Predicting vehicle travel time on routes of unbounded length in arterial roads

Abstract

A system and method for predicting travel time of a vehicle on routes of unbounded length within arterial roads. It collects historical information from probe vehicles positions using GPS technology in a periodic fashion and the sequence of links traversed between successive position measurements. Further, it collects information of neighborhood structure for each link within the arterial roads network. Any of the existing conditional probability distribution functions could be used to capture the spatio-temporal dependencies between each link of the arterial network and its neighbors. It learns the parameters of this data driven probabilistic model from historical information of probe vehicle trajectories traversed within the arterial roads network using an associated expectation maximization method. Finally it predicts travel time of vehicles on routes of unbounded length in a novel fashion within the network of arterial roads using the learnt parameters and current real time information of trajectories of vehicle.