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A Distributed Gradient Approach for System Optimal Dynamic Traffic Assignment
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DOI：10.1109/TITS.2022.3163369.png)
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Abstract
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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.
Keywords:
Computational modeling
Computational complexity
Linear programming
Load modeling
Transportation
Loading
Heuristic algorithms
Distributed
system optimal
dynamic traffic assignment
sub-problem
decomposition
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