On 12 th to 14 th December Organized by A SIMPLE METHODOLOGY FOR ESTIMATION OF QUEUE LENGTH

ABSTRACT Urban arterials are characterized by frequent intersections. Queue length and delay are the two primary measures of performance of intersection. These measures play a primary role in determining the arterial performance. This article presents a methodology to determine the number of vehicles in queue at a signalized intersection for under saturated traffic conditions. The results obtained were validated using actual values that are manually extracted. The root-mean-square error is of the range 1.3 vehicles for estimation of number of vehicles in queue. The various aspects that have to be considered in accurate estimation of performance measures are also discussed. 1.INTRODUCTION The typical characteristics of urban traffic are frequent stops due to congestion and intersections, and associated delays and pollution. One of the major reason for this is the presence of signalized intersections. Signalized intersections, while helping to make the traffic more organized and safe, may lead to more delays, especially during off-peak hours. The major parameters that are used to quantify the performance of a signal are the queue length and delays. Thus, the information of number of vehicles in queue and associated delay are useful for devising traffic management strategies that would help in improving the performance of traffic network. This study presents a simple approach to estimate the queue length using the data that are collected by automatic detectors for real time applications. The most commonly used and widely employed sensors for automatic detection of vehicles are the loop detectors. The loop detector produces an electric pulse when a vehicle passes over it. The produced pulse is digitized by setting all values higher than a threshold equal to one and others equal to zero. Loop detector data dominates the traffic detection systems due to its low costs. Automatic detectors however suffer from associated errors in detections. The present study employs traffic data collected from loop detectors for estimation of number of vehicles in queue at intersections. The data that are collected from the loop detectors include the stop bar and advanced detector actuations which gives the traffic count, time occupancy as well as the time headway. These traffic parameters are used in this study to develop suitable estimation schemes for finding out the number in queue