Real-time estimation of arterial travel time under congested conditions

Abstract

It is well-known that accurate estimation of arterial travel time on signalised arterials is not an easy task because of the periodic disruption on traffic flow by signal lights. It becomes even more difficult when the signal links are congested with long queues because under such situations the queue length cannot be estimated using the traditional cumulative input–output curves. In this article, we extend the virtual probe model previously proposed by the authors to estimate arterial travel time with congested links. Specifically, we introduce a new queue length estimation method that can handle long queues. The queue length defined in this article includes both the standing queue, i.e. the motionless stacked vehicles behind the stop line, and the moving queue, i.e. those vehicles joining the discharging traffic after the last vehicle in the standing queue starts to move. The moving queue concept is important for the virtual probe method because moving queue also influences the manoeuvre behaviour of a virtual probe. We show that, using the ‘event’ data (including both time-stamped signal phase changes and vehicle-detector actuations) collected from traffic signal systems, time-dependent queue length (including both standing queue and moving queue) can be derived by examining the changes in an advance detector's occupancy profile within a cycle. The effectiveness of the improved virtual probe model for estimating arterial travel time under congested conditions is demonstrated through a field study at an 11-intersection corridor along France Avenue in Minneapolis, MN.