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Taxi Hailing Choice Behavior and Economic Benefit Analysis of Emission Reduction Based on Multi-mode Travel Big Data

In: Logic-Driven Traffic Big Data Analytics

Author

Listed:
  • Shaopeng Zhong

    (Dalian University of Technology
    Southwest Jiaotong University)

  • Daniel (Jian) Sun

    (Chang’an University
    Shanghai Jiao Tong University)

Abstract

During the passing decade, taxi floating car data (FCD) has become an important tool to investigate urban trip choice behaviors and activities. The corresponding taxi exhaust reduction issue is also with rather significance for traffic emission mitigation in urban areas. By taking Shanghai as an empirical case, this chapter analyzed the spatiotemporal characteristics of multimode travelers by combining the taxi FCD (from Qiangsheng Inc.), the metro smartcard data and the GPS trajectories of Mobike, one of the most popular shared bicycles in China, 2018. Binomial logit models (BNL) were proposed to estimate mode choices for both peak and off-peak periods by incorporating socio-economic, demographic, urban morphology, land use properties, and various trip-related variables. The choices between metro and taxi, Mobike and taxi were analyzed, respectively, with the corresponding influential factors identified. The results indicated that the percentage of residential and commercial land uses, the number of educational facilities have significant impacts on travel mode choice during peak hours, while the percentage of commercial land, the number of hospitals and bus lines are more prominent during off-peak periods. To quantify the emission reduction benefits, localized calculation of automobile exhaust was established according to the Vehicle Specific Power (VSP) based measurements obtained from the Portable Emission Measurement System (PEMS) experiments. Then, five corresponding emission mitigation schemes were proposed based on the model findings, and the cost–benefit of each countermeasure was further analyzed. Comparing with releasing the peak-hour crowdedness of metro stations, increasing Mobike supply, updating taxis into electric vehicles, and equipping taxis with catalytic converters, the scheme of removing non-motor vehicle restrictions was found with the shortest payback period and was consequently recommended as accordance with the proposal of urban eco and non-motorized transportation. Findings of this study is useful for transportation management in improving the mode share of metro and bicycles, thus to alleviate the congestion and auto emissions in urban areas.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:sprchp:978-981-16-8016-8_11
    DOI: 10.1007/978-981-16-8016-8_11
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    Citations

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    Cited by:

    1. Lixin Yan & Bowen Sheng & Yi He & Shan Lu & Junhua Guo, 2022. "Forecasting and Planning Method for Taxi Travel Combining Carbon Emission and Revenue Factors—A Case Study in China," IJERPH, MDPI, vol. 19(18), pages 1-20, September.
    2. 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.
    3. Tang, Tianli & Gu, Ziyuan & Yang, Yuanxuan & Sun, Haobo & Chen, Siyuan & Chen, Yuting, 2024. "A data-driven framework for natural feature profile of public transport ridership: Insights from Suzhou and Lianyungang, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 183(C).

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