Optimization of the departure time and fare of new demand responsive transit systems

HE Yunzhu, JIA Peng, LI Haijiang, CAO Yuge

Systems Engineering - Theory & Practice ›› 2022, Vol. 42 ›› Issue (4) : 1060-1071.

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Systems Engineering - Theory & Practice ›› 2022, Vol. 42 ›› Issue (4) : 1060-1071. DOI: 10.12011/SETP2021-1149

Optimization of the departure time and fare of new demand responsive transit systems

  • HE Yunzhu1,2, JIA Peng1,3, LI Haijiang1,2, CAO Yuge2
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Abstract

In order to solve the problems of passengers' long waiting time, poor travel convenience, low bus load rate, and difficulty in recovering the input costs of bus operators in new urban areas and urban fringe areas, a demand responsive transit operation model considering the waiting time of reserved passengers is proposed. This mode performs differential pricing of bus fares based on the principle that the fare decreases as the waiting time of reserved passengers increases, and updates the bus departure time in real time based on changes in passenger flow, so as to shorten the waiting time of passengers and increase the bus load rate, thereby improving passenger satisfaction and increasing bus operators' revenue. In this paper, a mixed integer programming model is constructed to solve the departure schedule and bus fare in the new mode, and a genetic algorithm for model solving is designed. After that, a case analysis was carried out based on the actual bus lines and OD data of Meishan New District, Ningbo City. By comparing the calculation results with the current bus departure time, bus fare, waiting time and travel costs of travelers, and bus operator revenue, the advantages of the new model are verified.

Key words

urban public transit / demand responsive transit system / departure time / willingness to pay / balance of supply and demand

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HE Yunzhu , JIA Peng , LI Haijiang , CAO Yuge. Optimization of the departure time and fare of new demand responsive transit systems. Systems Engineering - Theory & Practice, 2022, 42(4): 1060-1071 https://doi.org/10.12011/SETP2021-1149

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Funding

National Key Research and Development Program of China (2019YFB1600400); National Natural Science Foundation of China (72174035); China Postdoctoral Science Foundation (2015M580128); Liaoning Xingliao Talents Plan (XLYC2008030)
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