Intelligent Geographical Information System for Vehicle Routing (IGIS-VR): A modeling framework

In Egypt, freight movement relies heavily on road transport. Commercial vehicles constitute a major segment of the vehicle population that travels the country's roads contributing to (and suffering from) daily congestion. Enhancing CV operations by minimizing their en-route travel times benefits both traffic network users as well as CV's business owners. The absence of traffic data collection infrastructure, in many developing countries, hampers the usage of readily available vehicle routing systems. This paper introduces a modeling framework of an Intelligent Geographical Information System for Vehicle Routing (IGIS-VR). IGIS-VR integrates a Geographic information system (GIS) and a Reinforcement learning (RL) system to address the Capacitated Vehicle Routing Problems with Time Windows (CVRPTW). The developed model uses CVs as probe vehicles for on-the-move data collection. Collected data is manipulated through a self-adaptive learning environment to capture traffic network dynamics. The Q-learning concepts of the Temporal Difference (TD) solution approach of RL are used in the model formulation. Different estimation procedures for the model main parameters are explored.