Adaptive real time traffic prediction using deep neural networks - Parinith R Iyer; Shrutheesh Raman Iyer; Raghavendran Ramesh; Anala M R; K.N. Subramanya (Institute of Advanced Engineering and Science, 2019)
The ever-increasing sale of vehicles and the steady increase in population density in metropolitan cities have raised many growing concerns, most importantly commute time, air and noise pollution levels. Traffic congestion can be alleviated by opting adaptive traffic light systems, instead of fixedtime traffic signals. In this paper, a system is proposed which can detect, classify and count vehicles passing through any traffic junction using a single camera (as opposed to multi-sensor approaches). The detection and classification are done using SSD Neural Network object detection algorithm. The count of each class (2-wheelers, cars, trucks, buses etc.) is used to predict the signal green-time for the next cycle. The model selfadjusts every cycle by utilizing weighted moving averages. This system works well because the change in the density of traffic on any given road is gradual, spanning multiple traffic stops throughout the day.