The constrained reliable shortest path problem for electric vehicles in the urban transportation network

Abstract

Electric vehicles (EVs) have recently received increasing attention, because of the environment-friendly features. As one of the major concerns of electric vehicles, the path planning problem in urban transportation networks faces the difficulties of the travel time uncertainty and driving range limitation, due to the unexpected congestion, bad weather and battery capacity. This paper investigates the constrained reliable shortest path (CRSP) problem for electric vehicles in the urban transportation network. A mixed-integer programming model is developed for the CRSP problem, where the reliable travel time is treated as the objective and the energy consumption, instead of the distance, is taken as the driving range constraint. To simply the scale of the problem, some pre-processing and network reduction techniques are introduced. Then a label setting algorithm based on elaborate dominance conditions is proposed to solve this problem. Besides, the A∗ technique is integrated to improve the search performance of the algorithm. The proposed model and algorithms are tested in two real-world urban transportation networks and scalable grid networks. Numerical studies show that the path choice is impacted by the risk attitudes of travelers and the battery capacity of electric vehicles. It implies that the navigation system should customize the optimal path in more detail to satisfy travelers’ heterogeneous requests. The proposed algorithm can obtain the exact solutions within satisfactory computational time, and it can be embedded into the intelligent navigation system to help the government promote the adoption of electric vehicles.