Development of A Travel Time Prediction Model for Urban Road Network using Multi-source Data

In view of the deficiencies of traditional travel time prediction models developed based on Kalman filtering technique and single data source ,the multi-source data are used to improve such models and the prediction accuracy of travel time .Floating cars and loop detectors are common ways for collecting travel time ,and the two are complementary to each other in many ways .Therefore ,the real-time traffic data from the two sources are used as the inputs of the pre-diction model .Through Kalman filtering ,flow ,occupancy and travel time are used as inputs of the proposed travel time prediction model .Finally ,the model is verified through a simulation from Vissim .The simulation results show that the average absolute relative error of the estimated travel time based on the model developed based on the multi-source data is 5 .45% ,which is 14 .4% lower than those estimated based on the loop detector data only and 7 .5% lower than those esti-mated based on the floating car data alone .