This paper provides a chance-constrained programming approach for transportation planning and operations under uncertainty. The major contribution of this paper is to approximate a joint chance-constrained Cell Transmission Model based System Optimum Dynamic Traffic Assignment with only partial distributional information about uncertainty as a linear program which is computationally efficient. Numerical experiments have been conducted to show the performance of the proposed approach compared with other two workable approaches based on a cumulative distribution function and a sampling method. This new approach can be used as a pragmatic tool for system optimum dynamic traffic control and management.
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Acknowledgment This work was partially supported by the grant awards CMMI-0824640 and CMMI-0900040 from the National Science Foundation and the grant awards from the Mid-Atlantic Universities Transportation Center (MAUTC).
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Artificial Intelligence