Innovative Dynamic Queue-Length Estimation Using Google Maps Color-Code Data

Abstract

Queue length is an important parameter for traffic-signal priority systems for emergency vehicles. Instead of using conventional detector data, this paper investigates the feasibility of queue-length estimation using Google Maps color-code data via random forest (RF) and gradient-boosting machine (GBM) methods. Alternative ways of specifying independent variables from color-code data are also investigated. Additionally, the models are separated by peak or off-peak periods and by the presence or absence of adjacent upstream signalized intersections. The results show that the performance predicted by the RF and GBM methods is similar in all cases. Although the error values of both methods are relatively high, they are considerably lower than those obtained from estimates using historical queue-length data. The results obtained using variable-importance analysis show that the importance of the red band near an intersection is significantly higher than that of other variables for a direction without a prior signalized intersection. For a direction with a prior signalized intersection, the importance varies, depending on the period (peak or off-peak). Since Google Maps data are available and cover most of the world intersections, the proposed approach provides a cost-effective option for cities with no detectors installed.