An efficient learning-based approach to multi-objective route planning in a smart city

Route planning is an important service in the map navigation. However, most of commercial map applications provide an optimal path that only minimize a single metric such as distance, time or other costs, while ignoring a critical criterion: safety. When citizens or travellers walk in a city, they may prefer to find a safe walking route to avoid the potential crime risk and to have a short distance, which can be formulated as a multi-objective optimization problem. Many previous methods are proposed to solve the multi-objective route planning, however, most of them are not efficient or optimized in a large-scale road network. In this paper, we propose a reinforcement learning based Multi-Objective Hyper-Heuristic (MOHH) approach to route planning in a smart city. We conduct experiments on the safety index map constructed based on the historical urban data of the New York city. Comprehensive experimental results show that the proposed approach is almost 34 and 1.4 times faster than the exact multi-objective optimization algorithm and the NSGA-II algorithm respectively. Moreover, it can obtain more than 80% Pareto optimal solutions in a large-scale road network.