Deep Learning Assisted Memetic Algorithm for Shortest Route Problems

Finding the shortest route between a pair of origin and destination is known to be a crucial and challenging task in intelligent transportation systems. Current methods assume fixed travel time between any pairs, thus the efficiency of these approaches is limited because the travel time in reality can dynamically change due to factors including the weather conditions, the traffic conditions, the time of the day and the day of the week, etc. To address this dynamic situation, we propose a novel two-stage approach to find the shortest route. Firstly deep learning is utilised to predict the travel time between a pair of origin and destination. Weather conditions are added into the input data to increase the accuracy of travel time predicition. Secondly, a customised Memetic Algorithm is developed to find shortest route using the predicted travel time. The proposed memetic algorithm uses genetic algorithm for exploration and local search for exploiting the current search space around a given solution. The effectiveness of the proposed two-stage method is evaluated based on the New York City taxi benchmark dataset. The obtained results demonstrate that the proposed method is highly effective compared with state-of-the-art methods.