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Quantifying the employment accessibility benefits of shared automated vehicle mobility services: Consumer welfare approach using logsums

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  • Ahmed, Tanjeeb
  • Hyland, Michael
  • Sarma, Navjyoth J.S.
  • Mitra, Suman
  • Ghaffar, Arash

Abstract

The goal of this study is to assess and quantify the potential employment accessibility benefits of shared-use automated vehicle (AV) mobility service (SAMS) modes across a large diverse metropolitan region considering heterogeneity in the working population. To meet this goal, this study proposes employing a welfare-based (i.e. logsum-based) measure of accessibility, obtained via estimating a hierarchical work destination-commute mode choice model. The employment accessibility logsum measure incorporates the spatial distribution of worker residences and employment opportunities, the attributes of the available commute modes, and the characteristics of individual workers. The study further captures heterogeneity of workers using a latent class analysis (LCA) approach to account for different worker clusters valuing different types of employment opportunities differently, in which the socio-demographic characteristics of workers are the LCA model inputs. The accessibility analysis results in Southern California indicate: (i) the accessibility benefit differences across latent classes are modest but young workers and low-income workers do see higher benefits than high- and middle-income workers; (ii) there are substantial spatial differences in accessibility benefits with workers living in lower density areas benefiting more than workers living in high-density areas; (iii) nearly all the accessibility benefits come from the SAMS-only mode as opposed to the SAMS+Transit mode; and (iv) the SAMS cost per mile assumption significantly impacts the magnitude of the overall employment accessibility benefits.

Suggested Citation

  • Ahmed, Tanjeeb & Hyland, Michael & Sarma, Navjyoth J.S. & Mitra, Suman & Ghaffar, Arash, 2020. "Quantifying the employment accessibility benefits of shared automated vehicle mobility services: Consumer welfare approach using logsums," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 221-247.
    • Handle: RePEc:eee:transa:v:141:y:2020:i:c:p:221-247
    DOI: 10.1016/j.tra.2020.09.002
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    2. Bahk, Younghun & Hyland, Michael & An, Sunghi, 2024. "Re-envisioning the Park-and-Ride concept for the automated vehicle (AV) era with Private-to-Shared AV transfer stations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 181(C).
    3. Gao, Jing & Li, Sen, 2024. "Regulating for-hire autonomous vehicles for an equitable multimodal transportation network," Transportation Research Part B: Methodological, Elsevier, vol. 183(C).
    4. Jing Gao & Sen Li, 2023. "Regulating For-Hire Autonomous Vehicles for An Equitable Multimodal Transportation Network," Papers 2301.05798, arXiv.org, revised Oct 2023.
    5. Zou, Tianqi & Aemmer, Zack & MacKenzie, Don & Laberteaux, Ken, 2022. "A framework for estimating commute accessibility and adoption of ridehailing services under functional improvements from vehicle automation," Journal of Transport Geography, Elsevier, vol. 102(C).
    6. Monika Hamerska & Monika Ziółko & Patryk Stawiarski, 2022. "A Sustainable Transport System—The MMQUAL Model of Shared Micromobility Service Quality Assessment," Sustainability, MDPI, vol. 14(7), pages 1-18, March.
    7. Hosseinzadeh, Aryan & Baghbani, Asiye, 2020. "Walking Trip Generation and Built Environment: A Comparative Study on Trip Purposes," MPRA Paper 109025, University Library of Munich, Germany.
    8. Sarri, Paraskevi & Kaparias, Ioannis & Preston, John & Simmonds, David, 2023. "Using Land Use and Transportation Interaction (LUTI) models to determine land use effects from new vehicle transportation technologies; a regional scale of analysis," Transport Policy, Elsevier, vol. 135(C), pages 91-111.

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