Intelligent Traffic Control System Using Deep Learning

Abstract

Traffic congestion and regulating traffic in traffic signals are major issues in cities. Nowadays, in most of the cities, traffic management centers installed numerous cameras all over the roads and traffic signals. Such cameras can be effectively used for the automation of traffic signals. The objective is to develop a real time system that can automatically monitor real time traffic and make the system intelligent using artificial intelligence techniques. Specifically, Deep Convolutional Neural Networks are employed to perform the task. From statistical traffic data, it determines count, type of vehicle, average speed, distance between vehicles, etc. Based on traffic, the algorithm instructs to stop vehicle or queue or move. It can also record a wrong-way driver. Using license plate recognition, security applications such as unauthorized vehicles are identified. If there is violation of traffic rules, they are recorded with registration number. It can detect ambulances and give first preference. The proposed algorithm identifies VIP vehicles and clear traffics in automated ways. Ambulances are given priority to pass the road. The entire system have been developed using a standalone-Graphical User Interface (GUI). We have implemented successfully and the proposed framework performs satisfactorily.

Dynamic Lane Reversal Strategy in Intelligent Transportation Systems in Smart Cities

“…To evaluate the performance of the DLRS, we performed some simulations based on the cellular automaton (CA) model. In order to understand the traffic flow dynamics, it is universally acknowledged that cellular automaton models (CA) are the essential component [21][22][23][24][25][26][27][28]. Based on prior research, the Nagel-Schreckenberg (NS) model is the most commonly used model in CA studies [3][4][5][7][8][9]14,29].…”