Many approaches had been proposed for travel time prediction in these decades; most of them focus on the predicting the travel time on freeway or simple arterial network. Travel time prediction for urban network in real time is hard to achieve for several reasons: complexity and path routing problem in urban network, unavailability of real-time sensor data, spatiotemporal data coverage problem, and lacking real-time events consideration. In this paper, we propose a knowledge based real-time travel time prediction model which contains real-time and historical travel time predictors to discover traffic patterns from the raw data of location based services by data mining technique and transform them to travel time prediction rules. Besides, dynamic weight combination of the two predictors by meta-rules is proposed to provide a real-time traffic event response mechanism to enhance the precision of the travel time prediction.
['Wei-Hsun Lee', 'Shian-Shyong Tseng', 'Sheng-Han Tsai']
A knowledge based real-time travel time prediction system for urban network