Research Projects

Urban roads are experiencing increased vehicle usage, leading to congestion and delays in travel time. Initially, traffic control was manually controlled by traffic police, but this system was not effective for dynamic traffic flow. New techniques were developed using cameras and sensors to monitor traffic flow, such as density-based traffic control systems. These systems save time and avoid excessive congestion, leading to smoother traffic flow. However, limitations include unclear images during heavy rain and inaccurate sensor arrangement. Wireless traffic control techniques have been developed to efficiently control traffic flow with sensors and load cells attached to pavement for vehicle density. However, installation costs are high and sensor accuracy decreases over time. Adaptive neuro fuzzy inference systems (ANFIS) were developed to improve traffic conditions, but nighttime vehicle detection methods cannot be used due to poor illumination conditions. Traffic control systems have also been integrated with traffic light control systems using radio frequency identification (RFID) technology and fuzzy logic. RFID is a wireless link that uniquely identifies objects or people, but the phase sequence is uncertain in this system. An innovative method for controlling traffic effectively is self-sensing piezo resistive conductive material pavement interfaced with an automized traffic control system with a microcontroller. The conductive fillers in the pavement sense stress, strain, deflection, cracks, humidity, and temperature through electron transfer. Although the initial cost is slightly higher than traditional sensors, maintenance and repair work can be neglected, making it more economical.