Abstract
The popularity of DiDi, Uber and other online car-hailing service apps has had an impact on the modes of residents’ travel and has a strong influence on the taxi market. This paper builds the Agent modeling based on time-sharing pricing and studies the choice of travel decisions for complex individuals and their changes in the share of different modes of travel in transportation systems. It takes Beijing actual data as an example to analyze the sharing rate of peak and off-peak periods under the shared economic environment. The simulation results show that the taxi time-sharing pricing strategy uses price levers to achieve peak shaving and valley filling, which is conducive to improving the imbalance between supply and demand of taxis and the problem of high no-load rates during off-peak hours. In addition, it improves the utilization rate of resources and balance the sharing rate of different modes of travel. Finally, it realizes the optimal configuration of public transportation system.
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