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Exploring the Spatial-temporal dynamics of travel patterns and air pollution exposure of E-scooters

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  • Vallamsundar, Suriya
  • Jaikumar, Rohit
  • Venugopal, Madhusudhan

Abstract

Dockless mobility has been the biggest disruptive force in the shared mobility industry solving the “first-last” mile issue. With their high adoption levels combined with little to no regulation regarding their usage, these users have been driving along with motorized vehicles exposing them to major concerns. The users are exposed to high levels of traffic-related air pollution (TRAP) due to their direct exposure to vehicle exhaust. This study aims to understand the spatial and temporal dimensions of this emerging transportation mode in terms of travel behavior patterns, geographical aspects of travel, interactions between the travel route taken with the existing vehicle traffic, and resulting air pollution and exposure. The travel behavior patterns are evaluated through spatial-temporal analysis of a sample of e-scooter trip data collected in 2018 in the City of Austin and an online travel behavior survey. The analysis identified areas with peak usage, and peak ridership time. The survey results found the e-scooter user demographics to be mostly white males, in the 26–45 age range, with an undergraduate degree and working full-time. Secondly, key responded in influencing the use of an e-scooter are trip length, connectivity to transit, congestion and parking issues, and pollution reduction. Thirdly, e-scooter predominantly replaced personal vehicles and shared ridership in case of home-to-work trips and replaced walking for connecting to transit stops. The exposure to TRAP was obtained by integrating the spatial-temporal dynamics of e-scooter trips with spatial-temporal dynamics of pollutant concentrations modeled from traffic. Exposure analysis found peak exposure levels during midday and evening periods focused in the Central Austin area. This area houses the University of Texas campus and several neighborhoods with lots of shopping, restaurants, bars, and live music avenues. The findings are useful for policymakers and planners when planning for infrastructure changes air pollution control measures, incentive programs, and policies to motivate shared mobility.

Suggested Citation

  • Vallamsundar, Suriya & Jaikumar, Rohit & Venugopal, Madhusudhan, 2022. "Exploring the Spatial-temporal dynamics of travel patterns and air pollution exposure of E-scooters," Journal of Transport Geography, Elsevier, vol. 105(C).
    • Handle: RePEc:eee:jotrge:v:105:y:2022:i:c:s0966692322002009
    DOI: 10.1016/j.jtrangeo.2022.103477
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    References listed on IDEAS

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    1. Foissaud, Nicolas & Gioldasis, Christos & Tamura, Shun & Christoforou, Zoi & Farhi, Nadir, 2022. "Free-floating e-scooter usage in urban areas: A spatiotemporal analysis," Journal of Transport Geography, Elsevier, vol. 100(C).
    2. Shaheen, Susan & Guzman, Stacey & Zhang, Hua, 2010. "Bikesharing in Europe, the Americas, and Asia: Past, Present, and Future," Institute of Transportation Studies, Working Paper Series qt79v822k5, Institute of Transportation Studies, UC Davis.
    3. McKenzie, Grant, 2019. "Spatiotemporal comparative analysis of scooter-share and bike-share usage patterns in Washington, D.C," Journal of Transport Geography, Elsevier, vol. 78(C), pages 19-28.
    4. Shaheen, Susan A & Guzman, Stacey & Zhang, Hua, 2010. "Bikesharing in Europe, the Americas, and Asia: Past, Present and Future," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6qg8q6ft, Institute of Transportation Studies, UC Berkeley.
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    Cited by:

    1. Zhang, Yuting & Nelson, John D. & Mulley, Corinne, 2024. "Learning from the evidence: Insights for regulating e-scooters," Transport Policy, Elsevier, vol. 151(C), pages 63-74.
    2. Colovic, Aleksandra & Prencipe, Luigi Pio & Giuffrida, Nadia & Ottomanelli, Michele, 2024. "A multi-objective model to design shared e-kick scooters parking spaces in large urban areas," Journal of Transport Geography, Elsevier, vol. 116(C).

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