Traffic rules violation detection systems

Due to the massive population increase and the overcrowding of transport networks, traffic accidents are also increasing. These accidents cause colossal material and moral losses and are considered a hindrance to development. Accidents are frequently triggered by a combination of variables rather than a single cause. The most effective way to reduce these accidents is to activate the control systems, detect violators of traffic laws promptly, and deter potential violators. Traffic rules violation detection systems (TRVDS) are an effective solution to help traffic management authorities. TRVDS can detect traffic rules violations, such as escaping from red lights and over-speeding. However, despite the diversity of existing systems, there is no comprehensive research that abstracts the used systems and technologies as a foundation for TRVDS. Therefore, this thesis presents a deep review of the techniques used to build TRVDS. Moreover, this work aims to provide a referential source that can organize ideas and direct TRVDS research in a promising direction. Furthermore, many techniques are used to build TRVDS, the most important and promising ones like Vehicular Ad Hoc Network (VANET), Radio Frequency Identification (RFID), and Artificial intelligence (AI) are discussed, analyzed, and compared with details in this thesis.