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Access and egress times to high-speed rail stations: a spatiotemporal accessibility analysis

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  • Moyano, Amparo
  • Moya-Gómez, Borja
  • Gutiérrez, Javier

Abstract

Accessibility by high-speed rail (HSR) depends not only on station-to-station travel time, but also on access and egress times, which can be determining factors in total journey travel time. However, studies focusing on accessibility analyses of access/egress times to/from stations are less extended in the literature and centre mainly on the influence of access times to stations on HSR accessibility levels on a regional scale. This paper's aim is to evaluate the importance of access and egress times to/from HSR stations in an urban context. We carry out a spatiotemporal accessibility analysis that considers the temporal variations of both taxi and public transport travel times. General Transit Feed Specification (GTFS) files for public transport and TomTom Speed Profiles data for cars are used to measure access/egress times. These kinds of data allow for the calculation of travel times from/to HSR stations through network analysis GIS tools at different times of the day, and thus a spatiotemporal accessibility measure can be obtained. This accessibility measure is complemented by a mass factors representing the activity ‘hotspots’ in the visited city throughout the workday, which is derived from Twitter data, while population is considered for city of residence. This method was applied to the two largest metropolitan areas in Spain: Madrid and Barcelona, where the influence of access/egress times acquires a higher relevance for rail-based trips. The results obtained show that access and egress times vary significantly during the day, depending on the levels of traffic congestion and the frequency of public transport services, which are always more favourable for taxis. In addition, weighted average access and egress times at the home end are higher than those at the activity end since population tends to show more dispersed spatial patterns than activities. Another interesting finding is that the first and last mile of the HSR trip usually account for a high percentage increase in travel time (about 35% for taxis and 55% for public transport, respectively). These results have important policy implications. The paper suggests that HSR accessibility can be improved also by improving local transport services, scheduling coordination and land use policies.

Suggested Citation

  • Moyano, Amparo & Moya-Gómez, Borja & Gutiérrez, Javier, 2018. "Access and egress times to high-speed rail stations: a spatiotemporal accessibility analysis," Journal of Transport Geography, Elsevier, vol. 73(C), pages 84-93.
  • Handle: RePEc:eee:jotrge:v:73:y:2018:i:c:p:84-93
    DOI: 10.1016/j.jtrangeo.2018.10.010
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    File URL: http://www.sciencedirect.com/science/article/pii/S096669231830187X
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    References listed on IDEAS

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    Cited by:

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    2. Zhang, Hui & Cui, Houdun & Wang, Wei & Song, Wenbo, 2020. "Properties of Chinese railway network: Multilayer structures based on timetable data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    3. Borsati, Mattia & Albalate, Daniel, 2020. "On the modal shift from motorway to high-speed rail: evidence from Italy," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 145-164.
    4. Huang, Yan & Zong, Huiming, 2020. "The spatial distribution and determinants of China’s high-speed train services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 142(C), pages 56-70.
    5. Yuyang Zhou & Minhe Zhao & Songtao Tang & William H. K. Lam & Anthony Chen & N. N. Sze & Yanyan Chen, 2020. "Assessing the Relationship between Access Travel Time Estimation and the Accessibility to High Speed Railway Station by Different Travel Modes," Sustainability, MDPI, Open Access Journal, vol. 12(18), pages 1-15, September.
    6. Daniel Albalate & Mattia Borsati, 2019. "“On the modal shift from motorway to high-speed rail: evidence from Italy”," IREA Working Papers 201910, University of Barcelona, Research Institute of Applied Economics, revised Jun 2019.

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