2022 journal article
A Distributed Gradient Approach for System Optimal Dynamic Traffic Assignment
IEEE Transactions on Intelligent Transportation Systems.
author keywords: Computational modeling; Computational complexity; Linear programming; Load modeling; Transportation; Loading; Heuristic algorithms; Distributed; system optimal; dynamic traffic assignment; sub-problem; decomposition
TL;DR:
This study presents a distributed gradient-based approach to solve system optimal dynamic traffic assignment (SODTA) formulated based on the cell transmission model that distributes SODTA into local sub-problems, who find optimal values for their decision variables within an intersection.
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Added: April 21, 2022
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.