Bi-Objective Optimization for Time-Dependent Preference-Driven Route Planning

Abstract

The development of intelligent transportation systems and the advancement of information technology bring new challenges to route planning, as shorter travel time may no longer be the travelers’ only preference for a route, and the preferences may also change over time which is overlooked in most prior work. In this paper, we study a new bi-objective planning problem with both time-dependent travel time and preference. The first objective is to maximize the total preference score and the second one is to minimize the total travel time. For the considered problem, an appropriate bi-objective integer linear model is formulated. Then, an exact $\epsilon$-constraint method is proposed for small-sized instances, while a problem specific non-dominated sorting genetic algorithm-II (NSGA-II) is designed to handle large-sized instances. Specifically, novel region-based encoding and decoding methods are introduced to generate a set of solutions. Additionally, a feasibility condition and a repair strategy are incorporated to address cases where a chromosome is infeasible. We evaluate the proposed methods thoroughly based on 120 randomly generated road networks and 3 real-world road networks crawled via the OpenStreetMap platform. Results show that: (i) $\epsilon$-constraint method obtains good performance on small-sized road networks; (ii) our problem-specific NSGA-II works well with large-sized road networks in obtaining the high-quality solutions while significantly saving computational time.