Abstract
This paper studies the problem of estimation and computation of reliable least-time paths in stochastic time-varying (STV) networks with spatio-temporal dependencies. For a given desired confidence level a, the least-time paths from any origin to a given destination node are to be found over a desired planning horizon. In STV networks, least-time path finding approaches aim to incorporate an element of reliability to help travelers better plan their trips to prepare for the risk of arriving later or traveling for longer than desired. A label-correcting algorithm that incorporates time-dependence of the travel time distributions is proposed. The algorithm incorporates a Monte Carlo sampling approach for a path travel time estimation with time-dependence, which can also be used as an approximate solution method with spatial link travel-time correlations. Numerical results on the large-scale Chicago network are provided to test for the performance of the algorithms and the robustness of solutions. The trade-off between accuracy and efficiency of the approximate solution method compared to a Monte Carlo simulation-based approach is discussed and evaluated.
| Original language | English (US) |
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| Title of host publication | 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781728141497 |
| DOIs | |
| State | Published - Sep 20 2020 |
| Event | 23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020 - Rhodes, Greece Duration: Sep 20 2020 → Sep 23 2020 |
Publication series
| Name | 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020 |
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Conference
| Conference | 23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020 |
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| Country/Territory | Greece |
| City | Rhodes |
| Period | 9/20/20 → 9/23/20 |
Funding
This paper is based on work funded by the Northwestern University Transportation Center, motivated by research conducted as part of a project funded by the Federal Highway Administration, US Department of Transportation, in collaboration with Leidos, Inc. The authors are grateful to Doug Laird of FHWA and David Hale at Leidos for their role on the related project. The authors remain fully responsible for all content of the paper, which may not necessarily reflect the views of the sponsoring agencies.
ASJC Scopus subject areas
- Artificial Intelligence
- Decision Sciences (miscellaneous)
- Information Systems and Management
- Modeling and Simulation
- Education