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Accessibility can be defined for personal travel as the ability to reach desired destinations such as jobs, shopping, or recreational opportunities. For goods movement it can be defined as the ability to reach suppliers or buyers of products. Key determinants of accessibility include:
The performance of the transportation system. For a given land use pattern, quicker, more reliable, and/or lower-cost transportation alternatives provide greater accessibility.
Land use patterns, including the density and mix of development. For a given level of transportation performance, a more dense arrangement of land uses means greater accessibility, since more activities can be reached within a given distance/time. The mix of land uses, for example jobs/housing balance or proximity to retail, also influences accessibility.
The resulting benefits and impacts of accessibility changes include:
As an end goal in itself. Providing accessibility can be viewed as a fundamental objective for the transportation system, as it allows people to access desired activities.
Economic activity. Businesses benefit from easier access to suppliers, a larger labor pool, and expanded consumer markets. These factors can reduce transportation costs both for business-related passenger travel and for the movement of goods. Access to larger numbers of workers, consumers, and suppliers also provides greater choice and allows greater specialization, thus increasing business efficiencies.
Land use patterns. Feedback between accessibility and land use means that the relationship between transportation improvements and accessibility gains is complicated. An example is the construction of a new highway. The immediate accessibility improvements may lead to significant land development in the vicinity of the highway. Eventually, however, the traffic generated by new development can cause significant congestion, reducing some of the original accessibility benefits provided by the highway.
Some travel models use accessibility as an input variable. Auto ownership models developed in Philadelphia, Portland, and other places include accessibility as a variable. Portland's model has included transit accessibility for many years, while a model recently developed for Philadelphia includes both highway and transit accessibility. Trip generation models have also been developed using accessibility variables, including models in San Francisco and New Orleans.
While accessibility measures are a basic element in the development of both transportation models and land use models, they are rarely used directly in transportation decision-making. The significance of accessibility improvements may not be as easy to interpret as (for example) travel time savings. Given the growing recognition of its importance, however, the use of accessibility as a performance measure in regional planning is increasing. One area in which it has a particularly promising role is in the measurement of differences in benefits among population groups. For example, accessibility changes can be compared among income groups or for transit versus auto users, to compare the effectiveness of alternative transportation investments.
The included case studies illustrate how accessibility measures are currently being used in the evaluation of transportation and land use alternatives.
During 1998 and 1999, Montgomery County analyzed five alternative year 2020 transportation networks. Planning staff chose to use accessibility as one of a few key indicators of the performance of each alternative transportation scenario.
The Orange County Transportation Authority in California applied GIS to analyze transit accessibility and ridership based on street patterns and land use data.
The Metropolitan Transportation Commission used measures of job accessibility to compare the impacts of the Regional Transportation Plan among different population groups.
Regional accessibility measures were applied to compare the performance of rail and bus transit investment alternatives and transit-oriented development policies.
At the regional level, accessibility can most easily be measured using data from a regional travel demand model. Various measures of accessibility can be derived based on the number of jobs (or other opportunities) accessible within X minutes of the average person living in the region, or the number of residents accessible within X minutes of a typical employment site. The data required to compute this measure include population and employment by TAZ and zone-to-zone travel times. Accessibility can also be distinguished by mode of travel, by the median income of the TAZ of residence, or along other dimensions. Differences in accessibility by population subgroup can be compared among transportation alternatives to measure relative benefits.
The use of regional accessibility measures is demonstrated in the Montgomery County, San Francisco Bay Area, and Tren Urbano case studies. The San Francisco case study also includes an appendix that illustrates the use of occupational matching. Occupational matching is a refinement of the population-to-employment accessibility measure to include consideration of compatibility between the occupations of residents and the types of nearby employment.
Rood (1997) describes a Local Index of Transit Accessibility (LITA), which compares zone-to-zone transit versus automobile travel times, weighted by overall trip volumes between zones. LITA utilizes data from an MPO's regional transportation model. If these data are not available, an alternative index is proposed which reflects qualities of the transit service being provided to a geographic area, based on transit operator data.
Access to transit is a mode-specific component of regional accessibility. It can serve as an indicator of the availability of modal alternatives, particularly to transportation-disadvantaged populations. Measures of transit accessibility can also indicate the extent to which land use-transportation patterns make alternatives to automobile travel feasible.
Measurements of access to transit generally rely on GIS to analyze the spatial relationship between transit stops or routes and population and employment. A basic measure of transit access is the number or percentage of people or employment sites within a reasonable walking distance (one-quarter to one-half mile) of a transit stop. This measure can be calculated in approximate terms by drawing a buffer around transit lines or stations and overlaying this buffer with TAZ-level population and employment data.
More precise methods of transit station area analysis have recently been developed that utilize street network data and/or parcel-level land use data. Network data can be used to identify the actual street network within one-half mile walking distance of transit stations, while parcel data can be used to identify the number of dwelling units or square footage of development that can be reached within this distance. Examples are provided in the Orange County and Tren Urbano case studies. Jasciewicz and Russ (1998) provide an additional example of this type of analysis for light rail transit planning.
Transit access is most meaningful when combined with a level of service and/or accessibility measure. Transit level of service depends on a variety of factors including peak and off-peak headways, hours of service, and the coverage of the system. A detailed transit network in a regional travel model can provide a starting point for estimating transit levels of service between zones, as well as accessibility (i.e., to jobs) by transit. The addition of micro-level analyses such as those detailed above can help refine the analysis by providing a more precise estimate of population or employment convenient to transit.