What is it about?
Queue length is more likely to be affected by inefficient departure behaviour near the stop line at an intersection because of the prohibition of lane changes. The inefficient departure behaviour and its influence on the evolution of queue discharging in specific lanes have significant research value. In the past few years, the deployment of automatic vehicle identification (AVI) systems has made it possible to acquire information on vehicles’ passing through an intersection. We can obtain individual driving information more accurately and effectively based on an analysis of AVI data. These high-resolution data have enabled the reconstruction and the quantitative analysis of the influence of microscopic traffic behaviour. The aim of this study is to model the departure of vehicle platoon and predict the queue length at an isolated intersection. We propose a multi-layer GRU network to understand the mechanism of queue length evolution. A case study of vehicle departure pattern and queue length prediction is presented with AVI data obtained from an isolated intersection in XuanCheng, An’Hui province, China for the first quarter of 2018. The results of the case study indicate that our work has good application prospects. The proposed multi-layer GRU network has potential to guide the signal scheme optimization.
Photo by Samuel Foster on Unsplash
Why is it important?
(1) Make an elaborate distinction based on traffic efficiency at different segments in a queue. (2) Design a novel data structure containing temporal information by using AVI data and signal records. (3) Implement an efficient multi-layer GRU network for queue length prediction.
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This page is a summary of: Vehicle departure pattern and queue length prediction at an isolated intersection with automatic vehicle identity detection, IET Intelligent Transport Systems, September 2019, the Institution of Engineering and Technology (the IET),
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