Optimal Toll Rate Decision Model and Algorithm for Toll Collected Highway Network

Abstract

This paper established a bi-level programming model to determine the optimal toll rate for toll collected highway network, which described the relationship of network manager, operators of tolled roads and users. The upper objective in the bi-level programming model is to maximize network user's surplus, the lower-level problem is multi-vehicle type user equilibrium model with elastic demand based on entry-exit toll charges. In view of complicated characteristic of the bi-level model, a kind of hybrid optimization algorithm—genetic simulated annealing algorithm was proposed to solve it. The calculating data showed that multi-vehicle type model's outcomes: benchmark vehicle type toll rates, entry-exit flows and network user's surplus were respectively lower, bigger and 4.5% higher than that of single-vehicle type model's. The numerical example results indicate that bi-level programming model can balance the benefits among manager, operators and users reasonably, the outcomes of multi-vehicle type model accord with the fact more accurately than single-vehicle type model.