Pricing of Connected and Autonomous Vehicles in Mixed-Traffic Networks

Abstract

For networks with human-driven vehicles (HDVs) only, pricing with arc-specific tolls has been proposed to achieve minimization of travel times in a decentralized way. However, the policy is hardly feasible from a technical viewpoint without connectivity. Therefore, for networks with mixed traffic of HDVs and connected and autonomous vehicles (CAVs), this paper considers pricing in a scenario where only CAVs are charged. In contrast to HDVs, CAVs can be managed as individual vehicles or as a fleet. In the latter case, CAVs can be routed to minimize the travel time of the fleet of CAVs or that of the entire fleet of HDVs and CAVs. We have a selfish user behavior in the first case, a private monopolist behavior in the second, a social planner behavior in the third. Pricing achieves in a decentralized way the social planner optimum. Tolls are not unique and can take both positive and negative values. Marginal cost pricing is one solution. The valid toll set is provided, and tolls are then computed according to two schemes: one with positive tolls only and minimum toll expenditure, and one with both tolls and subsidies and zero net expenditure. Convergent algorithms are used for the mixed-behavior equilibrium (simplicial decomposition algorithm) and toll determination (cutting plane algorithm). The computational experience with three networks: a two-arc network representative of the classic town bypass case, the Nguyen-Dupuis network, and the Anaheim network, provides useful policy insight.

Nash Equilibrium and Stackelberg Approach for Traffic Flow Optimization in Road Transportation Networks—A Case Study of Warsaw

“…The model proposed by Xu et al [68] achieved shorter travel times. The approaches considered in the articles [69][70][71] usurped significantly higher travel times for the whole network due to additional restrictions introduced by the authors. In contrast, the decision support model for infrastructure investment issues proposed by Lin [72], in the absence of any investment, was less effective in optimising travel times.…”

“…In contrast, the decision support model for infrastructure investment issues proposed by Lin [72], in the absence of any investment, was less effective in optimising travel times. EC-TEP model with environmental constrains 194,025 [69] Multi-class multibehaviour equilibrium ~240,000 [70] Generalized Nash equilibrium problem ~180,000 [71] Bush-Mosteller reinforcement learning ~(75,000-91,000) [72] Bi-level model for investment decision making Table 13 compares the Nash equilibrium and the Stackelberg approach in terms of objective function, constraints and conditions, advantages and disadvantages. The Nash equilibrium aims at minimizing travel time for individual users.…”

Differentiable Bilevel Programming for Stackelberg Congestion Games

“…With CAVs it is possible to charge HDVs and CAVs a different toll even when they share the same road facility, a practice known as discriminatory pricing. Several recent studies (Mehr and Horowitz, 2019;Lazar et al, 2019;Delle Site, 2021) examined the toll design problem in this context.…”