Abstract Based on the pictures of queuing vehicles at intersections collected by video cameras, a method of traffic signal control based on picture self-learning is proposed. Based on Convolution Neural Network, this paper classifies the queuing length pictures of vehicles with different phase-critical traffic flow, establishes the relationship database between the picture data set and the green light display time of the pictures with different phase-critical traffic flow queuing length categories, and obtains the current phase green light display time of the current cycle on the basis of the relationship database, so as to achieve real-time optimization. The purpose of the signal control scheme. This method does not need the exact traffic flow collected by traffic flow detector, but acquires the pictures of queuing vehicles in different periods and phases, and trains the green light duration to control the traffic in real time.
Research on Road Traffic Signal Timing Method Based on Picture Self-learning
International Symposium for Intelligent Transportation and Smart City (ITASC) 2019 Proceedings ; 118-126
2019-01-01
9 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Intelligent Transportation Systems , Traffic signal control , Picture self-learning , Convolution Neural Network Energy , Transportation , Transportation Technology and Traffic Engineering , Geotechnical Engineering & Applied Earth Sciences , Sustainable Development , Communications Engineering, Networks
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