Optimal Tradable Credit Scheme Design with Recommended Credit Price

As an interesting research topic in transportation field, tradable credit scheme (TCS) has been extensively explored in the latest decade. Existing studies implicitly assumed that travelers are clear about the equilibrium credit price and make their trips accordingly. However, this may not be the case in reality, since the credit price is endogenously determined by the credit-trading behavior, especially in the early stages after the implementation of a TCS. Considering travelers’ uncertainty on the equilibrium credit price, this paper aims to investigate the impacts of perception error on credit price and how to accommodate such errors by an appropriate scheme design. Transferring the perception error on credit price to a given and fixed value released by central authority, we first investigate the impacts of recommended credit price under a given TCS. The numerical results imply that it is necessary to simultaneously consider the choice of recommended credit price and charging scheme in TCS design. Regarding this, we combine the goals of social welfare and public acceptance of the scheme and propose a bilevel biobjective programming (BLBOP) model, by which the net economic benefit is maximized while the gap between the recommended and realized credit prices is minimized. Through two numerical examples, it is found that the rise in perception variance could intensify the contradiction effect between the two objectives. Additionally, a nonnegligible price gap must be allowed to occur to maintain the effectiveness of a TCS.

Investigating the Impacts of Transaction Cost under a Tradable Credit Scheme with Heterogenous Users

In this paper, the impacts of transaction cost are investigated under a tradable credit scheme (TCS) considering user heterogeneity. Under the credit scheme, a certain number of credits are initially distributed among all the travelers for a specific O-D pair, and a link-specific number of credits are charged from travelers using that link. The scheme allows for free trading of the credits among travelers, and both the sellers and buyers need to pay an extra transaction cost, which is associated with trading volume. Travelers in the network are assumed to be heterogenous with a discrete value of time (VOT). For a given tradable credit scheme and discrete VOT set, the combined network user equilibrium (UE) and credit-trading market equilibrium (ME) are formulated as a variational inequality (VI) problem, and the conditions for the uniqueness of the network flow pattern and the credit price at equilibrium are established. A bisection-based trial-and-error method is proposed to solve the proposed VI problems. Based on the simulation results, the computational advantages of the proposed method are demonstrated. Then, an example network is presented to investigate the effect of transaction cost in different kinds of markets. It is found that the implementation of transaction cost can suppress trading volume and either elevate or drive down the equilibrium credit price. Besides, it is also found that users with the lowest VOT suffer the most from the increase in transaction cost, while those with the higher VOT are more likely to experience a reduction in travel cost with the implementation of TCS.

Traffic Equilibrium for Mixed Traffic Flows of Human-Driven Vehicles and Connected and Autonomous Vehicles in Transportation Networks under Tradable Credit Scheme

In this paper, the traffic equilibriums for mixed traffic flows of human-driven vehicles (HDV) and connected and autonomous vehicles (CAV) under a tradable credit scheme (TCS) are established and formulated as two variational inequality (VI) problems with exogenous and endogenous CAV penetration rate, respectively. A modified Lagrangian dual (MLD) method embedded with a revised Smith’s route-swapping (RSRS) algorithm is proposed to solve the problems. Based on the numerical analysis, the impacts of CAV penetration and the extra expense of using a CAV on network performance are investigated. A novel driveway management, autonomous vehicle/credit charge (AVCC) link, is put forward to improve the efficiency of TCS. Under the TCS with exogenous CAV penetration rate, a logit-based model is applied to describe the stochastic user equilibrium for mixed traffic flow. It is found that the penetration of CAV gives rise to a better network performance and it can be further improved by the deployment of AVCC link. Under the TCS with endogenous penetration rate, a nested-logit model is applied to describe travelers’ choices of vehicle types and routes. It is found that the deployment of AVCC links can slow down the decline rate of CAV penetration with increasing expense and thus ensure a lower average travel time for CAVs. In both cases, the deployment of AVCC links can stimulate credit trading and drop down its unit price.