A data mining based algorithm for traffic network flow forecasting

Recent development in ITS (intelligent transportation systems) methods and technologies has moved traffic operating systems from passive to proactive control and management, where real-time and accurate traffic flow information is critical to actual implementation. So far many algorithms have been proposed for traffic network flow forecasting, but problems in accuracy and timeliness still remain to be the major obstacle for their successful applications. For example, presumed human travel habit and vehicle turning probabilities at intersections have greatly limited the use of dynamic assignment algorithm. In order to improve the forecasting and real-time responsiveness, a new algorithm based on data mining which can do association rules mining and association analysis is proposed here for predicting traffic network flow. Simulation results using Corsim 5.0 have demonstrated effectiveness of the new algorithm in both accuracy and timeliness.