Predicting experienced travel time with neural networks: a PARAMICS simulation study

The implementation of intelligent transportation systems (ITS) in recent years has resulted in the development of systems capable of monitoring roadway conditions and disseminating traffic information to travelers in a network. However, the development of algorithms and methodologies specialized in handling large amounts of data for the purpose of real-time control has lagged behind the sensing and communication technological developments in ITS. In this study, data generated by a PARAMICS model of a real-world freeway section are used to develop an artificial neural network (ANN) capable of predicting experienced travel time between two points on the transportation network. Computational experiments demonstrate that the studied ANNs were able to reasonably predict the experienced travel time. Generally, the study shows that the length of the time lag did not have a statistically significant effect on ANN performance, that speed appears to be the most influential input variable, and no statistically significant difference in ANN performance was observed when data from the left lane loop detector was substituted for data from the right lane loop detector.