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Improving Ridesplitting Service Using Optimization Procedures on Shareability Network: A Case Study of Chengdu, China

Résumé : Ridesourcing services play a crucial role in metropolitan transportation systems and aggravate urban traffic congestion and air pollution. Ridesplitting is one possible way to reduce these adverse effects and improve transport efficiency. This paper aims to explore the potential of ridesplitting in peak hours using empirical ridesourcing data of Chengdu, China provided by DiDi Chuxing. A ridesplitting trip identification algorithm based on a shareability network is developed to quantify the potential of ridesplitting. Then, we evaluate the gap between the potential and actual scales of ridesplitting, which the literature has not yet reported. We compare the potential of ridesplitting under three different objectives. The results show that the objective of minimizing the total travel cost produces better performance than the objectives of maximizing shared trips and time savings. Under the objective of maximizing cost savings, the percentage of potential cost savings is 18.47% with an average delay of 4.76 minutes, whereas the actual percentage is 1.22% with an average delay of 9.86 minutes. The potential percentage of shared trips is 90.69%, while the actual percentage is 7.85%. Furthermore, the potential time savings can reach 25.75%, while the actual time savings are 2.38% in the real world. The findings of this study can help transportation management agencies and ridesourcing companies develop sensible policies to improve ridesplitting services.
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Submitted on : Wednesday, September 9, 2020 - 3:33:38 PM
Last modification on : Tuesday, December 8, 2020 - 10:20:50 AM

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Meiting Tu, Ye Li, Wenxiang Li, Minchao Tu, Olivier Orfila, et al.. Improving Ridesplitting Service Using Optimization Procedures on Shareability Network: A Case Study of Chengdu, China. ITSC 2019, IEEE Intelligent Transportation Systems Conference, Oct 2019, Auckland, France. 6p, ⟨10.1109/ITSC.2019.8917402⟩. ⟨hal-02934694⟩

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