Personalized Pareto-Improving Pricing Schemes with Truthfulness Guarantees: An Alternative Approach to Congestion Pricing
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We design a coordination mechanism for truck drivers that uses pricing schemes to 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. A basic characteristic of the mechanism is that the coordinator asks the truck drivers to declare their desired OD pair, as well as their individual VOT from a set of N available options, and guarantees that the resulting pricing 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. We show that the Optimum Pricing Scheme (OPS) can be calculated by solving a nonconvex optimization problem. To achieve computational efficiency, we additionally propose an Approximately Optimum Pricing Scheme (AOPS) and we prove that it satisfies the aforementioned characteristics. Both pricing schemes are compared to the Congestion Pricing with Uniform Revenue Refunding (CPURR) scheme through extensive simulation experiments. Initially, we experimentally show for the single OD pair with two routes network, CPURR does not provide a significantly better solution compared to the UE in terms of expected total monetary cost whenever the OD demand is stochastic. For the same network, we also show that the difference in the expected total monetary cost of truck drivers between the OPS and the CPURR solutions becomes higher as the difference between the distinct classes of VOT becomes larger. Finally, the simulation results using the Sioux Falls network demonstrate that both OPS and AOPS consistently outperform CPURR both in expected total travel time and in expected total monetary cost.
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