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<p>This paper constructs and settles a charging facility location problem with the link capacity constraint over a mixed traffic network. The reason for studying this problem is that link capacity constraint is mostly insufficient or missing in the studies of traditional user equilibrium models, thereby resulting in the ambiguous of the definition of road traffic network status. Adding capacity constraints to the road network is a compromise to enhance the reality of the traditional equilibrium model. In this paper, we provide a two-layer model for evaluating the efficiency of the charging facilities under the condition of considering the link capacity constraint. The upper level model in the proposed bi-level model is a nonlinear integer programming formulation, which aims to maximize the captured link flows of the battery electric vehicles. Moreover, the lower level model is a typical traffic equilibrium assignment model except that it contains the link capacity constraint and driving distance constraint of the electric vehicles over the mixed road network. Based on the Frank-Wolfe algorithm, a modified algorithm framework is adopted for solving the constructed problem, and finally, a numerical example is presented to verify the proposed model and solution algorithm.</p>
</abstract>

This paper constructs and settles a charging facility location problem with the link capacity constraint over a mixed traffic network. The reason for studying this problem is that link capacity constraint is mostly insufficient or missing in the studies of traditional user equilibrium models, thereby resulting in the ambiguous of the definition of road traffic network status. Adding capacity constraints to the road network is a compromise to enhance the reality of the traditional equilibrium model. In this paper, we provide a two-layer model for evaluating the efficiency of the charging facilities under the condition of considering the link capacity constraint. The upper level model in the proposed bi-level model is a nonlinear integer programming formulation, which aims to maximize the captured link flows of the battery electric vehicles. Moreover, the lower level model is a typical traffic equilibrium assignment model except that it contains the link capacity constraint and driving distance constraint of the electric vehicles over the mixed road network. Based on the Frank-Wolfe algorithm, a modified algorithm framework is adopted for solving the constructed problem, and finally, a numerical example is presented to verify the proposed model and solution algorithm.

Flow-Capture Location Model with Link Capacity Constraint Over a Mixed Traffic Network

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This paper constructs and settles a charging facility location problem with the link capacity constraint over a mixed traffic network. The reason for studying...

Flow-Capture Location Model with Link Capacity Constraint Over a Mixed Traffic Network

Publié en ligne: 23 juil. 2022

Pages: 223 - 234

Reçu: 05 mars 2022

Accepté: 29 juin 2022

DOI: https://doi.org/10.2478/jaiscr-2022-0015

Mots clés
Traffic assignment problem, link capacity constraint, charging location, path distance constraint

© 2022 Ping Liu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Flow-Capture Location Model with Link Capacity Constraint Over a Mixed Traffic Network

Publié en ligne: 23 juil. 2022

Pages: 223 - 234

Reçu: 05 mars 2022

Accepté: 29 juin 2022

DOI: https://doi.org/10.2478/jaiscr-2022-0015

Mots clésTraffic assignment problem, link capacity constraint, charging location, path distance constraint

© 2022 Ping Liu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Traffic assignment problem, link capacity constraint, charging location, path distance constraint