Traffic congestion constitutes a major problem in urban areas. Trucks contribute to congestion and have a negative impact on the environment due to their size, slower dynamics and higher fuel consumption. The individual routing decisions made by truck drivers do not lead to system optimum operations and contribute to traffic imbalances especially in places where the volume of trucks is relatively high. In this paper, we design a coordination mechanism for truck drivers that uses pricing-and-routing schemes that can help alleviate traffic congestion in a general transportation network. We consider the user heterogeneity in Value-Of-Time (VOT) by adopting a multi-class model with stochastic Origin–Destination (OD) demands for the truck drivers. The main characteristic of the mechanism is that the coordinator asks the truck drivers to declare their desired OD pair and pick their individual VOT from a set of N available options, and guarantees that the resulting pricing-and-routing scheme is Pareto-improving, i.e. every truck driver will be better-off compared to the User Equilibrium (UE) and that every truck driver will have an incentive to truthfully declare his/her VOT, while leading to a revenue-neutral (budget balanced) on average mechanism. This approach enables us to design personalized (VOT-based) pricing-and-routing schemes. We show that the Optimum Pricing Scheme (OPS) can be calculated by solving a nonconvex optimization problem. To improve computational efficiency, we propose an Approximately Optimum Pricing Scheme (AOPS) and prove that it satisfies the aforementioned properties. Both pricing-and-routing schemes are compared to the Congestion Pricing with Uniform Revenue Refunding (CPURR) scheme through extensive simulation experiments where it is shown that OPS and AOPS achieve a much lower expected total travel time and expected total monetary cost for the users compared to the CPURR scheme, without negatively affecting the rest of the network. These results demonstrate the efficiency of personalized (VOT-based) pricing-and-routing schemes.

交通拥堵是城市地区的主要问题。卡车由于尺寸大，动力变慢和油耗高而导致拥堵，并对环境产生不利影响。卡车司机做出的单个路线选择决定不会导致系统最佳运行，并且会导致交通不平衡，尤其是在卡车数量相对较高的地方。在本文中，我们设计了一种卡车司机的协调机制，该机制使用了定价和路由方案，可以帮助缓解一般运输网络中的交通拥堵情况。我们通过采用卡车司机的原产地-目的地（OD）需求随机的多类模型，来考虑用户在时间价值（VOT）中的异质性。ñ可用的选项，并保证由此产生的价格和路线选择方案能够改善帕累托，即与“用户平衡”（UE）相比，每位卡车驾驶员的境况都更好，并且每位卡车驾驶员将有动力如实声明他/她的VOT，同时导致平均机制的收入中立（预算平衡）。这种方法使我们能够设计个性化（基于VOT的）定价和路由方案。我们表明，可以通过解决非凸优化问题来计算最优定价方案（OPS）。为了提高计算效率，我们提出了近似最佳定价方案（AOPS）并证明其满足上述特性。通过广泛的模拟实验，将定价和路由方案与具有统一收入退款的拥挤定价方案（CPURR）进行了比较，结果表明，与用户相比，OPS和AOPS的预期总旅行时间和预期总货币成本要低得多对CPURR方案的支持，而不会对网络的其余部分产生负面影响。这些结果证明了个性化（基于VOT）定价和路由方案的效率。