Deployment of dedicated CAV lanes and toll lanes in the mixed transportation network: A global optimization method

•Three types of lanes with different passability settings are considered in the mixed network.•The optimization problem of deploying dedicated lanes and toll lanes in the mixed transportation network is proposed.•A model is established to formulate the optimization problem based on link-node modelin...

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Veröffentlicht in:Applied mathematical modelling Jg. 153; S. 116629
Hauptverfasser: Chen, Yanyan, Qian, Zhen, Li, Tongfei, Dou, Xueping
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Inc 01.05.2026
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ISSN:0307-904X
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Abstract •Three types of lanes with different passability settings are considered in the mixed network.•The optimization problem of deploying dedicated lanes and toll lanes in the mixed transportation network is proposed.•A model is established to formulate the optimization problem based on link-node modeling methods.•A globally optimal solution algorithm is proposed to converge to a global optimum. In the future, during the long transition period in which regular human-driven vehicles and connected and autonomous vehicles coexist, conflicts between the two types of vehicles could hinder traffic efficiency. To address this challenge, the optimization problem of deploying dedicated lanes and toll lanes in the road network has been proposed. Due to the complexity and scale of the problem, much research relies on heuristic algorithms to identify the optimal solution. However, heuristic methods’ lack of stability and convergence results in only near-optimal solutions, which limits the capacity of connected and autonomous vehicles to boost the road network’s performance. Therefore, this study focuses on the optimization problem of deploying dedicated lanes and toll lanes, where the route decisions of diverse vehicle types are considered and modeled based on the user equilibrium principle. The objective is to reduce total travel time by optimizing the spatial arrangement of dedicated lanes and toll lanes within the road network, and by setting tolls for human-driven vehicles on toll lanes. By link-node modeling, the optimization problem is subsequently constructed into a mixed-integer nonlinear programming model, which circumvents the need for time-intensive path enumeration. The global optimization algorithm, along with the outer approximation method and other linearization methods, is employed to achieve the global optimum. Finally, numerical experiments conducted on the Nguyen-Dupuis network and the Sioux-Falls network illustrate the effectiveness of the proposed model and algorithm. The results show that the proposed algorithm achieves the globally optimal solution, which demonstrates a 7.51 % reduction in total travel time compared to the initial feasible deployment scheme of dedicated lanes and toll lanes. Furthermore, sensitivity analyses based on the proportion of connected and autonomous vehicles offer valuable insights for traffic planners to make informed decisions.
AbstractList •Three types of lanes with different passability settings are considered in the mixed network.•The optimization problem of deploying dedicated lanes and toll lanes in the mixed transportation network is proposed.•A model is established to formulate the optimization problem based on link-node modeling methods.•A globally optimal solution algorithm is proposed to converge to a global optimum. In the future, during the long transition period in which regular human-driven vehicles and connected and autonomous vehicles coexist, conflicts between the two types of vehicles could hinder traffic efficiency. To address this challenge, the optimization problem of deploying dedicated lanes and toll lanes in the road network has been proposed. Due to the complexity and scale of the problem, much research relies on heuristic algorithms to identify the optimal solution. However, heuristic methods’ lack of stability and convergence results in only near-optimal solutions, which limits the capacity of connected and autonomous vehicles to boost the road network’s performance. Therefore, this study focuses on the optimization problem of deploying dedicated lanes and toll lanes, where the route decisions of diverse vehicle types are considered and modeled based on the user equilibrium principle. The objective is to reduce total travel time by optimizing the spatial arrangement of dedicated lanes and toll lanes within the road network, and by setting tolls for human-driven vehicles on toll lanes. By link-node modeling, the optimization problem is subsequently constructed into a mixed-integer nonlinear programming model, which circumvents the need for time-intensive path enumeration. The global optimization algorithm, along with the outer approximation method and other linearization methods, is employed to achieve the global optimum. Finally, numerical experiments conducted on the Nguyen-Dupuis network and the Sioux-Falls network illustrate the effectiveness of the proposed model and algorithm. The results show that the proposed algorithm achieves the globally optimal solution, which demonstrates a 7.51 % reduction in total travel time compared to the initial feasible deployment scheme of dedicated lanes and toll lanes. Furthermore, sensitivity analyses based on the proportion of connected and autonomous vehicles offer valuable insights for traffic planners to make informed decisions.
ArticleNumber 116629
Author Qian, Zhen
Li, Tongfei
Dou, Xueping
Chen, Yanyan
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Keywords Globally optimal solution algorithm
Lane deployment scheme
Traffic planning
Traffic management
Mixed traffic
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Snippet •Three types of lanes with different passability settings are considered in the mixed network.•The optimization problem of deploying dedicated lanes and toll...
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StartPage 116629
SubjectTerms Globally optimal solution algorithm
Lane deployment scheme
Mixed traffic
Traffic management
Traffic planning
Title Deployment of dedicated CAV lanes and toll lanes in the mixed transportation network: A global optimization method
URI https://dx.doi.org/10.1016/j.apm.2025.116629
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