of the inverse power function. The value of the distance-decay parameter a used most often in empirical studies as 1 (Gutierrez and Gomez, 1999), which was also used in this investigation. It is also assumed that (1) travelling between an HDB town or a DGP zone centre and its closest MRT station is either by foot if it is within an acceptable walking distance, or otherwise by bus; (2) there are always bus services from the HDB towns or DGP zones to the MRT stations; (3) the bus routes from HDB towns or DGP zones to the MRT stations are taken to be the shortest street paths (this is assumed because of detailed data on bus routes); (4) there are no transfer or waiting times. A procedure for this accessibility analysis using Accessibility Analyst is provided in figure 5. Table 1 (over) lists the functions of Accessibility Analyst used for this procedure. Figures 6(a) and 6(b) (over) show the accessibility surfaces resulting from the analysis for the scenarios before and after the new MRT lines were introduced, respectively. Working-population figures were fixed in both scenarios. Each accessibility surface is represented as a 2D isoline map and a 3D view. The peaks in the 3D views represent high accessibility values, indicating a high level of accessibility to the working population living in the HDB towns, whereas the valleys represent low accessibility values. In general, the patterns of accessibility in the two scenarios look very similar. Most of the DGP zones along the East ^ West Line and the southern sections of the North ^ South Line have higher accessibility values, because they are highly accessible and also closer to the HDB towns that have a large working population (see figure 7, over). The DGP zones in the periphery areas, except in the south, are low-accessibility areas because they are far away from the MRT network and the HDB towns. In both scenarios, the Bedok DGP zone (BD) has the highest accessibility to the working population because of its proximity to the two HDB towns with the largest working populations in Singapore, whereas the Changi Bay DGP zone (CB) has the lowest accessibility value. Figure 8 (over) shows the changes in accessibility to working population, in which the 3D view represents the relative changes and in which the 2D isoline map describes the absolute changes. The absolute changes are indeed the difference between the accessibility surfaces shown in figures 6(a) and 6(b), and the relative changes are their percentage differences. It can clearly be seen that, in general, the DGP zones along the new MRT lines show a greater improvement than do the others for which the greater distance from the new lines means less benefit is obtained. The biggest improvements occur in the Punggol DGP zone (PG) with a 20.6% increase in accessibility to the working population; the Changi DGP zone (CH) with an increase of 17.2%, the Hougang DGP zone (HG) with an increase of 10.1%; and the Paya Lebar DGP zone (PL) and the Outram DGP zone (OP), each with an increase of 9.6%. However, the new lines have had little impact on the accessibility to the working population in the north region or in the area along the section of the East ^ West Line starting from the Bedok DGP zone to its eastern end. There are basically two reasons why this is the case. First, there are no HDB towns in the Changi DGP zone (CH) or the Changi Bay DGP zone (CB).

13 Step 1: Find the closest point to each HDB town centre on the street network (an origin boarding point, or OB) Step 2: Find the closest point to each MRT station on the street network (a destination alighting point, or DA) Step 3: Calculate the shortest paths between the OBs and DAs over the street network Step 4: Generate a time OD matrix Step 5: Perform a catchment profile analysis to get the closest MRT station to each HDB town Step 6: Find the closest MRT station in terms of travel time for each DGP zone centre by following Steps 1±5 Step 7: Calculate the shortest distances between the MRT stations Step 8: Formulate a time OD matrix for travel by MRT (called an MRT time OD matrix) The travel time over the shortest distance between an HDB town centre and an MRT station ˆ The walking time from the centre to its corresponding OB The walking time from the MRT station to its corresponding DA The traveltimebybus between the OB and DA The travel time by MRT between an HDB town and a DGP zone ˆ The travel time from the town centre to its nearest MRT station The travel time by MRT between the station nearest to the town centre and the station nearest to the DGP zone The travel time between the DGP zone centre and its nearest MRT station 1 Figure 5. Procedure for measuring the level of accessibility to the working populations of Housing and Development Board (HDB) towns from the development guide plan (DGP) zones. Note: MRT, mass rapid transit; OD, origin ^ destination. 1 Step 9: Find the closest point to each HDB town centre on the street network (an origin boarding point, or OB) Step 10: Find the closest point to each DGP zone centre on the street network (a destination alighting point, or DA) Step 11: Calculate the shortest paths between the OBs and DAs over the street network Step 12: Generate a time OD matrix for travel by bus (called a bus time OD matrix) Step 13: Formulate a time OD matrix by comparing the bus time OD matrix and the MRT time OD matrix Step 14: Apply a potential model Step 15: Interpolate accessibility surfaces Step 16: Present the accessibility values Thetraveltimebybus over the shortest distance between an HDB town and a DGP zone ˆ The walking time from the centre to its corresponding OB The walking time from the DGP zone centre to its corresponding DA The bus travel time between the OB and DA over the street network The total travel time betweenanhdbtown and a DGP zone ˆ Minimum (the travel time by bus over the shortest distance between an HDB town and a DGP zone, the travel time by MRT between an HDB town and a DGP zone) Accessibility Analyst 117

14 118 S Liu, X Zhu Table 1. The functions of Accessibility Analyst, and the inputs and outputs for each step in the procedure described in figure 5. Step Function Input Output 1 Find Nearest Point on Network 2 Find Nearest Point on Network 3 Shortest-Path Distance Matrix 4 Network Time Matrix 5 Catchment Profile Analysis 6 Same as steps 1 ± 5 7 Shortest-Path Distance Matrix 8 Network Time Matrix 9 Find Nearest Point on Network 10 Find Nearest Point on Network 11 Shortest-Path Distance Matrix 12 Network Time Matrix 13 Matrices Operation 26 HDB town centres Street network (comprising 4070 links) 48 MRT stations (16 more in the `after' scenario) Street network Origin boarding points Destination alighting points Street network HDB town centres MRT stations Bus shortest-path table HDB town centres MRT stations Time OD matrix from step 4 Same as steps 1 ± 5, except the origins are the DGP zone centres instead of the HDB town centres MRT stations MRT network 26 HDB town centres 51 DGP zone centres Catchment profile tables from steps 5 and 6 MRT shortest path table HDB town centres Street network DGP zone centres Street network Origin boarding points Destination alighting points Street network HDB town centres DGP zones centres Bus shortest-path table Bus time OD matrix MRT time OD matrix 14 Potential Model HDB town centres DGP zone centres Time OD matrix from step ± 16 ArcView's Surface Functions HDB town centres DGP zone centres Accessibility value table 26 origin boarding points 48 destination alighting points Bus shortest-path table (a distance OD matrix) time OD matrix Catchment profile table Catchment profile table MRT shortest-path table (a distance OD matrix) MRT time OD matrix) Origin boarding points Destination alighting points Bus shortest-path table (a distance OD matrix) bus time OD matrix time OD matrix Accessibility value table Accessibility surface Note: DGP, development guide plan; HDB, Housing Development Board; MRT, mass rapid transit; OD, origin ± destination.

15 Accessibility Analyst 119 (a) (b) Figure 6. Accessibility surface for the scenarios (a) before and (b) after the new mass rapid transit (MRT) lines were introduced. Second, the use of the Northeast Line in comparison with the use of bus transportation actually increases the travel distance from the northern and eastern part of the island to the HDB town areas along the line because travellers have to transfer at MRT stations in the city centre (for example, at City Hall Station). Based on our assumptions described above, in the `before' scenario, a traveller from the northern or eastern part of the island might go to an area close to the Northeast Line by first taking MRT to the MRT station on the North ^ South Line or East ^ West Line nearest to the destination

16 120 S Liu, X Zhu Figure 7. Housing Development Board (HDB) towns and their working populations (source: HDB, 2000). Figure 8. Changes in accessibility of development guide plan (DGP) zones to working populations in the Housing Development Board towns after the introduction of the new mass rapid transit (MRT) lines. and then walking or taking a bus to the destination. The distance and time required for travel in this way may be shorter than those required for taking MRT to the city centre first and then to the destination using the Northeast Line. Therefore, the new Northeast Line does not benefit the travellers in those areas in terms of time. In contrast, some benefit is obtained in the western part of the island because of the linkage of the East ^ West Line with the Northeast Line, which improves the accessibility of the western region to the northeastern region.

17 Accessibility Analyst Strengths and limitations of Accessibility Analyst Accessibility Analyst provides an integrated GIS tool for accessibility analysis in urban transportation planning. Broadly speaking, it has several advantages. First, Accessibility Analyst integrates a number of well-established measures for measuring levels of accessibility over a region. The functions in Accessibility Analyst for measuring travel impedance can be used to measure straight-line distances, shortest-path distances, three-part network distances, travel time, and costs. The distance for a journey can be divided into several three-part network distances, and these can be used in combination to calculate total travel distances in complicated situations where multiple travel modes are involved. With these different accessibility measures and travel-impedance measurement tools, Accessibility Analyst can be applied to a wide range of research and planning issues, such as studies of the relationship between transportation and land use, evaluation of the efficiency of transportation networks, transportation infrastructure planning, facility planning concerning the accessibility of service provision to the target markets of those facilities (for example, new housing developments, and new school and shopping-centre developments), and the impact assessment of transportation policies. Unlike the standard GIS network analysis which requires all origins and destinations (or their point references) to be located on the transportation network, Accessibility Analyst can measure accessibility between the defined origins and destinations located anywhere in a study area, thus providing an overall view of accessibility covering the entire region. In addition, Accessibility Analyst allows accessibility analysis to be conducted at any level of spatial resolution or spatial disaggregation. Accessibility can be measured by point as well as by zone. When accessibility is measured by zone, a zone centroid is chosen and becomes the point of accessibility reference for the zone as a whole. Zones can be of any size. A study area can also be tessellated into small zones of equal size, such as hexagons and grid cells. This allows users to perform a more detailed analysis, to identify more efficiently those areas with poor accessibility, to measure accessibility regarding the use of facilities across administrative or statistical boundaries, and to obtain data that reflect more closely actual access patterns. Moreover, Accessibility Analyst is developed within the ArcView GIS environment. A significant advantage of Accessibility Analyst is that its accessibility analysis functions interoperate with the data-management, data-integration, spatial-analysis, network-analysis, surface-modelling, spatial visualisation, and other functions of ArcView without any need for data exchange via computer system files. Preprocessing and postprocessing of data are handled using the ArcView functions. Accessibility analysis functions directly access and operate on shape files the (ArcView data format) and the results are displayed directly by using ArcView visualisation functions (such as charting, mapping, and 3D visualisation). The results of accessibility analysis are also saved as shape files for further analysis and mapping. Such integration effectively extends a desktop GIS with efficient accessibility analysis capabilities. Therefore, Accessibility Analyst is an integrated desktop GIS tool for accessibility analysis available to a wide range of users. However, Accessibility Analyst also has some limitations. The accessibility measures built into the system are based on the locational proximity of opportunities. They do not take into account any personal preferences in travel behaviour and ignore the role of individual time ^ budget and space ^ time constraints in measuring accessibility. In other words, they tend to reflect place accessibility rather than individual accessibility (Kwan, 1998; Pirie, 1979). Therefore, Accessibility Analyst is more suitable for large-scale transportation analysis and planning. It is not designed for activity-based analysis. In addition, the functions in Accessibility Analyst for measuring travel impedance do not take into account the schedules of public transportation service on a route