Novel model for integrated demand-responsive transit service considering rail transit schedule

Yingjia Tan; Bo Sun; Li Guo; Binbin Jing; Yingjia Tan; Bo Sun; Li Guo; Binbin Jing

doi:10.3934/mbe.2022577

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 12: 12371-12386. doi: 10.3934/mbe.2022577

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Novel model for integrated demand-responsive transit service considering rail transit schedule

1.
Shenzhen General Integrated Transportation and Municipal Engineering Design & Research Institute Co., Ltd., Shenzhen 518003, China
2.
School of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China
3.
Shenzhen Urban Planning && Land Resource Research Center, Shenzhen 518003, China
4.
School of Transportation and Civil Engineering, Nantong University, Nantong 226000, China

Academic Editor: Wenqiang Zhang

Received: 18 April 2022 Revised: 03 August 2022 Accepted: 04 August 2022 Published: 24 August 2022

This research aims to develop an optimization model for optimizing demand-responsive transit (DRT) services. These services can not only direct passengers to reach their nearest bus stops but also transport them to connecting stops on major transit systems at selected bus stops. The proposed methodology is characterized by service time windows and selected metro schedules when passengers place a personalized travel order. In addition, synchronous transfers between shuttles and feeder buses were fully considered regarding transit problems. Aiming at optimizing the total travel time of passengers, a mixed-integer linear programming model was established, which includes vehicle ride time from pickup locations to drop-off locations and passenger wait time during transfer travels. Since this model is commonly known as an NP-hard problem, a new two-stage heuristic using the ant colony algorithm (ACO) was developed in this study to efficiently achieve the meta-optimal solution of the model within a reasonable time. Furthermore, a case study in Chongqing, China, shows that compared with conventional models, the developed model was more efficient formaking passenger, route and operation plans, and it could reduce the total travel time of passengers.
- DRT system,
- synchronous transfer of railway transit,
- stop selection,
- two-stage ant colony algorithm
Citation: Yingjia Tan, Bo Sun, Li Guo, Binbin Jing. Novel model for integrated demand-responsive transit service considering rail transit schedule[J]. Mathematical Biosciences and Engineering, 2022, 19(12): 12371-12386. doi: 10.3934/mbe.2022577

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Abstract

This research aims to develop an optimization model for optimizing demand-responsive transit (DRT) services. These services can not only direct passengers to reach their nearest bus stops but also transport them to connecting stops on major transit systems at selected bus stops. The proposed methodology is characterized by service time windows and selected metro schedules when passengers place a personalized travel order. In addition, synchronous transfers between shuttles and feeder buses were fully considered regarding transit problems. Aiming at optimizing the total travel time of passengers, a mixed-integer linear programming model was established, which includes vehicle ride time from pickup locations to drop-off locations and passenger wait time during transfer travels. Since this model is commonly known as an NP-hard problem, a new two-stage heuristic using the ant colony algorithm (ACO) was developed in this study to efficiently achieve the meta-optimal solution of the model within a reasonable time. Furthermore, a case study in Chongqing, China, shows that compared with conventional models, the developed model was more efficient formaking passenger, route and operation plans, and it could reduce the total travel time of passengers.

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