A Real-Time Prediction Model for Individual Vehicle Travel Time on an Undersaturated Signalized Arterial Roadway

Abstract

Predicting the segment travel time of a vehicle, which includes its travel time on a road segment and the delay for passing the downstream intersection, can greatly help the drivers to better control a vehicle’s speed to reduce signal delay and gasoline consumption. However, so far, few effective methods have been developed that can accurately characterize an individual vehicle’s segment travel time. To address this problem, this article seeks to develop an analytical model to predict each vehicle’s travel time on a signalized arterial roadway under nonsaturated traffic conditions. The proposed model is developed based on the following three situations a vehicle can experience when passing through a signalized intersection: 1) passing directly in the green time phase, 2) decelerating until coming to a complete stop in the red intervals, and 3) decelerating initially, but passing through without a stop. The situation that a vehicle will encounter is predicted by analyzing the correlation between a vehicle’s entry time to the segment and the signal-phase timing using the kinematic wave theory. Then, the intersection delay of each vehicle is predicted based on the queue shockwave speed and the discharge shockwave speed. A vehicle’s travel time on a road segment is predicted by summing the intersection delay and the segment free-flow travel time. The numerical application shows that the proposed model can accurately characterize a vehicle’s travel time. Thereby, it can be applied in a connected environment to predict the segment travel time to improve the mobility of the traffic.