A New Model for Estimating Real-time Travel Time on a Signalized Arterial

Abstract

A new model for estimating the real-time travel time on a signalized arterial is developed in this paper.The basic idea behind the proposed model is that the time horizon analyzed is first divided into multiple time windows,and these time windows are then further divided into shorter time intervals.In the paper,the arterial system states are defined as whether the vehicles at the stopping line of an intersection queue or not.After the determination of the arterial system states at each time interval within each time window,a stochastic process with memoryless property is then established. Consequently,the travel time on a signalized arterial within a time window can be estimated by using the discrete-time Markovian decision process(DTMDP).This process is applied repeatedly to each time window,and the real-time travel time on the signalized arterial over the time horizon is then obtained.Compared to the previous related models,the model's inputs only include the data of traffic flow and signal setting,the proposed model's parameters are less and easier to calibrate,the cost of model application is low,and the model has a good transplant property as well.Finally,the model proposed in this paper is validated on an actual signalized arterial in Guangzhou.