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Published on: January 20, 2023
Novel model for integrated demand-responsive transit service considering rail transit schedule.
Yingjia Tan1, Bo Sun2, Li Guo3
1Shenzhen General Integrated Transportation and Municipal Engineering Design & Research Institute Co., Ltd., Shenzhen 518003, China.
This study introduces an optimized model for demand-responsive transit (DRT) services, improving passenger travel time. The ant colony algorithm (ACO) efficiently solves complex transit optimization problems.
Area of Science:
- Operations Research
- Transportation Engineering
- Computer Science
Background:
- Demand-responsive transit (DRT) services face challenges in optimizing passenger journeys and transfers.
- Integrating DRT with major transit systems requires sophisticated planning models.
Purpose of the Study:
- To develop an optimization model for demand-responsive transit (DRT) services.
- To minimize total passenger travel time, including ride and transfer wait times.
- To enhance coordination between DRT, feeder buses, and metro schedules.
Main Methods:
- A mixed-integer linear programming model was formulated to optimize DRT operations.
- A two-stage heuristic using the ant colony algorithm (ACO) was developed to solve the NP-hard optimization problem.
- The model incorporates service time windows and synchronized transfers.
Main Results:
- The developed optimization model demonstrated higher efficiency in passenger, route, and operation planning compared to conventional methods.
- A case study in Chongqing, China, validated the model's effectiveness.
- The model successfully reduced overall passenger travel time.
Conclusions:
- The proposed optimization model and ACO-based heuristic provide an efficient solution for DRT services.
- This approach enhances the integration of DRT with public transit networks, improving user experience.
- The findings offer practical implications for urban transportation planning and operations.
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