Spatiotemporal Analysis of Competition Between Subways and Taxis Based on Multi-Source Data

We present a sustainable multimodal transit system that integrates taxi-sharing with subways to alleviate traffic congestion and restore the cooperative relationship between taxis and subways. This study proposes a two-phase matching model based on optimization theory, in which pick-up/drop-off sequences for participants, as well as their motivation to shift to a TSS service, were considered. For the transportation system, achieving a reduction in vehicle miles is considered to be the matching objective. We tested the matching model using empirical taxi global positioning system (GPS) data for a typical morning rush hour in Beijing. The optimization model performs well for large-scale data and the optimal solution can be calculated quickly, which is ideal in a dynamic system. Furthermore, several sensitive analysis experiments were conducted to evaluate the performance of the TSS system. We found that approximately 23.13% of taxi users can be served by TSS transit, total taxi mileage can be reduced by 20.17%, and carbon dioxide emissions may be reduced by 15.16%. The proposed model and findings demonstrate that the TSS service considered here is a feasible multimodal transit mode, with the advantages of flexibility and sustainability, and has great potential for improving social benefits.

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Spatiotemporal Analysis of Competition Between Subways and Taxis Based on Multi-Source Data

Cited by 2 publications

References 46 publications

Exploring the operational performance discrepancies between online ridesplitting and carpooling transportation modes based on DiDi data

Exploring the operational performance discrepancies between online ridesplitting and carpooling transportation modes based on DiDi data

A Matching Model for Door-to-Door Multimodal Transit by Integrating Taxi-Sharing and Subways

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