An ε-Constraint Multi-objective Algorithm for Transit Route Design with Subsidy Consideration

Author(s):  
Li-Wen Chen ◽  
Ta-Yin Hu ◽  
Le-Chi Shih ◽  
Tsai-Yun Liao
2015 ◽  
Vol 50 (4) ◽  
pp. 507-521 ◽  
Author(s):  
Xiaolin Lu ◽  
Jie Yu ◽  
Xianfeng Yang ◽  
Shuliang Pan ◽  
Nan Zou

Author(s):  
Gyugeun Yoon ◽  
Joseph Y. J. Chow

While public transit network design has a wide literature, the study of line planning and route generation under uncertainty is not so well covered. Such uncertainty is present in planning for emerging transit technologies or operating models in which demand data is largely unavailable to make predictions on. In such circumstances, this paper proposes a sequential route generation process in which an operator periodically expands the route set and receives ridership feedback. Using this sensor loop, a reinforcement learning-based route generation methodology is proposed to support line planning for emerging technologies. The method makes use of contextual bandit problems to explore different routes to invest in while optimizing the operating cost or demand served. Two experiments are conducted. They (1) prove that the algorithm is better than random choice; and (2) show good performance with a gap of 3.7% relative to a heuristic solution to an oracle policy.


2019 ◽  
Vol 274 (2) ◽  
pp. 545-559 ◽  
Author(s):  
Leena Ahmed ◽  
Christine Mumford ◽  
Ahmed Kheiri

Author(s):  
David Rey ◽  
Khaled Almi'ani ◽  
Anastasios Viglas ◽  
Lavy Libman ◽  
S. Travis Waller

2014 ◽  
Vol 1030-1032 ◽  
pp. 2166-2169
Author(s):  
Xiao Wen Liu

Most high-speed rail stations are generally built in the periphery of the city. Therefore, it is necessary for the government to build new transit routes to meet the traffic demand in the emerging city unit around the high-speed rail stations. This paper formulates a model based on genetic algorithms (GA) to design transit route considering various evaluation standards. Numerical example is set up to illustrate the model and algorithm.


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