Accessibility (transport)

In transport planning, accessibility refers to a measure of the ease of reaching (and interacting with) destinations[1] or activities distributed in space,[2][3] e.g. around a city or country.[4][5] Accessibility is generally associated with a place (or places) of origin. A place with "high accessibility" is one from which many destinations can be reached, or destinations can be reached with relative ease. "Low accessibility" implies that relatively few destinations can be reached for a given amount of time/effort/cost or that reaching destinations is more difficult or costly from that place.

Access to jobs by public transit in Toronto

The concept can also be defined in the other direction, and we can speak of a place having accessibility from some set of surrounding places. For example, one could measure the accessibility of a store to customers as well as the accessibility of a potential customer to some set of stores.

In time geography, accessibility has also been defined as "person based" rather than "place based", were one would consider a person's access to some type of amenity through the course of their day as they move through space.[6] For example, a person might live in a food desert but have easy access to a grocery store from their place of work.

Accessibility is often calculated separately for different modes of transport.[7]

Mathematical definition

In general, accessibility is defined as:

where:

  • = index of origin locations
  • = index of destination locations
  • = a set of weights associated with destinations e.g. the number of jobs in a traffic analysis zone
  • is a cost of travel from to and
  • is an impedance function on the travel cost giving the utility of a destination.[8]

Cost metrics

Travel cost metrics ( in the equation above) can take a variety of forms such as:

Cost metrics may also be defined using any combination of these or other metrics. For a non-motorized mode of transport, such as walking or cycling, the generalized travel cost may include additional factors such as safety or gradient. The essential idea is to define a function that describes the ease of travelling from any origin to any destination .

Impedance functions

The function on the travel cost determines how accessible a destination is based on the travel cost associated with reaching that destination. Two common impedance functions are "cumulative opportunities" and a negative exponential function. Cumulative opportunities[12][8] is a binary function[13] yielding 1 if an opportunity can be reached within some threshold and 0 otherwise. It is defined as:

where is the threshold parameter.

A negative exponential[12] impedance function can be defined as:

where is a parameter defining how quickly the function decays with distance.

Relation to land use

Accessibility has long been associated with land-use;[14][15] as accessibility increases in a given place, the utility of developing the land increases.[16][2] This association is often used in integrated transport and landuse forecasting models.

In practice

Transport for London utilize a calculated approach known as Public Transport Accessibility Level (PTAL) that uses the distance from any point to the nearest public transport stops, and service frequency at those stops, to assess the accessibility of a site to public transport services. Destination-based accessibility measures are an alternate approach that can be more sophisticated to calculate. These measures consider not just access to public transport services (or any other form of travel), but the resulting access to opportunities that arises from it. For example, using origin-based accessibility (PTAL) we can understand how many buses one may be able to be access. Using destination-based measures we can calculate how many schools, hospitals, jobs, restaurants (etc..) can be accessed.[17]

See also

References

  1. Farber, Steven; Fu, Liwei (2017-03-01). "Dynamic public transit accessibility using travel time cubes: Comparing the effects of infrastructure (dis)investments over time" (PDF). Computers, Environment and Urban Systems. 62: 30–40. doi:10.1016/j.compenvurbsys.2016.10.005. ISSN 0198-9715.
  2. El-Geneidy, Ahmed; Levinson, David (2006-05-01). "Access to Destinations: Development of Accessibility Measures". Cite journal requires |journal= (help)
  3. Song, Ying; Miller, Harvey; Stempihar, Jeff; Zhou, Xuesong (2017-10-01). "Green accessibility: Estimating the environmental costs of network-time prisms for sustainable transportation planning". Journal of Transport Geography. 64: 109–119. doi:10.1016/j.jtrangeo.2017.08.008. ISSN 0966-6923.
  4. Andrew, Owen; Brendan, Murphy (2018). "Access Across America: Transit 2017". Cite journal requires |journal= (help)
  5. Owen, Andrew; Levinson, David M. (2016-10-08), "Developing a Comprehensive U.S. Transit Accessibility Database", Springer Geography, Springer International Publishing, pp. 279–290, doi:10.1007/978-3-319-40902-3_16, hdl:11299/180074, ISBN 9783319409009
  6. Miller, Harvey J. (2005-12-06), "Place-Based Versus People-Based Accessibility", Access to Destinations, Emerald Group Publishing Limited, pp. 63–89, doi:10.1108/9780080460550-004, ISBN 9780080446783
  7. Iacono, Michael; Krizek, Kevin; El-Gemeidy, Ahmed (2010-01-01). "Measuring non-motorized accessibility: issues, alternatives, and execution". Journal of Transport Geography. 18 (1): 133–140. CiteSeerX 10.1.1.558.6960. doi:10.1016/j.jtrangeo.2009.02.002. ISSN 0966-6923.
  8. Andrew, Owen; Brendan, Murphy (2018). "Access Across America: Transit 2017 Methodology". Cite journal requires |journal= (help)
  9. El-Geneidy, Ahmed; Levinson, David; Diab, Ehab; Boisjoly, Genevieve; Verbich, David; Loong, Charis (2016-09-01). "The cost of equity: Assessing transit accessibility and social disparity using total travel cost". Transportation Research Part A: Policy and Practice. 91: 302–316. doi:10.1016/j.tra.2016.07.003. hdl:11299/179814. ISSN 0965-8564.
  10. Nassir, Neema; Hickman, Mark; Malekzadeh, Ali; Irannezhad, Elnaz (2016-06-01). "A utility-based travel impedance measure for public transit network accessibility". Transportation Research Part A: Policy and Practice. 88: 26–39. doi:10.1016/j.tra.2016.03.007. ISSN 0965-8564.
  11. Levinson, David; Cui, Mengying (2018-10-05). "Full cost accessibility". Journal of Transport and Land Use. 11 (1). doi:10.5198/jtlu.2018.1042. ISSN 1938-7849.
  12. Allen, Jeff; Farber, Steven (2018). "Generating measures of access to employment for Canada's eight largest urban regions". doi:10.31235/osf.io/pvrd9. Retrieved 13 October 2018. Cite journal requires |journal= (help)
  13. Fayyaz S., S. Kiavash; Liu, Xiaoyue Cathy; Zhang, Guohui (2017-10-05). "An efficient General Transit Feed Specification (GTFS) enabled algorithm for dynamic transit accessibility analysis". PLOS ONE. 12 (10): e0185333. doi:10.1371/journal.pone.0185333. ISSN 1932-6203. PMC 5628824. PMID 28981544.
  14. Hansen, Walter G. (1959). "How Accessibility Shapes Land Use". Journal of the American Institute of Planners. 25 (2): 73–76. doi:10.1080/01944365908978307. ISSN 0002-8991.
  15. Geurs, Karst T.; Van Wee, Bert (2004-06-01). "Accessibility evaluation of land-use and transport strategies: review and research directions". Journal of Transport Geography. 12 (2): 127–140. doi:10.1016/j.jtrangeo.2003.10.005. ISSN 0966-6923.
  16. Iacono, Michael; Levinson, David (2017-02-01). "Accessibility dynamics and location premia: Do land values follow accessibility changes?". Urban Studies. 54 (2): 364–381. CiteSeerX 10.1.1.226.4890. doi:10.1177/0042098015595012. ISSN 0042-0980.
  17. Lock, Oliver; Pinnegar, Simon; Z Leao, Simone; Pettit, Christopher. "'Chapter 28: The making of a mega-region: evaluating and proposing long-term transport planning strategies with open-source data and transport accessibility tools.". In Geertman, S; Stillwell, J (eds.). Handbook of Planning Support Science. Edward Elgar Publishing. pp. 442–457. ISBN 9781788971072.
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