Developing a Context-Aware POI Network of Adaptive Vehicular Traffic Routing for Urban Logistics

Advanced information and communication technology promote smart city development, especially in urban logistics. Vehicular traffic routing problem is the key factor to influence the logistics chauffeur’s service quality. Different from traditional vehicular ad hoc networks, this study proposes a novel approach using data mining, skyline domination, and multi-criteria decision analysis to develop a context-aware point-of-interest network of vehicular traffic routing for urban logistics. The density-based clustering discovers the logistics destination, referred to as the “points-of-interest (POI),” nearby the logistics chauffeur. The candidate POI filtered by the skyline domination. The multi-criteria decision analysis produces a ranking of candidate POI based on the status of traffic criteria evaluation. We use open data from Google map and Foursquare to construct a context-aware POI network. An experimental system implementation to demonstrate the proposed approach effectiveness. The contribution is to optimize the adaptive vehicular traffic routing solution for the urban logistics in a smart city.