Real-time Queue Length Estimation on Freeway Off-ramps Using Case Based Reasoning Combined with Kalman Filter

Date
2015-09-18
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Abstract
Real-time queue length estimation and prediction provides useful information for proactively managing transportation networks. Queue spillback from off-ramps onto main freeway lanes is a serious traffic issue that can be efficiently managed using dynamic queue information. In this thesis, a case-based reasoning algorithm combined with a Kalman filter is developed to provide real-time queue length measurements and predictions on long freeway off-ramps. Estimations are based on occupancy readings from three loop detectors installed on the ramp. The proposed method is examined using a micro-simulation model in a Quadstone Paramics package on an off-ramp‏ with a length of 650 meters. The simulation results demonstrate that the model is capable of estimating and predicting the queue length on long off-ramps in 60 second time intervals. The performance of the algorithm is examined under various demand loading scenarios, estimation time intervals and number of detectors through several experiments.
Description
Keywords
Engineering--Civil
Citation
Heshami, S. (2015). Real-time Queue Length Estimation on Freeway Off-ramps Using Case Based Reasoning Combined with Kalman Filter (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/27549