Adaptive spatio-temporal mining for route planning and travel time estimation

Realistic transportation time estimation for urban logistics is challenging due to large amount of historical spatial connections and high variability of transportation time caused by inconsistent traffic situations varying in space and time. In this paper, we propose a probability based method using temporal distribution patterns to estimate logistical transportation time among locations in an urban road network. The method explores historical logistics data including location and time data to construct temporally weighted transportation time patterns in spatial domain. It enables a point-based distributed temporal pattern to be extended to probabilistic area-based spatio-temporal pattern. The experimental results demonstrated that the estimated transportation time fell within vicinity of historical temporal records. The method can be used to generate a map of spatial distribution of transportation time which may provide support in decision making process for urban logistics planning and management.