Please use this identifier to cite or link to this item: https://doi.org/10.1109/TVT.2020.2964784
Title: Using Reinforcement Learning to Minimize the Probability of Delay Occurrence in Transportation
Authors: CAO ZHIGUANG 
Guo Hongliang
Song Wen
Gao Kaizhou
Chen Zhenghua
Zhang Le
Zhang Xuexi
Issue Date: 8-Jan-2020
Publisher: IEEE
Citation: CAO ZHIGUANG, Guo Hongliang, Song Wen, Gao Kaizhou, Chen Zhenghua, Zhang Le, Zhang Xuexi (2020-01-08). Using Reinforcement Learning to Minimize the Probability of Delay Occurrence in Transportation 69 (3) : 2424 - 2436. ScholarBank@NUS Repository. https://doi.org/10.1109/TVT.2020.2964784
Rights: Attribution-NonCommercial-ShareAlike 4.0 International
Abstract: Reducing traffic delay is of crucial importance for the development of sustainable transportation systems, which is a challenging task in the studies of stochastic shortest path (SSP) problem. Existing methods based on the probability tail model to solve the SSP problem, seek for the path that minimizes the probability of delay occurrence, which is equal to maximizing the probability of reaching the destination before a deadline (i.e., arriving on time). However, they suffer from low accuracy or high computational cost. Therefore, we design a novel and practical Q-learning approach where the converged Q-values have the practical meaning as the actual probabilities of arriving on time so as to improve the accuracy of finding the real optimal path. By further adopting dynamic neural networks to learn the value function, our approach can scale well to large road networks with arbitrary deadlines. Moreover, our approach is flexible to implement in a time dependent manner, which further improves the performance of returned path. Experimental results on some road networks with real mobility data, such as Beijing, Munich and Singapore, demonstrate the significant advantages of the proposed approach over other methods.
URI: https://scholarbank.nus.edu.sg/handle/10635/167430
ISSN: 00189545
DOI: 10.1109/TVT.2020.2964784
Rights: Attribution-NonCommercial-ShareAlike 4.0 International
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