A queue length estimation and prediction model for long freeway off-ramps

Abstract

Real-time queue length estimation and prediction provides useful information for proactively managing transportation networks. Queue spillback from off-ramps onto main lanes of freeways is one of the traffic issues caused by vehicular queues that can be efficiently managed using dynamic queue information. In this paper, a case-based reasoning algorithm combined with a Kalman filter is developed to provide real-time queue length estimations and predictions on freeway off-ramps. The estimations are based on occupancy readings from three loop detectors installed on a ramp. The proposed method is examined using a micro-simulation model of an off-ramp with a length of 650 meters and a traffic signal downstream of the ramp. The simulation results show an accuracy of ±3.15 vehicles in the queue in 60-second time intervals. In addition, a rigorous sensitivity analysis is conducted to examine the performance of the algorithm under various demand loading scenarios, time intervals, number of detectors used, and errors in prior estimations. The results show that the model performs well in terms of estimating and predicting the length of long queues on freeway off-ramps at various congestion levels. The outcomes of this study can be utilized to activate dynamic, responsive, and proactive queue management and traffic control measures.

Keywords:

• Case-based reasoning
• Kalman filtering
• off-ramp
• queue length estimation/prediction

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was financially supported by the Urban Alliance Professorship Fund from the City of Calgary and by the Alberta Motor Association – Alberta Ingenuity Technology Future (AMA-AITF) Collaborative Grant in Smart Multimodal Transportation Systems.