Environment and Planning B: Planning and Design 2003, volume 30, pages 69 ^ 87

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1 Environment and Planning B: Planning and Design 2003, volume 30, pages 69 ^ 87 DOI: /b12940 Evaluation of accessibility impacts of land-use scenarios: the implications of job competition, land-use, and infrastructure developments for the Netherlands Karst T Geurs National Institute for Public Health and the Environment, PO Box 1, 3720 BA Bilthoven, The Netherlands; karst.geurs@rivm.nl Jan R Ritsema van Eck Urban Research Centre, Faculty of Geographical Sciences, Utrecht University, PO Box 80115, 3508 TC Utrecht, The Netherlands; j.ritsema@geog.uu.nl Received 17 May 2002; in revised form 24 July 2002 Abstract. Activity-based accessibility measures, describing the level of access to spatially distributed activities, are not put to the same use in land use and/or transport policy evaluations as are infrastructure-based accessibility measures, which describe congestion levels or the average speed on the motorway network. In this paper we attempt to improve the current evaluation practice by the application of potential, activity-based, accessibility measures for the analysis of job accessibility, using existing traditional land-use and transport data and/or models. We try to improve the interpretability of the results by estimating the separate influence of land-use changes, infrastructure projects, and congestion on the development of job accessibility. A case study of the Netherlands shows the importance of incorporating job competition and the match between educational and job levels in the analysis of job accessibility. 1 Introduction Accessibility plays an important role in policymaking, and is a central concept used in a number of scientific disciplines such as transport planning, urban planning, and geography. Traditional evaluations of land-use and transport policy often include the impacts on passenger mobility (mainly car use), accessibility, energy use, and emissions. Accessibility is usually evaluated by means of infrastructure-based measures which describe the level of service of the road network, for example, congestion levels or average travel speed. These measures are easy for policymakers and researchers to interpret but have the major disadvantage of excluding the spatial component of accessibility. More important than the level of service of the transport infrastructure is the degree to which this network enables one to reach the necessary facilities to carry out one's desired activities. These facilities are usually unequally distributed in space, which strongly affects conclusions on accessibility. For example, Linneker and Spence (1992) showed Inner London to have the highest access costs in the United Kingdom, but also the highest potential accessibility level to jobs. Activity-based accessibility measures, focused on the distribution of activities in space, are often used in geographical studies but are not put to the same use in transport policy evaluations. In this paper we aim to improve the current practice of evaluating the accessibility impacts of national or regional land-use transport policies (projects, plans, or scenarios) by applying activity-based accessibility measures, using existing (traditional) land-use and transport data and models. We have undertaken a case study of the Netherlands, estimating several activity-based accessibility measures to analyse the development of job accessibility by car between 1995 and 2020, using Dutch national land-use and transport models. A novel feature of the study is the analysis of the separate influence of land-use and transport changes on the accessibility index (for example,

2 70 K T Geurs, J R Ritsema van Eck new employment locations, increased congestion levels, infrastructural expansion), which greatly improves the interpretation of changes in activity-based accessibility measures. We also analysed the relevance of incorporating job and labour-force competition, and matching educational levels of workers to job requirements, in evaluating job accessibility. Job competition was analysed by comparing three types of accessibility measure. First, a potential accessibility measure was applied; this is one of the most popular accessibility measures in geographical studies, and does not include competition effects. Second, an accessibility measure developed by Joseph and Bantock (1982) for the analysis of potential physical access to general practitioners was applied, because of the ability to incorporate competition effects at destination locations. Third, the balancing factors of the well-known doubly constrained spatial interaction model can be interpreted as an accessibility measure that incorporates the interdependent competition effects on origin and destination locations. There is little experience with the application of these as accessibility measures in policy analysis. Furthermore, several studies (for example, Cervero et al, 1997; Shen, 1998) have shown that in the case of job accessibility, inclusion of occupational matching has a strong effect on the results, that is, if residents of an area are close to many job opportunities but do not have the skills or education to qualify for these jobs, they still have a low level of access to employment. Moreover, Van Ham (2001) shows that good access to suitable jobs helps occupational achievement and career advancement over the life course. In this study, the accessibility measures are computed for two educational levelsöhigh (university or bachelor degree), and low-to-medium education level. For a more elaborate description of the study, please refer to Geurs and Ritsema van Eck (2001). In the next section we present a literature review of accessibility measures, followed in section 3 by a description of the methodology used for applying accessibility measures in our case study. In section 4 we describe the results, and in section 5 discuss the implications of the study. In section 6 we present our conclusions. 2 Accessibility measures: a literature review Accessibility is a concept used in such widely differing fields as transport planning, urban planning, geography, and marketing. Accessibility has taken on a variety of meanings and is operationalised in many different ways (see detailed overviews by Geurs and Ritsema van Eck, 2001; Jones, 1981; Schu«rmann et al, 1997). Accessibility measures can be categorised in several ways. Here, three basic perspectives on the measurement of accessibility are identified: (1) Infrastructure-based measures describe the level of service in transport infrastructure. Typical measures are level of congestion and average travelling speed on the road network. Measures of this type are often used in transport planning and play an important role in current transport policies in the Netherlands and other European countries (Ypma, 2000). For example, the current accessibility goal for the Netherlands is to achieve a minimum speed of 60 km/h on the main motorway network during peak hours (NTTP, 2001). (2) Activity-based measures describe the level of access to spatially distributed activities. Activity-based measures can be subdivided into: (a) potential-accessibility measures, analysing accessibility on the macrolevel; examples are in the number of activities within reach from origin locations, often used in urban planning and geographical studies (see, for example, Schu«rmann et al, 1997; Shen, 1998); (b) Time ^ space measures, in which accessibility is analysed at the microlevel, for example, the activities in which an individual can participate at a given time. This type of measure is used in time geography (see for example, Miller, 1991; Wang and Timmermans, 1996).

3 Evaluation of accessibility impacts of land-use scenarios 71 (3) Utility-based measures focus on the (economic) benefits people derive from access to spatially distributed activities. This type of measure has its origin in economic studies (for example, Mart nez, 1995; Mart nez and Araya, 2000; Neuburger, 1971; Williams, 1977). In this paper we focus on potential activity-based accessibility measures in order to evaluate the job-accessibility impacts of land-use and transport scenarios. Infrastructure-based measures provide useful information on the functioning of infrastructure in an area, but fail to recognise that destinations of interest may be far outside this area. Hence their usefulness for our purpose of this paper is limited. Utility-based measures provide valuable information for the economic evaluation of accessibility changes, but our purpose here is to evaluate accessibility effects themselves. Time ^ space measures are potentially very useful for the evaluation of different planning scenarios, but require so many detailed data that their application on a national scale is not (yet) feasible. Several types of potential accessibility measures are used in urban planning and transportation planning studies. The contour measure (also known as: the isochronic measure, cumulative opportunities, proximity count, daily accessibility) is a measure often used in urban planning and in geographical studies (for recent applications, see Gutie rrez and Urbano, 1996; Schu«rmann et al, 1997; Vickerman et al, 1999). The measure indicates the number of opportunities available within a given travelling time or distance. The arbitrary selection of the isochrone of interest, and the lack of differentiation between opportunities adjacent to the origin and those only just within that isochrone, are major disadvantages of the contour measure. As a result, the measure is very sensitive to land-use and travel-time changes, but not very useful for evaluating accessibility changes over time (Geurs and Ritsema van Eck, 2001). Potential accessibility measures have been widely used in urban and geographical studies since the late 1940s (for example, Hansen, 1959; Stewart, 1948; for an overview of references, see Geurs and Ritsema van Eck, 2001). Potential measures estimate the accessibility of opportunities in zone i to all other zones in such a way that more distant opportunities have diminishing influences, thus overcoming the disadvantages of the contour type of measure. The potential accessibility measure has the following general form: A i ˆ Xn j ˆ 1 D j F d ij, (1) where A i is a measure of accessibility in zone i to opportunities D in all zones j, d ij the distance between i and j used, and F is a distance-decay function. A disadvantage is that the functional form leads to high weights for internal accessibility, especially in zones with greater mass. For example, the contribution of a city to its own accessibility may be considerable for large cities. The use of small zones or areas leads to less dependence on the self-potential and provides a good way of avoiding this problem. Furthermore, the distance-decay function used has a significant influence on the accessibility measure. For plausible results, the form of the function should be carefully chosen, and its parameters estimated using recent empirical data of spatial behaviour in the study area. Contour measures and potential accessibility measures do not account for the spatial distribution of the demand for opportunities and the capacity limitations of available opportunities (where jobs are only valid for one worker at any moment in time). In other words: they do not handle competition effects. This may lead to inaccurate or even misleading results when measuring job accessibility (Shen, 1998) To incorporate these competition effects, several authors have developed alternative

4 72 K T Geurs, J R Ritsema van Eck accessibility measures based on potential accessibility measures. The approaches can be grouped into three categories. First, a number of authors have tried to incorporate the effects of competition on opportunities in accessibility measures by evaluating both the opportunities within reach from origin zone i (the `supply' potential) and the relevant population within reach from the same origin zone i (the `demand' potential), then dividing the two (for example, see Knox, 1978; Van Wee et al, 2001; Weibull, 1976). This approach confronts supply and demand by comparing their accessibilities at the same location, and therefore is not suitable for applications such as job accessibility, where the distance between demand and opportunity for meeting this demand (between one's home and one's job) can be considerable. When looking for a job, one considers jobs in the area that can be reached from one's residential zone, i. When one applies for a job in employment zone j, one has to compete with other applicants who live in the area; as well as those who can reach zone, j. These are not identical, and the differences may significantly influence accessibility. In a second approach, taken, for example, by Joseph and Bantock (1982) and Shen (1998), the accessibility of supply and demand at different locations is evaluated; this therefore seems more suitable for our purpose. The basic idea is to divide the supply located in destination zone j by the relevant population within reach of that zone j (the `demand' potential). Then, the resulting `relative supply' for all destination zones within reach of the origin zone i is evaluated. Joseph and Bantock (1982) used this approach to develop a measure for the potential accessibility of general practitioners. The potential accessibility of origin i to general practitioners was calculated by first dividing the number of general practitioners in each zone, j, by the potential demand within the catchment area of those general practitioners. The resulting quotient for all zones j reachable within a certain travel distance from i was then evaluated. Joseph and Bantock's measure is shown in equation (2): 2 3 A i ˆ Xn Gp j 6 X j ˆ 1 4 m 7 F d, (2) P k F d jk 5 ij k ˆ 1 where A i represents the potential accessibility of area i to general practitioners, Gp j is the number of general practitioners in area j within range of area i, P k is the population in area k within the doctors' catchment area, and F(d ij ) is a function of the distance between zones i and j. Shen (1998) refines this approach by including mode choice in the analysis of job accessibility. It should be noted that this approach incorporates competition effects at destinations only. In other words, the measure shows that access to a doctor decreases if the number of patients living within the catchment area of the doctor's practice increases, but it does not incorporate the impact of other doctors present in the neighbourhood on the demand for the original doctor. This may lead to overestimation of the competition effect. A third approach involves interpretation of the balancing factors A i and B j of the doubly constrained spatial interaction model as accessibility measures (see Kirby, 1970; Wilson, 1970; 1971). The (inverse) balancing factors are represented in the following equations: A i ˆ Xn j ˆ 1 1 B j D j F d ij, B j ˆ Xm i ˆ 1 1 A i O i F d ij. (3)

5 Evaluation of accessibility impacts of land-use scenarios 73 In the interaction model, the balancing factors serve to ensure that the magnitude of flow (for example, trips) originating from and destined for each zone equals the correct number for that zone (for example, inhabitants or jobs). Because these are mutually dependent, they have to be estimated iteratively. The functional form of the inverse balancing factors is very similar to the potential accessibility measure and Joseph and Bantock's index. In fact, the first iteration in our estimation is equivalent to computing the demand potential for all zones, and the second iteration is equivalent to computing Joseph and Bantock's measure. In the third step, demand in all zones is divided by this measure and then used to compute a modified demand potential. Next, supply in all zones is divided by this modified demand potential and a modified supply potential is computed and so on, until convergence is reached. In this way, the mutual dependence between the competition on supplied opportunities and the competition on demand is reflected in the inverse balancing factors. Note that the values of A i and B j are not fully specified by equations (3). All A i values can be multiplied by an arbitrary constant c, ifallb j values are divided by the same number. In the computations for our case study, we have, after the necessary number of iterations, scaled B j to an average of 1, which leads to values for A i which are comparable in size with the more familiar potential accessibility measure computed with the same parameters. A major disadvantage of the inverse balancing factors compared with most other measures is that they are less easily interpreted as they are the result of an iterative process incorporating the locations both of demand and of supply. Furthermore, the nature of this iterative procedure makes it somewhat more time consuming to compute. These may be reasons why the measure is relatively seldom used as an accessibility measure. Recently, Mart nez and Araya (2000) and Mart nez (1995) developed utilitybased measures for accessibility and attractiveness by using the respective natural logarithm of the balancing factors a i and b j, multiplied by a cost factor. Mart nez interprets the measures as the economic benefits which landowners can extract from the activities at the origin and the destination. A priori, it seems that the choice of an accessibility measure that does nor does not incorporate competition effects will depend on the study objective. (a) For the purpose of analysing accessibility changes to destinations where no competition effects occur, or competition effects are not a subject of interest, a potential accessibility measure is most suitable. (b) For analysis of accessibility to destinations where competition effects occur in destination locations (for example, nature areas), or available opportunities have capacity limitations (for example, in the analysis of recreational facilities or healthcare facilities), Joseph and Bantock's measure seems useful. However, if the distance between origins and destinations is relatively small, a measure of the type described by Knox (1978) and Weibull (1976) seems useful. (c) The balancing factors of the doubly constrained spatial interaction model may be an appropriate measure for analysing job accessibility where competition effects occur both in destination and in origin locations, that is, workers compete with each other for jobs and employers compete with each other for employees. In our case study we will show to what degree these types of measures lead to different results, and what these differences tell us about spatial aspects of competition effects.

6 74 K T Geurs, J R Ritsema van Eck 3 Case study: application of accessibility measures in the Netherlands for the period 1995 ^ Introduction In this section we describe the methodology used in a case study assessing the accessibility impacts of a land-use transport scenario for the Netherlands for the period 1995 ^ 2020, developed earlier by the RIVM (the Netherlands National Institute for Public Health and the Environment). We describe the application of selected accessibility measures to the analysis of job accessibility by car. We describe three accessibility measures to illustrate the influence of competition, that is, the potential accessibility measure, Joseph and Bantock's measure, and the balancing factor A i. In section 3.2 we describe the land-use scenario, and in section 3.3 the relevant transport developments. In section 3.4 we describe the method used for analysing the separate effects of the transport and land-use components. 3.2 Land-use developments in the Netherlands The land-use scenario is based on the macroeconomic context according to the `European Coordination' scenario of the CPB Bureau for Economic Policy Analysis (CPB, 1997), in which economic and population developments in the Netherlands are moderate. Land-use policies are not assumed to be very restrictive; therefore it is consumers' housing preferences that determine the spatial distribution of housing and related employment developments. Furthermore, planned housing locations are only realised if the policy plans are in an advanced state or if they conform to projected consumer demand. The land-use scenario was constructed using the Dutch national housing-market and employment-market models. For a more detailed description please refer to Geurs and Ritsema van Eck (2002). The land-use data derived from the models comprise inhabitants and households split into seventy-two household types (categorised by age group, educational level, and income level), and an employment split according to educational requirement and economic sector (both on a four-digit postal code basis). In this paper, the data are used to compute job and labour-force accessibility for two education levels: high (university or bachelor degree) and low-to-medium education level (table 1). Note that the data used for this study apply to the Netherlands onlyö the job opportunities (and labour force) in neighbouring countries (such as the Ruhr area in Germany) are not included. This will result in lower job-accessibility levels in regions near the national border. However, the inclusion of these foreign job opportunities would generate unrealistic results, because the financial, political, cultural, and language barriers between European countries have up to now been much more important than physical barriers (expressed in travel time). As an illustration; only a very small percentage of workers living near the national border actually work in neighbouring counties: in 2000, less than workers living in the Netherlands had Table 1. Jobs, inhabitants, and working population, in millions, 1995 ^ Index 1995 ± 2020 million % million % Number of inhabitants Number of jobs low to medium educational level high educational level Working population low to medium educational level high educational level

7 Evaluation of accessibility impacts of land-use scenarios 75 a job in Belgium or Germany (CBS, 2002); this is about 2% of the workers living near the national borders, and less than 0.5% of the total Dutch working population. See also Rietveld and Bruinsma (1998) for a discussion on the impact of national borders on accessibility of European cities. In the Netherlands, the Randstad (the most densely populated area in the western part of the country, containing the four main cities of Amsterdam, Rotterdam, The Hague, and Utrecht) has the major concentration both of jobs and of population (that is about 45% of the population and 50% of all jobs are to be found in just 20% of the country's area). However, the last few decades have seen a process of regional suburbanisation and national deconcentration of urbanisation. The land-use scenario sees the continuation of these land-use trends (figure 1). Urban areas, especially in the Randstad, are to experience decreasing employment, whereas suburban and rural areas will benefit from employment growth (regional suburbanisation). Furthermore, Job-density change (jobs/ha) 4 1:0 1:0 to 1:0 1:0 to 5:0 55:0 Provincial border Emmen Zwolle Amsterdam Apeldoorn The Hague Utrecht Rotterdam Middelburg N 0 75km Figure 1. Development of job density, 1995 ^ 2020.

8 76 K T Geurs, J R Ritsema van Eck the land-use scenario has an employment shift from the Randstad to the `halfway zone' between the Randstad and the rest of the Netherlands, and the eastern part of the country (national deconcentration). These two processes are mainly the result of the lack of space and the high land-use costs of offices in the Randstad (push factors), and the forecast population shift away from the Randstad to peripheral regions (pull factors) (see also Crommentuijn et al, 2002). 3.3 Transport developments In applying potential accessibility measures, the transport-impedance and distancedecay functions must be carefully chosen. Potential accessibility measures may use the amount of travel time, costs, and/or effort as impedance to travel from an origin to a destination. In this case study we use travel times only, as estimated by the Dutch National Model System (NMS) (Gunn, 1994). The output of land-use models for the land-use scenario (number of inhabitants by age group, households, and employment by economic sector) has been translated into input for the NMS. Furthermore, for the situation in 2020, road infrastructure expansions (new roads and expanded capacity), planned for the main road network for the period 2000 ^ 10, are also assumed to have been implemented (V&W, 1999). The NMS was then used to forecast congested travel times for the morning peak-hour period and free-flow travel times for travel by car between all origins and destination zones (total 1308 zones) for 1995 and Potential accessibility measures are strongly influenced by the choice of distancedecay function and its parameters. In this study, the correlation between several impedance functions and actual travel behaviour was analysed with the aid of the 1995 Dutch National Travel Survey (CBS, 1996). The 1995 Travel Survey contains almost trips, with such variables as trip length, trip time, trip purpose, and travel mode. With the aid of these data, a number of different impedance functions were estimated for a combination of trip likelihoods for all modes and purposes of trip. A log-logistic distance-decay function was found to have the best fit with the observed travel data: F d ij ˆ 1 exp a b ln d ij Š 1, (4) where d ij is travel time between i and j, and a and b are parameters to be estimated. This is consistent with results found earlier by Hilbers and Verroen (1993), based on the 1990 National Travel Survey. Incidentally, a desirable property of this distancedecay function is that it has an upper limit of 1 as d ij approaches 0, instead of approaching infinity as the traditionally used power and exponential functions do. This makes the resulting accessibility surface less sensitive to the somewhat arbitrarily chosen intrazonal travel times. Furthermore, it allows interpretation of the potential value as a `fuzzy contour measure'. It counts the number of jobs within reach, where `within reach' is fuzzily defined, and job counts for almost 1 for very small travel times, decreasing to just above 0 for very large travel times. The parameters of separate loglogistic functions were estimated for different modes, purpose of trip, and household characteristics (table 2). Table 2. Parameters for log-logistic distance-decay function, home-to-work car trips. a b Average for all education levels Low to medium education level High education level

9 Evaluation of accessibility impacts of land-use scenarios Contribution of changes in land use and transport to accessibility changes In general, the development of potential accessibility may be the result of changes in (a) land use, that is, the amount, characteristics, and spatial distribution of opportunities, (b) transport, that is, the travel impedance between origin and destination locations (travel time, costs, etc), and/or (c) travel preferences, for example, changes in attitudes resulting in different travel behaviours. In this paper the contribution of land-use and transport changes to the development of job accessibility by car is analysed, and it is assumed that travel preferences remain constant. The land-use component consists of the job-accessibility changes caused by changes in the size and spatial distribution of employment, keeping the travel times constant at base-year level. The travel-time component consists of the job-accessibility changes due to travel-time changes as a result of increased congestion and delays and infrastructure expansions, keeping the number and location of jobs the same as in The combination component contains the accessibility changes resulting from the combination of both the land-use and travel-time components. The components can be the most easily visualised in a simple contour measure (figure 2). Assume a contour around a residential location, enclosing the area that can be reached from that location within, for example, 45 minutes in The number of jobs in this area in 1995 is the contour measure for The change in the number of jobs in this area between 1995 and 2020 is the land-use component of the accessibility change. In 2020, the 45-minute contour will have moved, due to changes in congestion levels, new infrastructure, etc. The area between the two contours, for 1995 and 2020, can be visualised as a ring. The number of jobs in this ring in 1995 (positive where the 2020 contour is wider than the 1995 contour, negative where it is narrower) is the travel-time component. The change in the number of jobs in this ring between 1995 and 2020 is the combination component. Note that visualisation is more difficult for other types of potential accessibility measures because of the absence of a maximum travel distance or time. The general principle of the three components of accessibility change is, however, the same for all potential accessibility measures. These components can be computed from accessibility indices based on the data for both years (1995 and 2020) and making the year of the employment data independent of the year of the travel-time data. So we have the accessibility at time t(a t i ) and at time t 1 (A t 1 i ) and three components: the land-use component (luc), the travel-time component (ttc) and the combination component (cc). The components are measured as: A t i ˆ Xn j ˆ 1 D t j F d t ij, (5) Travel-time limit 1995 Travel-time limit 2020 Contour measure 1995 Travel-time component Land-use component Combination component ) jobs in 1995 ) new jobs in 2020 Figure 2. Land-use, travel-time, and combination components of accessibility change.

10 78 K T Geurs, J R Ritsema van Eck and A t 1 i ˆ Xn j ˆ 1 D t 1 j F d t 1 ij, (6) DA tt 1, i luc DA tt 1, i ttc ˆ Xn j ˆ 1 D t 1 j F d t ij A t i, (7) ˆ Xn D t j F d t 1 ij j ˆ 1 A t i, (8) DA tt 1, i cc ˆ A t 1 i A t i DA tt 1,, i luc i ttc ˆ A t 1 i ˆ A t 1 i A t i Xn j ˆ 1 " X n j ˆ 1 DA tt 1 D t 1 j F d t ij A t i D t 1 j F d t ij Xn D t j ˆ 1 j F d t 1 ij # " X n D t j F d t 1 ij j ˆ 1 A t A t i, (9) where D t j is the number of opportunities (here, employment) in region j at time t, Fis the distance-decay function (here, log logistic), and d t ij is the travel impedance (here, travel time) between region i and j at time t. Furthermore, equation (9) shows that the combination component can be defined as the accessibility changes between time t 1 and t less the sum of the land-use and travel-time components, assuming the components of change to be independent. The travel-time component can be further specified into an infrastructure component, reflecting mainly changes in infrastructure supply but also changed speed regulations, and a congestion component, reflecting changes in delays and congestion. The infrastructure component can be computed in exactly the same way as the travel-time component [equation (8)], but using free-flow travel times instead of peak-hour, congested, travel times. The congestion component then is the difference between the travel-time component and the infrastructure component. Theoretically, there is also interaction between the infrastructure and congestion components. However, the available data do not allow us to separate this from the congestion component itself. 4 Results of the case study 4.1 The impact of job competition on job accessibility The results of the case study can be presented as average accessibility values for the entire study area (Netherlands), and for different regions, showing the spatial distribution of accessibility. Table 3 shows the aggregate results for the different accessibility indicators for 1995 (as an average or per capita value) and the land-use scenario for The potential accessibility measure shows that, in 1995, an average worker in the Netherlands (not accounting for the match between worker skills and educational requirements of jobs) has more than jobs accessible by car if we interpret it as a `fuzzy contour measure' (explained in section 3.4). Figure 3 shows the spatial distribution of job accessibility according to the potential accessibility measure. The highest accessibility levels for 1995 are found in and around the major towns of the Randstad, and the lowest accessibility levels in the northern and southwestern parts of the country. This last is partly because of the exclusion of job opportunities in neighbouring countries (see section 3.2). i #

11 Evaluation of accessibility impacts of land-use scenarios 79 The development of job accessibility for the period 1995 ^ 2020 shows large spatial differences (figure 4, over), while, at the national level, a near-zero accessibility change is exhibited. This is the result of the land-use and transport developments working in opposite directions (see section 4.3). Number of jobs within reach ^ ^ ^ Provincial border N 0 75km Figure 3. Job accessibility by car in 1995, potential accessibility measure. Table 3. Average job accessibility per worker by car, 1995 and Accessibility measure Population group index 1995 ± 2020 Potential measure unweighted average low to medium education level high educational level weighted average Joseph and Bantock unweighted average low to medium education level high education level weighted average Balancing factor unweighted average low to medium education level high education level weighted average

12 80 K T Geurs, J R Ritsema van Eck Job-accessibility change ^ ^ ^ ^ Provincial border N 0 75km Figure 4. Job-accessibility change by car, 1995 ^ 2020, potential accessibility measure. Joseph and Bantocks' index shows a 3% increase in the number of jobs an average worker (not taking into account education level) can reach by car between 1995 and 2020; from 0.98 to 1.0 job per worker. This essentially shows that in the land-use scenario job competition decreases slightly at the national level; this is the result of the relatively stronger job growth forecast in the land-use scenario (see table 1), and the job shift from the Randstad to the middle and eastern parts of the Netherlands, which results in a somewhat more even distribution of jobs. The balancing factor (A i ) shows an 8% lower average job accessibility level for 1995 compared with the potential measure. The regional differentiation of the impact of job competition s relatively large. Within central urban and suburban areas in the Randstad, the inverse balancing factor shows lower values than the potential accessibility measure (up to 20% ^ 25% lower), whereas in peripheral areas the balancing factors may be more than twice as high as the potential measure. The regional differences are illustrated in figure 5, which show a cross-section of the accessibility data from the southwest of the Netherlands (Middelburg) to the northeast (Emmen). The figure shows a balancing factor with lower accessibility levels in the urban and suburban areas of Rotterdam and Utrecht because of a relatively large labour force (a high level of job competition), whereas in peripheral areas the balancing factor shows higher accessibility levels because of a relatively small labour force (a low level of job competition). For the period 1995 ^ 2020, the balancing factor shows a

13 Evaluation of accessibility impacts of land-use scenarios Rotterdam Utrecht Potential measure Balancing factor Job accessibility Zwolle Emmen Middelburg Southwest! Northeast Figure 5. Job accessibility by car for a cross section of the Netherlands for 1995, according to the balancing factor (a i ) and potential measure. job-accessibility growth which is identical to Joseph and Bantocks' index (index 103), and a somewhat higher growth than the potential measure (index 101), because of the increased job availability. Furthermore, the spatial distribution of job accessibility according to the balancing factor for 1995 and 2020 (not shown here) is very similar to the potential measure. 4.2 The impact of matching job and educational levels Both the potential measure and the balancing factor show that if the measure is estimated separately by education level, average accessibility for 1995 is about 40% lower (see table 3). This is primarily the result of the smaller number of available jobs, that is, poorly educated workers have no access to jobs requiring a university degree, and vice versa. For the period 1995 ^ 2020, all three accessibility measures show that job accessibility for highly educated workers will stabilise or slightly decrease, whereas for other employees job accessibility will increase. This is the result of the relatively large increase in workers' education level compared with the job requirements (see table 2), and (to a lesser extent) the forecasted job shift from the relatively job-rich Randstad to the relatively job-poor central and peripheral regions, creating more job competition for highly educated workers who are more concentrated in the Randstad. Thus, the land-use scenario, showing a continuation of land-use and demographic trends, exhibits a growing occupational mismatch between employees' housing locations and job locations in the Netherlands. As a result, the potential measure and balancing factor also show lower average job-accessibility values if one includes occupational matching. (1) Interestingly, Joseph and Bantock's index shows much larger differences between the population groups in the development of job accessibility than the balancing (1) The weighted average accessibility value is computed as the average number of jobs within reach per worker, weighted by the number of workers per educational level for each zone. This accounts for changes in the socioeconomic composition of the population according to the land-use scenario. As a result, the index of the weighted average accessibility value for the period 1995 ^ 2020 for the potential measure and balancing factor is lower (indices 96 and 98, respectively) than for the low/medium and highly educated workers (indices 102, 99, and 105, and 100, respectively).

14 82 K T Geurs, J R Ritsema van Eck factor A i. This demonstrates that the impact of job competition on the development of job accessibility decreases if alternative job locations within the catchment area of competing workers are accounted for. 4.3 The impact of land-use and infrastructure development on job accessibility As we saw in section 4.1, the average increase in accessibility in the period 1995 ^ 2020 is modest: 1% to 3%, depending on the accessibility measure used. This near-zero increase is the result of the different developments working in opposite directions. Here, the influence of the land-use, congestion, and infrastructure components is illustrated for the potential measure, using the methodology presented in section 3.4. Of course, the method can be applied to other measures as well. The potential accessibility measured show that, at the national level, land-use changes result in an average increase in job accessibility of about 32% (land-use component), and infrastructural expansions of about 3% (infrastructure component). However, this increase is almost fully counterbalanced by increased average car-travel times for given origin ^ destination combinations as a result of increased delays and congestion levels (congestion component). The increase in delays and congestion is the result, first, of the overall increase in car traffic (almost 60% for the period 1995 ^ 2020) forecast by the NMS. Second, the current infrastructure network and investments planned for the period 1995 ^ 2010 are not attuned to the (forecast) urbanisation shift from the Randstad to the central and eastern regions of the Netherlands; this results Job-accessibility change ^ ^ ^ ^ Provincial border N 0 75km Figure 6. Land-use component of job-accessibility change, 1995 ^ 2020.

15 Evaluation of accessibility impacts of land-use scenarios 83 in additional bottlenecks in the motorway network in those regions. Furthermore, the combination component (describing accessibility changes resulting from both land-use and travel-time changes) reduces job accessibility by about 12%. The net effect is an increase in the potential measure of 1%. The development of job accessibility between 1995 and 2020 for the land-use scenario shows a rather complicated spatial pattern, where zones with large accessibility increases can be found next to zones with large accessibility decreases. This complex pattern is the result of the combination of different land-use, congestion, and infrastructure changes. Figure 4 shows the development of the potential measure. Accessibility improves in the eastern and northern parts of the country, and in scattered areas in the west. Most suburban areas in the Randstad suffer a larger decrease in accessibility. The development of job accessibility can be explained by mapping the influence of land-use changes, the development of congestion and delays, and infrastructure expansions. Figure 6 shows the development of the number of jobs within reach, assuming no travel-time changes (land-use component). The figure clearly shows the forecast employment growth for the central and eastern regions of the Netherlands. The highest growth is found in and around the city of Utrecht, where job growth contributes to a 70% ^ 75% increase in job accessibility. Figure 7 shows the impact of increased delays and congestion on the development of job accessibility by car (congestion component). The figure shows the development of the difference between estimated travel times and free-flow travel times between 1995 and 2020 as forecast by the NMS. The (absolute) impact of increased delays and congestion Job-accessibility change ^ ^ ^ ^ Provincial border N 0 75km Figure 7. Congestion component of job-accessibility change, 1995 ^ 2020.

16 84 K T Geurs, J R Ritsema van Eck Job-accessibility change Decrease ^ ^ Provincial border N 0 75km Figure 8. Infrastructure component of job-accessibility change, 1995 ^ is highest in the Randstad, and gradually decreases towards the central and peripheral regions. The influence of increased delays and congestion on the development of job accessibility is relatively large. For example, in the western part of the Randstad (including Amsterdam, Rotterdam, and The Hague) the decrease in accessibility due to congestion is larger than the increase in accessibility due to employment growth (figure 6), and the net effect is a reduction of job accessibility by car (as can be seen in figure 4). Figure 8 shows the impact of infrastructure plans on the development of job accessibility. Locations where infrastructure is improved show a relatively small (absolute) increase in job accessibilityöaverage job accessibility increases by about 3%. Interestingly, some fragmented regions show a decrease in job accessibility. At these locations, motorway expansions serve as barriers to local traffic, causing additional delays. The combination component is not mapped here: although it follows the same spatial distribution as the congestion component, the accessibility values are much smaller. 5 Conclusions and discussion Accessibility impacts of land-use and transport policies are often evaluated by means of infrastructure-based measures such as average travelling speed on the road network. Potential, activity-based, accessibility measures, describing the level of access to

17 Evaluation of accessibility impacts of land-use scenarios 85 spatially distributed activities, are not put to the same use in land-use and/or transport policy evaluations. In this paper we have illustrated that traditional evaluations of accessibility impacts of land-use and transport policies can be improved by estimating potential accessibility measures. These measures can be easily computed from existing land-use and transport data, and/or models, which are traditionally employed as input for estimating infrastructure-based measures. The study has four important implications for the current practice of evaluating accessibility impacts of land-use and transport plans and projects. First, the case study of the Netherlands shows that incorporation of job competition into accessibility estimations significantly affects the results. Admittedly, job competition does not radically change the spatial distribution of job accessibility: this is mainly because of the spatial and socioeconomic structure of the Netherlands. Metropolitan and urban areas in the Netherlands areöcompared with other metropolitan areas in Europe or the United Statesöcompact but with a relatively low population and low job densities, and relatively well-mixed land uses. As an illustration, more than 50% of single home-to-work trips take less than 20 minutes travel time and more than 75% take less than 30 minutes (CBS, 1996). As a result, on the aggregate level (not accounting for the match between education and job levels), the spatial distribution of job accessibility by car in the Netherlands is very similar to the distribution of labour-force accessibility. This implies that for the analysis of job accessibility in metropolitan or urban areas in other countries (or in other land-use scenarios for the Netherlands), the impact of job competition on job accessibility may be higher, with stronger spatial variation in land use (for example, large job concentrations in central urban areas) which will thus be more relevant in land-use and transport policy evaluations. The balancing factors of the doubly constrained spatial-interaction model seem to be the most appropriate way to measure job accessibility with competition. This measure accounts for competition effects both on destination and on origin locations, for example, workers compete with each other for jobs, and employers compete with each other for employees. In this way, it is different from other accessibility measures which incorporate competition effects, such as Joseph and Bantocks index, which allow for competition on the supply side (jobs, or physicians) only. The case study shows that Joseph and Bantock's index overestimates competition effect in job-rich areas (where competitors have many alternative job locations within reach), and underestimates these effects in job-poor areas. Second, the interpretability of activity-based accessibility is much improved by estimating the separate influence of land-use changes, infrastructure projects, and congestion on the development of (job) accessibility. The computation of the different components of accessibility facilitates both the explanation of overall accessibility changes and the relative position of regions. The case study of the Netherlands showed a nearzero overall accessibility change, but a relatively complex pattern of local accessibility changes in which quite large increases and decreases in accessibility are found close to each other. The different components show that overall accessibility increases due to employment growth (land-use component) and, to a lesser extent, infrastructural expansion (infrastructure component), is balanced by increased delays and congestion (congestion component). Locally, land-use changes (for example, new employment locations) and/or transport changes (for example, infrastructure bottlenecks) result in a strong spatial variation in job-accessibility changes. Third, the case study shows that incorporating the match between job and educational levels is important in evaluation of job accessibility, and results in more accurate accessibility computations. However, a shortcoming of the study is the aggregate level of analysis: population forecasts at a spatially disaggregate level are only

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