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Assessing public transport systems connectivity based on Google Transit data

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  • Hadas, Yuval

Abstract

A PT system consists of various physical features such as roads, railways, routes, and stops which are represented by a complex network of spatial and temporal data. Since these networks are usually very large and include millions of entities, it is difficult to assess PT systems. Assessment in this context is defined as the ability to extract and analyze data in an automated and recurring process so as to enhance decision making and to make it possible to compare between PT networks over time. The unified methodology that this work presents for extracting, storing and analyzing PT data enables relatively easy spatial analysis with GIS techniques based solely on: (a) Google Transit feeds and (b) Transportation networks. In order to implement this new methodology for analyzing a PT system, five connectivity indicators are introduced: (a) transportation network coverage level; (b) average speed; (c) intersection coverage level; (d) stop transfer potential; and (e) route overlap. This work demonstrates the proposed methodology by analyzing PT systems in Auckland (New Zealand), Vancouver (Canada), and Portland (Oregon, USA).

Suggested Citation

  • Hadas, Yuval, 2013. "Assessing public transport systems connectivity based on Google Transit data," Journal of Transport Geography, Elsevier, vol. 33(C), pages 105-116.
    • Handle: RePEc:eee:jotrge:v:33:y:2013:i:c:p:105-116
    DOI: 10.1016/j.jtrangeo.2013.09.015
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    11. Xu, Wangtu (Ato) & Li, Yongling & Wang, Hui, 2016. "Transit accessibility for commuters considering the demand elasticities of distance and transfer," Journal of Transport Geography, Elsevier, vol. 56(C), pages 138-156.
    12. Goliszek Sławomir, 2022. "The potential accessibility to workplaces and working-age population by means of public and private car transport in Szczecin," Miscellanea Geographica. Regional Studies on Development, Sciendo, vol. 26(1), pages 31-41, January.
    13. Dimitrov, Stavri Dimitri & Ceder, Avishai (Avi), 2016. "A method of examining the structure and topological properties of public-transport networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 373-387.
    14. Wei, Sheng & Zheng, Wei & Wang, Lei, 2021. "Understanding the configuration of bus networks in urban China from the perspective of network types and administrative division effect," Transport Policy, Elsevier, vol. 104(C), pages 1-17.
    15. (Ato) Xu, Wangtu & Zhou, Jiangping & Yang, Linchuan & Li, Ling, 2018. "The implications of high-speed rail for Chinese cities: Connectivity and accessibility," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 308-326.
    16. Fransen, Koos & Neutens, Tijs & Farber, Steven & De Maeyer, Philippe & Deruyter, Greet & Witlox, Frank, 2015. "Identifying public transport gaps using time-dependent accessibility levels," Journal of Transport Geography, Elsevier, vol. 48(C), pages 176-187.
    17. Amparo Moyano & Marcin Stępniak & Borja Moya-Gómez & Juan Carlos García-Palomares, 2021. "Traffic congestion and economic context: changes of spatiotemporal patterns of traffic travel times during crisis and post-crisis periods," Transportation, Springer, vol. 48(6), pages 3301-3324, December.
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    20. Kevin Kane & Young-An Kim, 2020. "Parcels, points, and proximity: Can exhaustive sources of big data improve measurement in cities?," Environment and Planning B, , vol. 47(4), pages 695-715, May.

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