Multi-objective optimal path finding in stochastic time-dependent transportation networks considering timeliness reliability and travel expense

Road traffic congestion has aroused earnest concern in the public and the academic. Two apparent features of congested traffic networks are stochasticity and time dependency. In fact, different types of travelers have different route choice preferences, regarding to not only timeliness but also expense, which may involve multiple conflicting criteria. In this paper, a stochastic time-dependent network is presented and timeliness reliability, which means the probability that a trip can be successfully fulfilled within a desirable travel time budget, is focused. Risk-sensitive and cost-sensitive travelers are considered. Risk-sensitive travelers care most about timeliness reliability and cost-sensitive travelers desire least travel expense. A new multiobjective optimal path finding model is proposed in this paper to find optimal paths in stochastic time-dependent transportation networks considering timeliness reliability and travel expense. In order to solve this multi-objective model, non-dominated sorting genetic algorithm-II is utilized and its parameters are set by the Taguchi method. A numerical example of road network consisted of main streets in Beijing is demonstrated. Favorite results are obtained to verify that the proposed method is perfectly applied to provide a set of non-dominated paths in order to facilitate different travelers' route choice base on their attitudes toward timeliness reliability and travel expense.