Travel-time prediction for urban arterial road: a case on China

Traffic flow on urban arterial roads is interrupted by intersections. It is difficult to predict travel time because of the effect of uncertain factors. In this paper the average space speed is adopted as a medium variable. The artificial neural network technology is applied to map the relationship of the traffic flow and average-space speed. Then average space speed was converged to average travel time. This approach was tested using the data from an arterial road in Changchun. The result shows that this method can make the prediction calculation more reliable, more cost-efficient and easier.