Image-Based Learning to Measure the Stopped Delay in an Approach of a Signalized Intersection
IEEE Access, ISSN: 2169-3536, Vol: 7, Page: 169888-169898
2019
- 9Citations
- 17Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Traffic delays are inevitable when evaluating the performance of a signalized intersection, but these delays cannot be directly measured in the field based on existing spot detectors. Traffic-light controllers have adopted a reinforcement learning (RL) algorithm, which is currently prevalent in the field of study and requires real-time measurement of traffic delays to derive the state and reward for each time period. No RL-based study, however, has provided a robust way to measure traffic delays. In order to bridge the gap, we devised a convolutional neural network (CNN) to directly measure traffic delays from video footage in an end-to-end manner. The proposed methodology proved superior to both a state-of-the-art vision technology and an analytic formula that has widely been used to estimate delays. Furthermore, a robust method to secure labeled data without human input was suggested based on a cycle-consistent adversarial network (CycleGAN).
Bibliographic Details
Institute of Electrical and Electronics Engineers (IEEE)
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