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

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Abstract

With the popularization of Internet technologies and shared mobility services, online ridesharing has developed rapidly in numerous cities worldwide. However, perhaps owing to the lack of empirical data, there is a lack of comprehensive and comparative studies on the two major online ridesharing modes, namely, ridesplitting and carpooling, vis-à-vis operational performance discrepancies. Thus, we conduct an empirical study using the massive amount of actual operating data provided by DiDi Chuxing. Based on an analysis of the operating characteristics of ridesplitting and carpooling, this study proposes an approach to estimate ridesharing fuel-saving and distance-saving performance by combining the vehicle operating information and fuel economy indicators of various transportation modes. Furthermore, the operational performance discrepancies between the two major ridesharing modes are compared through an analysis of the user characteristics and interactive effects between ridesharing and subway systems. The results show that the average fuel-saving and distance-saving ratios of ridesplitting are lower than those of carpooling. From the perspective of the transportation system’s fuel economy, ridesharing is not considered to be fuel-saving, and its scale should be reasonably regulated. According to driver classification, carpooling is more suitable for commuting and intercity transportation. In addition, ridesplitting and carpooling can be employed as feeders into subway networks in suburban areas. These findings are believed likely to be beneficial for facilitating the sustainable and standardized development of these two ridesharing modes.

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Acknowledgements

This work is financially supported by National Natural Science Foundation of China (91746201, 71621001). Many thanks to the Institute of Policy Studies, DiDi Company for providing the data in this study.

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Authors and Affiliations

  1. MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, School of Traffic and Transportation, Beijing Jiaotong University, Beijing, 100044, People’s Republic of China

    Haoran Chen, Xuedong Yan & Xiaobing Liu

  2. Department of Civil, Geo and Environmental Engineering, Technical University of Munich, Arcisstrasse 21, 80333, Munich, Germany

    Tao Ma

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  1. Haoran Chen
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  2. Xuedong Yan
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  3. Xiaobing Liu
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  4. Tao Ma
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Correspondence to Xuedong Yan.

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Chen, H., Yan, X., Liu, X. et al. Exploring the operational performance discrepancies between online ridesplitting and carpooling transportation modes based on DiDi data. Transportation 50, 1923–1958 (2023). https://doi.org/10.1007/s11116-022-10297-6

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