A Data Mining and Optimization-based Real-time Mobile Intelligent Routing System for City Logistics

City logistics is facing the challenging problem of providing a quick-response and on-time delivery service in congested urban areas with frequent traffic jams. The dynamically changing traffic conditions make the predetermined best transportation plans suboptimal and consequently cause increased logistics cost and even greater air pollution. To help the driver determine time-optimal routing solutions in order to avoid congestion according to the real-time traffic flow, a Real-time Mobile Intelligent Routing System is designed and deployed on drivers' Smartphones to help in routing decision making. Data mining techniques are employed to discover the routing patterns from the past cases of routing plans so as to generate case-based routing plans for the drivers. A metaheuristic is used to undertake the optimization of a real-time optimal routing plan based on real-time traffic information. A case study and computational experiments demonstrate the effectiveness of the proposed methods in significantly reducing the traveling time.