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摘要
Current methods for time-of-day interval partition problem are mainly based on the similarity of a single parameter (passenger demand or travel time) while neglecting the combined effect of these parameters on the resulting scheme. To overcome this defect, in this paper a new method for the time-of-day interval partition is proposed based on fleet-time cost optimization using multi-source data. Firstly, the deficit function model was used to calculate the minimum fleet size required to fulfill the trip tasks of a time Subsequently, with the moving time window, the theoretical minimum fleet size in each time window during the operation period was calculated. Taking the time interval partition plan as the decision variable, the optimization model was developed with the objective of minimizing the cumulative fleet-time cost of the fleet throughout the day. The genetic algorithm was used to search the optimal time interval partition plan. Finally, the data from Route-87 bus in Guangzhou, China was used as an example for validation, and sensitivity analysis was conducted for the model parameters. The results show that compared to the existing approaches, the proposed method can better realize the matching between fleet capacity and passenger demand, and effectively reduce the fleet-time cost. Compared to traditional schemes based on either passenger flow or travel time, with this method the fleet-time cost is reduced by 25 veh?h and 45.33 veh?h, respectively.
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