Research article Special Issues

Dynamic balance between demand-and-supply of urban taxis over trajectories


  • Received: 24 August 2021 Accepted: 09 November 2021 Published: 29 November 2021
  • Urban taxi serves as an irreplaceable tool in public transportation systems. The balancing of demand-and-supply can be of significant social benefit, for which the equilibrium method for urban taxis, especially with dynamic trip demands, is not well studied yet. In this paper, we formally define the equilibrium problem and propose a coarse-grained dynamic balancing algorithm. It efficiently evaluates the trip demand distribution pattern and schedules supplies to more unbalanced regions. We first propose a density-based blocking algorithm to detect regions that are with more travel demands. A trip demand merging strategy is then proposed, which checks the correlation of trip demands to merge the trips into ones. To reduce the computation load, a lazy trip correlation strategy is devised to speed up the merging process. By calculating the defined balance factor, a scheduling algorithm is proposed to realize the trip merge and supply translocation based balancing approach. We evaluated our approach using a month of global positioning system (GPS) trajectories generated by 13,000 taxis of Shanghai. By learning the spatiotemporal distribution of historical taxi demand-and-supplies, we simulated an inflated trip demand platform. Tested on this platform with extensive experiments, the proposed approach demonstrates its effectiveness and scalability.

    Citation: Mingyang Liu, Junhao Han, Yushan Mei, Yuguang Li. Dynamic balance between demand-and-supply of urban taxis over trajectories[J]. Mathematical Biosciences and Engineering, 2022, 19(1): 1041-1057. doi: 10.3934/mbe.2022048

    Related Papers:

  • Urban taxi serves as an irreplaceable tool in public transportation systems. The balancing of demand-and-supply can be of significant social benefit, for which the equilibrium method for urban taxis, especially with dynamic trip demands, is not well studied yet. In this paper, we formally define the equilibrium problem and propose a coarse-grained dynamic balancing algorithm. It efficiently evaluates the trip demand distribution pattern and schedules supplies to more unbalanced regions. We first propose a density-based blocking algorithm to detect regions that are with more travel demands. A trip demand merging strategy is then proposed, which checks the correlation of trip demands to merge the trips into ones. To reduce the computation load, a lazy trip correlation strategy is devised to speed up the merging process. By calculating the defined balance factor, a scheduling algorithm is proposed to realize the trip merge and supply translocation based balancing approach. We evaluated our approach using a month of global positioning system (GPS) trajectories generated by 13,000 taxis of Shanghai. By learning the spatiotemporal distribution of historical taxi demand-and-supplies, we simulated an inflated trip demand platform. Tested on this platform with extensive experiments, the proposed approach demonstrates its effectiveness and scalability.



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