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A GIS-based toolkit for route choice analysis

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  • Papinski, Dominik
  • Scott, Darren M.

Abstract

This paper develops and applies the route choice analysis (RCA) toolkit. This GIS-based toolkit generates a suite of over 40 variables describing route characteristics such as distance, travel time, speed statistics, number of intersections, number of turns, number of stop signs/stop lights, and a measure of route circuity, to name a few. The input to the toolkit is one or more routes, which can be obtained from global positioning system (GPS) data or some other means (e.g., shortest path). While the toolkit is designed to support route choice modeling by generating variables that have been tested in previous modeling efforts, we demonstrate its utility by testing the hypothesis that workers choose routes to minimize either travel time or distance between home and work. A GPS-enhanced data set of 237 observed routes for home-to-work trips collected for auto drivers in Halifax, Nova Scotia, Canada is used in our analysis. We find that the null hypothesis is refuted – that is, a comparison of observed routes to their shortest-path alternatives based on time and distance via inferential statistics indicates that observed routes are significantly longer compared to their alternatives. This finding suggests that workers may choose routes based on other route attributes. The attributes generated by the RCA toolkit for observed, shortest time, and shortest distance routes are compared and significant differences are noted.

Suggested Citation

  • Papinski, Dominik & Scott, Darren M., 2011. "A GIS-based toolkit for route choice analysis," Journal of Transport Geography, Elsevier, vol. 19(3), pages 434-442.
    • Handle: RePEc:eee:jotrge:v:19:y:2011:i:3:p:434-442
    DOI: 10.1016/j.jtrangeo.2010.09.009
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    References listed on IDEAS

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    Cited by:

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    2. Kuo, Pei-Fen & Lord, Dominique & Walden, Troy Duane, 2013. "Using geographical information systems to organize police patrol routes effectively by grouping hotspots of crash and crime data," Journal of Transport Geography, Elsevier, vol. 30(C), pages 138-148.
    3. Shatu, Farjana & Yigitcanlar, Tan & Bunker, Jonathan, 2019. "Shortest path distance vs. least directional change: Empirical testing of space syntax and geographic theories concerning pedestrian route choice behaviour," Journal of Transport Geography, Elsevier, vol. 74(C), pages 37-52.
    4. Mengying Cui & David Levinson, 2021. "Shortest paths, travel costs, and traffic," Environment and Planning B, , vol. 48(4), pages 828-844, May.
    5. Jaap Vreeswijk & Tom Thomas & Eric Berkum & Bart Arem, 2014. "Perception bias in route choice," Transportation, Springer, vol. 41(6), pages 1305-1321, November.
    6. Dorsey, Bryan & Mulder, Alice, 2013. "Planning, place-making and building consensus for transit-oriented development: Ogden, Utah case study," Journal of Transport Geography, Elsevier, vol. 32(C), pages 65-76.
    7. De Cantis, Stefano & Ferrante, Mauro & Kahani, Alon & Shoval, Noam, 2016. "Cruise passengers' behavior at the destination: Investigation using GPS technology," Tourism Management, Elsevier, vol. 52(C), pages 133-150.
    8. Yiannakoulias, Nikolaos & Scott, Darren M., 2013. "The effects of local and non-local traffic on child pedestrian safety: A spatial displacement of risk," Social Science & Medicine, Elsevier, vol. 80(C), pages 96-104.
    9. Ciscal-Terry, Wilner & Dell'Amico, Mauro & Hadjidimitriou, Natalia Selini & Iori, Manuel, 2016. "An analysis of drivers route choice behaviour using GPS data and optimal alternatives," Journal of Transport Geography, Elsevier, vol. 51(C), pages 119-129.

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