An adaptive model for real-time estimation of overflow queues on congested arterials

The ability to estimate the status of current traffic congestion of a road network is of significant importance for many Intelligent Transportation Systems (ITS) applications such as in-vehicle route guidance systems (RGS) and advanced traffic management systems (ATMS). Substantial research effort has been dedicated to developing accurate and reliable techniques for estimation of various congestion measures such as link travel time and average travel speed. Few reliable models have however been reported, especially for congested arterials. This paper presents a model that can be used to estimate one of the congestion measures, namely real-time overflow queue at signalized arterial approaches. The model is developed on the basis of the principle of flow conservation, assuming that time-varying traffic arrivals can be obtained froth loop detectors located at signalized approaches and signal control information is available online. A conventional microscopic simulation model is used to generate data for evaluation of the proposed model. A variety of scenarios representing variation in traffic control, level of traffic congestion and data availability are simulated and analyzed. The evaluation results indicate that the proposed model is promising in terms of the accuracy it can provide and advantages it has over existing models.