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A Framework for Assessing Resilience in Urban Mobility: Incorporating Impact of Ridesharing

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  • Dawei Li

    (Jiangsu Key Laboratory of Urban ITS, Southeast University, 2 Dongnandaxue Rd, Nanjing 211189, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, 2 Dongnandaxue Rd, Nanjing 211189, China
    School of Transportation, Southeast University, 2 Dongnandaxue Rd, Nanjing 211189, China)

  • Yiping Liu

    (Jiangsu Key Laboratory of Urban ITS, Southeast University, 2 Dongnandaxue Rd, Nanjing 211189, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, 2 Dongnandaxue Rd, Nanjing 211189, China
    School of Transportation, Southeast University, 2 Dongnandaxue Rd, Nanjing 211189, China)

  • Yuchen Song

    (Jiangsu Key Laboratory of Urban ITS, Southeast University, 2 Dongnandaxue Rd, Nanjing 211189, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, 2 Dongnandaxue Rd, Nanjing 211189, China
    School of Transportation, Southeast University, 2 Dongnandaxue Rd, Nanjing 211189, China)

  • Zhenghao Ye

    (Jiangsu Key Laboratory of Urban ITS, Southeast University, 2 Dongnandaxue Rd, Nanjing 211189, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, 2 Dongnandaxue Rd, Nanjing 211189, China
    School of Transportation, Southeast University, 2 Dongnandaxue Rd, Nanjing 211189, China)

  • Dongjie Liu

    (Jiangsu Key Laboratory of Urban ITS, Southeast University, 2 Dongnandaxue Rd, Nanjing 211189, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, 2 Dongnandaxue Rd, Nanjing 211189, China
    School of Transportation, Southeast University, 2 Dongnandaxue Rd, Nanjing 211189, China)

Abstract

To a certain degree, the resilience of the transportation system expresses the safety of the transportation system, because it reflects the ability of the system to maintain its function in the face of disturbance events. In the current research, the assessment of the resilience of urban mobility is attractive and challenging. Apart from this, the concept of green mobility has been popular in recent years. As a representative way of shared mobility, the implementation of ridesharing will affect the level of urban mobility resilience to a certain extent. In this paper, we use a data low-intensity method to evaluate the urban traffic resilience under the circumstance of restricted car use. In addition, we incorporate the impact of ridesharing services. The research in this paper can be regarded as an evaluation framework, which can help policy makers and relevant operators to grasp the overall resilience characteristics of cities in emergencies, identify weak sectors, and formulate the best response plan. This method has been successfully applied to two cities in China, demonstrating its potential for practice. Finally, we also explored the relationship between urban traffic resilience and the pattern of population distribution. The analysis shows that population density has an impact on the level of transportation resilience. And the incorporation of ridesharing will bring an obvious increment in resilience of most areas.

Suggested Citation

  • Dawei Li & Yiping Liu & Yuchen Song & Zhenghao Ye & Dongjie Liu, 2022. "A Framework for Assessing Resilience in Urban Mobility: Incorporating Impact of Ridesharing," IJERPH, MDPI, vol. 19(17), pages 1-20, August.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:17:p:10801-:d:902088
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    References listed on IDEAS

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    1. Chaisak Suwansirikul & Terry L. Friesz & Roger L. Tobin, 1987. "Equilibrium Decomposed Optimization: A Heuristic for the Continuous Equilibrium Network Design Problem," Transportation Science, INFORMS, vol. 21(4), pages 254-263, November.
    2. Suresh Malodia & Harish Singla, 2016. "A study of carpooling behaviour using a stated preference web survey in selected cities of India," Transportation Planning and Technology, Taylor & Francis Journals, vol. 39(5), pages 538-550, July.
    3. Xu, Xiangdong & Qu, Kai & Chen, Anthony & Yang, Chao, 2021. "A new day-to-day dynamic network vulnerability analysis approach with Weibit-based route adjustment process," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    4. Watling, David, 1999. "Stability of the stochastic equilibrium assignment problem: a dynamical systems approach," Transportation Research Part B: Methodological, Elsevier, vol. 33(4), pages 281-312, May.
    5. Cai, Hua & Wang, Xi & Adriaens, Peter & Xu, Ming, 2019. "Environmental benefits of taxi ride sharing in Beijing," Energy, Elsevier, vol. 174(C), pages 503-508.
    6. Zhang, Tong & Burke, Paul J., 2020. "The effect of fuel prices on traffic flows: Evidence from New South Wales," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 502-522.
    7. Len Fisher, 2015. "More than 70 ways to show resilience," Nature, Nature, vol. 518(7537), pages 35-35, February.
    8. Tonn, Gina & Reilly, Allison & Czajkowski, Jeffrey & Ghaedi, Hamed & Kunreuther, Howard, 2021. "U.S. transportation infrastructure resilience: Influences of insurance, incentives, and public assistance," Transport Policy, Elsevier, vol. 100(C), pages 108-119.
    9. Mattioli, Giulio & Philips, Ian & Anable, Jillian & Chatterton, Tim, 2019. "Vulnerability to motor fuel price increases: Socio-spatial patterns in England," Journal of Transport Geography, Elsevier, vol. 78(C), pages 98-114.
    10. Di, Xuan & Ma, Rui & Liu, Henry X. & Ban, Xuegang (Jeff), 2018. "A link-node reformulation of ridesharing user equilibrium with network design," Transportation Research Part B: Methodological, Elsevier, vol. 112(C), pages 230-255.
    11. Ma, Jie & Meng, Qiang & Cheng, Lin & Liu, Zhiyuan, 2022. "General stochastic ridesharing user equilibrium problem with elastic demand," Transportation Research Part B: Methodological, Elsevier, vol. 162(C), pages 162-194.
    12. Cyril Veve & Nicolas Chiabaut, 2020. "Estimation of the shared mobility demand based on the daily regularity of the urban mobility and the similarity of individual trips," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-15, September.
    13. Gu, Yu & Fu, Xiao & Liu, Zhiyuan & Xu, Xiangdong & Chen, Anthony, 2020. "Performance of transportation network under perturbations: Reliability, vulnerability, and resilience," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    14. Shaopeng Zhong & Daniel (Jian) Sun, 2022. "Logic-Driven Traffic Big Data Analytics," Springer Books, Springer, number 978-981-16-8016-8, December.
    15. Ma, Jie & Xu, Min & Meng, Qiang & Cheng, Lin, 2020. "Ridesharing user equilibrium problem under OD-based surge pricing strategy," Transportation Research Part B: Methodological, Elsevier, vol. 134(C), pages 1-24.
    16. Shaopeng Zhong & Daniel (Jian) Sun, 2022. "Taxi Hailing Choice Behavior and Economic Benefit Analysis of Emission Reduction Based on Multi-mode Travel Big Data," Springer Books, in: Logic-Driven Traffic Big Data Analytics, chapter 0, pages 227-254, Springer.
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