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Spatial and Temporal Factors in Estimating the Potential of Ride-sharing for Demand Reduction

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  • Tsao, H.-S. Jacob
  • Lin, Da-Jie

Abstract

Traffic congestion has been a pervasive problem in many urban areas of this country. This paper studies the potential of carpooling among unrelated partners (i.e., inter-household carpooling) for demand reduction during peak commute hours. Basic questions about this potential include the following. Can the current population density, origin-destination distribution, tolerable pick-up and drop-off delays, departure time distribution, and the tolerance for deviation from preferred departure time support a sizable carpooling population that can make a significant contribution to traffic demand reduction? Could the proportion of long trips that are likely candidates for carpooling (e.g., those long trips with same O-D) be so small that no significant traffic demand reduction could be expected from carpooling? The potential depends on many factors, some of which are more amenable to quantification than others. Our approach to assessing the potential is to separate such quantifiable factors from the rest, and then, based on these quantifiable factors, identify likely upper bounds for the potential. This paper focuses on a simplified urban sprawl in which the densities of workers and jobs are uniform over an infinitely large flat geographical area. For our numerical study, we use the job and worker data of the city of Los Angeles to approximate the worker/job density. An entropy optimization model that is equivalent to the gravity model is used for trip distribution. Under the assumptions made in the paper, carpooling among unrelated partners has little potential for demand reduction. Key Words: Carpool, Demand Management, Urban Sprawl, Trip Distribution, Entropy Optimization

Suggested Citation

  • Tsao, H.-S. Jacob & Lin, Da-Jie, 1999. "Spatial and Temporal Factors in Estimating the Potential of Ride-sharing for Demand Reduction," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt2p57q0c9, Institute of Transportation Studies, UC Berkeley.
    • Handle: RePEc:cdl:itsrrp:qt2p57q0c9
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    Citations

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    Cited by:

    1. Vanoutrive, Thomas & Van De Vijver, Elien & Van Malderen, Laurent & Jourquin, Bart & Thomas, Isabelle & Verhetsel, Ann & Witlox, Frank, 2012. "What determines carpooling to workplaces in Belgium: location, organisation, or promotion?," Journal of Transport Geography, Elsevier, vol. 22(C), pages 77-86.
    2. Zhang, Haoran & Chen, Jinyu & Li, Wenjing & Song, Xuan & Shibasaki, Ryosuke, 2020. "Mobile phone GPS data in urban ride-sharing: An assessment method for emission reduction potential," Applied Energy, Elsevier, vol. 269(C).
    3. Wright, Steve & Nelson, John D. & Cottrill, Caitlin D, 2020. "MaaS for the suburban market: Incorporating carpooling in the mix," Transportation Research Part A: Policy and Practice, Elsevier, vol. 131(C), pages 206-218.
    4. Agatz, N.A.H. & Erera, A. & Savelsbergh, M.W.P. & Wang, X., 2010. "The Value of Optimization in Dynamic Ride-Sharing: a Simulation Study in Metro Atlanta," ERIM Report Series Research in Management ERS-2010-034-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    5. Gonçalo Correia & José Manuel Viegas, 2010. "Applying a structured simulation-based methodology to assess carpooling time--space potential," Transportation Planning and Technology, Taylor & Francis Journals, vol. 33(6), pages 515-540, June.
    6. Matteo Mallus & Giuseppe Colistra & Luigi Atzori & Maurizio Murroni & Virginia Pilloni, 2017. "Dynamic Carpooling in Urban Areas: Design and Experimentation with a Multi-Objective Route Matching Algorith," Sustainability, MDPI, vol. 9(2), pages 1-21, February.
    7. Oren Bahat & Shlomo Bekhor, 2016. "Incorporating Ridesharing in the Static Traffic Assignment Model," Networks and Spatial Economics, Springer, vol. 16(4), pages 1125-1149, December.
    8. Gonçalo Homem de Almeida Correia & João de Abreu e Silva & José Manuel Viegas, 2013. "Using latent attitudinal variables estimated through a structural equations model for understanding carpooling propensity," Transportation Planning and Technology, Taylor & Francis Journals, vol. 36(6), pages 499-519, August.
    9. Antonella Franco & Antonino Vitetta, 2023. "Preference Model in the Context of Mobility as a Service: A Pilot Case Study," Sustainability, MDPI, vol. 15(6), pages 1-14, March.
    10. Jun Guan Neoh & Maxwell Chipulu & Alasdair Marshall, 2017. "What encourages people to carpool? An evaluation of factors with meta-analysis," Transportation, Springer, vol. 44(2), pages 423-447, March.
    11. Roozbeh Jalali & Seama Koohi-Fayegh & Khalil El-Khatib & Daniel Hoornweg & Heng Li, 2017. "Investigating the Potential of Ridesharing to Reduce Vehicle Emissions," Urban Planning, Cogitatio Press, vol. 2(2), pages 26-40.
    12. Agatz, Niels A.H. & Erera, Alan L. & Savelsbergh, Martin W.P. & Wang, Xing, 2011. "Dynamic ride-sharing: A simulation study in metro Atlanta," Transportation Research Part B: Methodological, Elsevier, vol. 45(9), pages 1450-1464.
    13. Wang, Rui, 2011. "Shaping carpool policies under rapid motorization: the case of Chinese cities," Transport Policy, Elsevier, vol. 18(4), pages 631-635, August.

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