2022-05-05 ノースカロライナ州立大学(NCState)
Photo credit: Ryoji Iwata.
＜関連情報＞
- https://news.ncsu.edu/2022/05/more-efficient-traffic-models/
- https://ieeexplore.ieee.org/document/9761073
システム最適化動的トラフィックアサインメントのための分散勾配アプローチ A Distributed Gradient Approach for System Optimal Dynamic Traffic Assignment
Mehrzad Mehrabipour,Ali Hajbabaie
IEEE Transactions on Intelligent Transportation Systems Published: 20 April 2022
DOI: 10.1109/TITS.2022.3163369
Abstract
This study presents a distributed gradient-based approach to solve system optimal dynamic traffic assignment (SODTA) formulated based on the cell transmission model. The algorithm distributes SODTA into local sub-problems, who find optimal values for their decision variables within an intersection. Each sub-problem communicates with its immediate neighbors to reach a consensus on the values of common decision variables. A sub-problem receives proposed values for common decision variables from all adjacent sub-problems and incorporates them into its own offered values by weighted averaging and enforcing a gradient step to minimize its objective function. Then, the updated values are projected onto the feasible region of the sub-problems. The algorithm finds high quality solutions in all tested scenarios with a finite number of iterations. The algorithm is tested on a case study network under different demand levels and finds solutions with at most a 5% optimality gap.
