Flexible Bus Route Optimization for Multitarget Stations

This paper proposes a flexible bus route optimization model for efficient public city transportation systems based on multitarget stations. The model considers passenger demands, vehicle capacities, and transportation network and aims to solve the optimal route, minimizing the vehicles’ running time...

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Bibliographic Details
Published in:Mathematical problems in engineering 2020, Vol.2020 (2020), p.1-8
Main Authors: Pan-yi, Wei, Yan-yan, Chen, Huang, Jianling, Ji-yang, Sun, Jian-lin, Jia
Format: Article
Language:English
Subjects:
Algorithms
Boundary conditions
Buses (vehicles)
Computer simulation
Heuristic
Heuristic methods
Integer programming
Mathematical problems
Operating costs
Optimization
Passengers
Quality of service
Route optimization
Run time (computers)
Scheduling
Shuttles
Stations
Transportation services
Transportation systems
Travel
Travel time
Urban transportation
Variables
Vehicles
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Summary:This paper proposes a flexible bus route optimization model for efficient public city transportation systems based on multitarget stations. The model considers passenger demands, vehicle capacities, and transportation network and aims to solve the optimal route, minimizing the vehicles’ running time and the passengers’ travel time. A heuristic algorithm based on a gravity model is introduced to solve this NP-hard optimization problem. Simulation studies verify the effectiveness and practicality of the proposed model and algorithm. The results show that the total number of vehicles needed to complete the service is 17–21, the average travel time of each vehicle is 24.59 minutes, the solving time of 100 sets of data is within 25 seconds, and the average calculation time is 12.04 seconds. It can be seen that under the premise of real-time adjustment of connection planning time, the optimization model can satisfy the passenger’s dynamic demand to a greater extent, and effectively reduce the planning path error, shorten the distance and travel time of passengers, and the result is better than that of the flexible bus scheduling model which ignores the change of connection travel time.
ISSN:1024-123X
1563-5147
DOI:10.1155/2020/7183465