Personalized Pareto-improving pricing-and-routing schemes for near-optimum freight routing: An alternative approach to congestion pricing

We design a coordination mechanism for truck drivers that uses pricing-and-routing schemes can help alleviate traffic congestion in general transportation network. consider the user heterogeneity Value-Of-Time (VOT) by adopting multi-class model with stochastic Origin-Destination (OD) demands drivers. The main characteristic of is coordinator asks to declare their desired OD pair and pick individual VOT from set $N$ available options, guarantees resulting scheme Pareto-improving, i.e. every driver will be better-off compared User Equilibrium (UE) have an incentive truthfully his/her VOT, while leading revenue-neutral (budget balanced) on average mechanism. This approach enables us personalized (VOT-based) schemes. show Optimum Pricing Scheme (OPS) calculated solving nonconvex optimization problem. To improve computational efficiency, we propose Approximately (AOPS) prove it satisfies aforementioned properties. Both are Congestion Uniform Revenue Refunding (CPURR) through extensive simulation experiments where shown OPS AOPS achieve much lower expected total travel time monetary cost users CPURR scheme, without negatively affecting rest These results demonstrate efficiency