Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
2-2017
Abstract
The stochastic shortest path problem is of crucial importance for the development of sustainable transportation systems. Existing methods based on the probability tail model seek for the path that maximizes the probability of arriving at the destination before a deadline. However, they suffer from low accuracy and/or high computational cost. We design a novel Q-learning method where the converged Q-values have the practical meaning as the actual probabilities of arriving on time so as to improve accuracy. By further adopting dynamic neural networks to learn the value function, our method can scale well to large road networks with arbitrary deadlines. Experimental results on real road networks demonstrate the significant advantages of our method over other counterparts.
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 31st AAAI Conference on Artificial Intelligence, California, United States of America, 2017 Feb 4-9
First Page
4481
Last Page
4487
Identifier
10.1609/aaai.v31i1.11170
Publisher
AAAI press
City or Country
Washington
Citation
CAO, Zhiguang; GUO, Hongliang; ZHANG, Jie; OLIEHOEK, Frans; and FASTENRATH, Ulrich.
Maximizing the probability of arriving on time: A practical q-learning method. (2017). Proceedings of the 31st AAAI Conference on Artificial Intelligence, California, United States of America, 2017 Feb 4-9. 4481-4487.
Available at: https://ink.library.smu.edu.sg/sis_research/8131
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.1609/aaai.v31i1.11170