Conventional Traffic Control System Research Articles

An Experimental Study on Automatic Traffic Light System using IR Sensors

Traffic jam turns out to be key crisis in these days. Traffic jam mainly occurs in urban areas. Due to traffic jam, there are several problems arise such as increase in noise pollution, air pollution, accidents and delay in travel time etc. The present traffic signals deployed in all parts of the cities are not enough to solve above mentioned problems because these have specific pre- determined time for red and green signals. In this view various attempts were done for traffic lights to behave smartly based on density of vehicles on the road. Therefore, many techniques have been used in traffic control systems. This paper summarizes different techniques of traffic control system that were used for the improvement of conventional traffic control system. Congestion is a serious issue due to vehicular traffic. One of the known causes of traffic congestion is the amount of time spend waiting for the red light to change to green. The changing of traffic light is hard-coded and it is not reliant on traffic volume. There is therefore need to simulate and optimize traffic control to better accommodate density based traffic rather than time based. This system attempts to lessen Possibilities of traffic jams brought about by traffic lights to a reasonable degree. This project, a density-based traffic control system is been implemented to solve this problem. The system entails programming an Arduino using Arduino integrated development environment to enable traffic lights give the right of access to the road by selecting the lane with the high number of cars. The traffic lights are modified to chip away at an auspicious premise until there is a signal identified by the infrared sensors. The sensor identifies an object (i.e., a vehicle, a motorcycle etc.) and signals the Arduino to control the traffic lights for its individual path. Once there is no sign identified by any of the four sensors the traffic lights keep on dealing with an auspicious premise. The mean response time of the sensor was found to be 0.39 seconds. Further research is recommended to produce the device on a large scale to be deployed to all roads in the country.

Machine learning‐based traffic scheduling techniques for intelligent transportation system: Opportunities and challenges

SummaryThe rise in traffic congestion has become a significant concern in the urban city environment. The conventional traffic control systems with inefficient human resources management fail to control the traffic discipline, leading to increased traffic density and road offenses. However, the intelligent transportation system (ITS) can provide safety, efficiency, and sustainability for large‐scale vehicular traffic dilemmas. ITS unites machine learning with the available traffic control force and performs real‐time police scheduling to ensure the smooth flow of traffic. Many researchers have demonstrated notable work in intelligent traffic police scheduling and deployment using various optimization algorithms. However, the compilation of such praiseworthy work as a whole is still missing. Motivated by these facts, we provide a comprehensive review of the machine learning‐based state‐of‐the‐art technologies that can be used to form a three‐tier solution taxonomy. The first tier describes various tools and technologies that can be utilized to collect traffic data. The second tier highlights the machine learning algorithms and their accuracy, which forms a pattern in the collected data and then yields some crucial information about traffic flow, congestion levels, and so forth. In the third tier, the most vital taxonomy layer, various traffic police scheduling strategies are discussed. The proposed survey also presents the use of cases of traffic police scheduling that elaborates this review's applicability in various domains. Finally, some of the key challenges in the subject being reviewed are discussed, which initiates a further scope of improvement.

Developing a Coordinated Signal Control System for Urban Ring Road Under the Vehicle-Infrastructure Connected Environment

Ring roads have been widely built in many cities, especially in the central districts with excessively heavy traffic demands and frequently generated congestion. In order to improve the operations and reduce traffic delay on urban ring roads, this paper developed a coordinated signal control system for urban ring roads under vehicle-infrastructure connected environment. The speed guidance would be provided to motorists utilizing four sub-systems including detection, communication, signal control, and expected speed calculation in the system. The signal timing parameters such as cycle length, green split, and offset, would be adjusted based on the artificial bee colony-shuffled frog leaping algorithm. The proposed signal control system had been test using VISSIM simulation model and the simulation results showed that the average delay, number of stops, and queue length were significantly improved compared with the conventional traffic control system.

Agent Based Intelligent Traffic Management System for Smart Cities

The performance of traffic systems is greatly dependent on their ability to react to changing traffic patterns and different situations. On traditional traffic systems, the lights run green for fixed intervals of time no matter what the density of the traffic is. Here, we implement an intelligent-agent traffic model that controls the amount of time a light runs green for, based on the number of cars (density) standing in the light. With an increase in the pace at which we lead our lives, time is of the essence. One place where time is wasted is on the road being stuck in traffic jams due to mismanagement of traffic. The effectiveness of (urban) traffic control systems greatly depends on its ability to react upon changes in traffic patterns, however, conventional traffic control systems, being unintelligent are unable to do this. Be the traffic density, high or low, the signals are timed and run according to those times only. This results in increased congestion along the roads which also results in significant air pollution, an increased safety risk and also has a negative effect on the populace's mental wellbeing. It is only when the ability to react becomes an integral part of the traffic control unit, its ability to react to changes in traffic conditions improves. The responsive control system should have sufficient knowledge of the situation to be able to handle unforeseen changes in traffic flow, such as accidents or car break-down. Intelligent signal control systems must have the capability to optimize the traffic flow by adjusting the traffic lights and co- ordinate operation between each signal in order to maximize the person and vehicular throughput and minimize delay. For intelligent urban traffic control, we ideally need a fully pro-active, real-time traffic control system; anticipating what will happen within the near future. 2. Need for Smart City A smart city (also smarter city) uses digital technologies or information and communication technologies (ICT) to enhance quality and performance of urban services, to reduce costs and resource consumption, and to engage more effectively and actively with its citizens. Smart city applications are developed with the goal of improving the management of urban flows and allowing for real time responses to challenges (18).A