A Data-Driven Reinforcement Learning Based Multi-Objective Route Recommendation System

Driving route recommendation systems have been becoming popular due to high demands on such systems and their high socio-economic impacts. Existing route recommendation systems cannot provide a well-balanced route by considering the user preference on multiple criteria or make route recommendation in a short time. This paper presents a multi-objective route recommendation system considering three different attributes (i.e., fuel consumption, travel time, and air quality). The proposed route recommendation system uses the Q-learning based reinforcement learning algorithm to leverage the available datasets to make route recommendations in a timely manner. First, we build a road network graph using a publicly available map service (i.e., OpenStreetMap) and other real-world datasets on traffic, weather, and air substances. Second, we utilize the existing predictors for air quality, travel time, and fuel consumption estimations to update the road network graph periodically. Third, we design the route recommendation system using the Q-learning reinforcement learning approach considering the given user’s preference for travel time, fuel consumption, and air quality. To evaluate the proposed approach’s performance, we conduct experimental evaluations based on the real-world datasets with publicly available map service.