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Tarun Rambha, Stephen
D. Boyles, Avinash Unnikrishnan, and Peter Stone. **Marginal Cost
Pricing for System Optimal Traffic Assignment with Recourse under Supply-Side Uncertainty**. *Transportation Research
Part B: Methodological*, 110:104–21, 2018.

Official version from Publisher's
Webpage

Transportation networks are often subject to fluctuations in supply-side parameters such as capacity and free-flow travel time due to factors such as incidents, poor weather, and bottlenecks. In such scenarios, assuming that network arcs exist in a finite number of states with different delay functions with different probabilities, a marginal cost pricing scheme that leads to a socially optimal outcome is proposed. The suggested framework makes the behavioral assumption that travelers do not just choose paths but follow routing policies that respond to en route information. Specifically, it is assumed that travelers are fully- rational and that they compute the optimal online shortest path assuming full-reset. However, such policies may involve cycling, which is unrealistic in practice. Hence, a network transformation that helps restrict cycles up to a certain length is devised and the problem is reformulated as a convex optimization problem with symmetric delay functions. The results of numerical tests on the Sioux Falls test network are presented using the Frank-Wolfe algorithm.

@article{TRB-18, title="Marginal Cost Pricing for System Optimal Traffic Assignment with Recourse under Supply-Side Uncertainty", JOURNAL={Transportation Research Part B: Methodological}, year="2018", volume="110", pages="104-21", issn = "0191-2615", doi = "https://doi.org/10.1016/j.trb.2018.02.008", url = "https://www.sciencedirect.com/science/article/pii/S0191261516301540", author="Tarun Rambha and Stephen D.\ Boyles and Avinash Unnikrishnan and Peter Stone", abstract = {Transportation networks are often subject to fluctuations in supply-side parameters such as capacity and free-flow travel time due to factors such as incidents, poor weather, and bottlenecks. In such scenarios, assuming that network arcs exist in a finite number of states with different delay functions with different probabilities, a marginal cost pricing scheme that leads to a socially optimal outcome is proposed. The suggested framework makes the behavioral assumption that travelers do not just choose paths but follow routing policies that respond to en route information. Specifically, it is assumed that travelers are fully- rational and that they compute the optimal online shortest path assuming full-reset. However, such policies may involve cycling, which is unrealistic in practice. Hence, a network transformation that helps restrict cycles up to a certain length is devised and the problem is reformulated as a convex optimization problem with symmetric delay functions. The results of numerical tests on the Sioux Falls test network are presented using the Frank-Wolfe algorithm. }, wwwnote = {Official version from <a href="https://www.sciencedirect.com/science/article/pii/S0191261516301540">Publisher's Webpage</a>}, }

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