Proceedings of the 4th International Conference on Economic Management and Model Engineering, ICEMME 2022, November 18-20, 2022, Nanjing, China

Research Article

Matching and Market Design in Car Hailing Market

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  • @INPROCEEDINGS{10.4108/eai.18-11-2022.2327119,
        author={GuangYang  Yin},
        title={Matching and Market Design in Car Hailing Market},
        proceedings={Proceedings of the 4th International Conference on Economic Management and Model Engineering, ICEMME 2022, November 18-20, 2022, Nanjing, China},
        publisher={EAI},
        proceedings_a={ICEMME},
        year={2023},
        month={2},
        keywords={matching online car supply market design dynamic vehicle routing problem},
        doi={10.4108/eai.18-11-2022.2327119}
    }
    
  • GuangYang Yin
    Year: 2023
    Matching and Market Design in Car Hailing Market
    ICEMME
    EAI
    DOI: 10.4108/eai.18-11-2022.2327119
GuangYang Yin1,*
  • 1: Shanghai Jiao Tong University A Level International Center
*Contact email: markyin2003@icloud.com

Abstract

Online ride-hailing plays an important part in our lives. Until recently DIDI already have about 493 million active annual users. [1] As the pace of life becomes faster, people are also pursuing more efficiency. An increasing number of people are capable to use advanced network technology to call cars to achieve true peer-to-peer commuting. Making the entire taxi system more efficient is not only for passengers to save time and cost, but also for drivers to increase their productivity. In this essay, five different two-sided matching models will be compared to discuss the current research progress, development trends and shortcomings of online car-hailing platforms. Besides, three criteria will also be mentioned in this essay in order to measure effectiveness of different algorithms. After the research, it shows that batch matching and serial dispatch are two optimal choices. If these two algorithms combined with the dynamic vehicle routing problem (DVRP) may provide higher efficiency for passengers, drivers and the car-hailing platform. Further researches about DVRP and data analysis are needed to provide a solution to the problem in the online car supply market.