Spatio-temporal Mining with Scene Data Integration for Urban Transportation Navigation

With recent development of telemetry technology, various types of sensors have been used in current logistics and transportation industry to automate coordination among logistics companies, deliverers and customers. Global Positioning Sensor (GPS) has been widely used to track delivers' location. However, real-time and on-site environmental information plays a key role in making optimal decisions for vehicle routing and transport navigation. In this study, we propose and develop a data mining method which generates optimal routes based on global spatio-temporal pattern knowledge and local scene information extracted from large-scale and realtime images captured by dash cameras. Scene recognition is used to generate location-based scene information. A temporally weighted route mining model establishing transportation time distribution patterns can be used to produce optimal routes. Experimental results demonstrated that image data from location of different types of road segments could be converted to geospatial information used for spatio-temporal pattern generation.