Estimation of time-dependent, stochastic route travel times using artificial neural networks

This paper presents an artificial neural network (ANN) based method for estimating route travel times between individual locations in an urban traffic network. Fast and accurate estimation of route travel times is required by the vehicle routing and scheduling process involved in many fleet vehicle operation systems such as dial-a-ride paratransit, school bus, and private delivery services. The methodology developed in this paper assumes that route travel times are time-dependent and stochastic and their means and standard deviations need to be estimated. Three feed-forward neural networks are developed to model the travel time behaviour during different time periods of the day the AM peak, the PM peak, and the off-peak. These models are subsequently trained and tested using data simulated on the road network for the City of Edmonton, Alberta. A comparison of the ANN model with a traditional distance-based model and a shortest path algorithm is then presented. The practical implication of the ANN method is subsequently demonstrated within a dial-a-ride paratransit vehicle routing and scheduling problem. The computational results show that the ANN-based route travel time estimation model is appropriate, with respect to accuracy and speed, for use in real applications.