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The current traffic signal light timing scheme cannot effectively solve the problem of traffic congestion. This paper studied the intelligent control of signal lights based on infrared sensors. Firstly, the traffic flow was detected by infrared sensors; then, the future traffic flow was predicted using a back-propagation neural network; an intelligent control algorithm was designed based on the prediction results, and data were collected at the actual intersection for simulation experiments. It was found that the maximum and minimum root-mean-square error (RMSE) value of the BPNN prediction were 72.33 and 45.77, respectively, i.e., BPNN could predict the traffic flow accurately. Then, in the comparison of timing schemes, the maximum average queuing delay time of the BPNN was 0.55 s, and the maximum average queuing length was 92 m in the case of large traffic flow, both of which were better than the traditional fixed timing scheme. The results prove the reliability of the proposed signal light intelligent control algorithm. The algorithm can be further applied in practice. [ABSTRACT FROM AUTHOR]

In the process of collecting traffic information, traditional traffic flow control methods have some problems, such as high loss rate of sensing and detection information and long average queue length, which lead to the unsatisfactory effect of traffic control and evacuation in main roads. Therefore, based on multi-sensor information fusion technology, this study designs a new intelligent control method of traffic flow on main road. According to the multi-sensor information, the fusion algorithm and measurement equation are designed to obtain the main road traffic sensor position, vehicle linear velocity and other parameters, and update the fusion results in real time. Then, the short-term characteristics of traffic flow are used to control the appearance time of target section. Based on the ordinary differential equation, the main road traffic network is represented by intelligent directed graph, and the intelligent dredging model of urban main road traffic flow is constructed. The simulation results show that in five experimental roads, the average delay time of this method is less than 35 s, the maximum average number of stops is only 8.8, the average queue length is less than 30 m, the average travel speed is more than 25 km/h, the traffic flow per unit time is more than 250veh/10 min, and the traffic congestion index of main road is always more than 5.5. The above indexes of this method are better than those of traditional methods, which verifies that this method has better application performance. [ABSTRACT FROM AUTHOR]

Actuated and pre-timed traffic signal controllers have been beneficial to the improvement of traffic flow in cities and dense urban environments around the world. While these methods have been effective in reducing traffic congestion, recent works have shown that incorporation of reinforcement learning (RL) or other artificial intelligence (AI) based optimization techniques may further improve the performance of traffic signal controllers. This work investigates a novel decentralized traffic signal control structure which encourages cooperative signal behavior via repeated negotiations between neighboring intelligent agents. This method capitalizes on emerging inter-infrastructure communications technologies to exercise 'system-level' control over a network of connected signalized intersections. The proposed method was tested in a simplified grid-network of 20 intersections. In this network, static arrivals of 1440 veh / l / h along east-west lanes and 360 veh / l / h along north-south lanes were supplied. In addition, a simulated shift to 720 veh / l / h along east-west lanes and 1080 veh / l / h along north-south lanes was analyzed to provide insights into the presented method's performance in response to any sudden shifts in traffic patterns. The findings indicated that, when compared to non-negotiating traffic signals, the presented method may improve the service rate of traffic networks under static conditions by 67 1 veh / h on average, reduce emissions by an average of 326 kg / h in addition to reducing travel time across a network of intersections. The performance characteristics were captured by a SUMO testbed, and computational efficiency was explored using a suite of simple testbeds developed in MATLAB. [ABSTRACT FROM AUTHOR]

With the development of Internet of Things infrastructures and intelligent traffic systems, the traffic congestion that results from the continuous complexity of urban road networks and traffic saturation has a new solution. In this research, we propose a traffic signal control scenario based on edge computing. We also propose a chemical reaction–cooperative particle swarm optimization (CRO-CPSO) algorithm so that flexible traffic control is sunk to the edge. To implement short-term real-time vehicle waiting time prediction as a collaborative judgment of CRO-CPSO, we suggest a traffic flow prediction system based on fuzzy logic. In addition, we introduce a co-factor (collaborative factor) set based on offline learning to take into account the experiential characteristics of intersections in urban road networks for the generation of strategies by the algorithm. Furthermore, the real case of Changsha County is simulated on the SUMO simulation platform. The issue of traffic flow saturation is improved by our method. Compared with other methods, our algorithm enhances the proportions of vehicles that reach their destinations on time by 13.03%, which maximizes the driving experience for drivers. Meanwhile, our algorithm reduces the driving times of vehicles by 25.34%, thus alleviating traffic jams. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization and the development of the automobile industry, the contradiction between the traffic capacity of existing urban roads and the growing traffic demand has become increasingly acute. Traffic congestion is becoming increasingly prominent. The purpose of this article is to consider the characteristics of traffic risk factors and to study the method of establishing a flexible traffic system. It can relieve traffic congestion and provide a smooth and orderly traffic environment using intelligent transportation systems to control and direct traffic flow. Based on the large urban road network, this research uses the theory of coordinated control and learning mechanism, fuzzy control, dynamic reprogramming, and other theories to study phase sequence, balance peripheral load, and overall traffic flow. It also uses on-board sensors to optimize the collection and processing of network information, decompose traffic guidance work, select the optimal route, and autonomously guide the intelligent transportation system. Under the flexible demand scheme, the average load of the trunk road is reduced by a larger degree, which is 4% lower than that of the fixed demand scheme. At the same time, the average load of the branch has increased more, which is 4% higher than that of the fixed demand scheme. It can be seen that under the elastic demand scheme, the distribution of traffic flow in the road network is more balanced and the optimization effect of relieving traffic pressure on trunk roads and improving the utilization rate of branch roads is more significant. [ABSTRACT FROM AUTHOR]

The great increase in car ownership has led to the daily recurrence of traffic congestion. Thus, traffic mobility, safety and emission concerns have become the most serious challenges for transportation researchers. To mitigate traffic congestion, a variety of proactive traffic-control strategies, such as ramp metering (RM), have been intensively investigated and deployed. With the aim of improving freeway traffic conditions, RM regulates the on-ramp flows dynamically in response to dynamic road conditions. However, most early RM strategies focus on optimising the traffic from one single aspect. This paper presents an RM control algorithm that predicts and evaluates the RM-controlled future traffic states. The impact of RM control was evaluated using a macroscopic traffic-flow model. The designed RM control algorithm possesses a multi-objective optimisation module, which improves the traffic network from the aspects of mobility, safety and emissions. The designed algorithm is evaluated through simulation and calibrated using field data collected over an 11 km major freeway stretch in Edmonton, Alberta, Canada. The comparison of the proposed algorithm-controlled scenario and the uncontrolled scenario shows that the proposed RM control algorithm can effectively relieve traffic congestion, improve safety and reduce carbon emissions concurrently. [ABSTRACT FROM AUTHOR]

The rapidly increasing traffic congestion is affecting the population of the world in a variety of ways including personal (physical and mental health), family, social, environmental, accidents and passage of emergency and law and order vehicles. Out of the many varieties of approaches tested for solutions, the current research used roadside sensors to collect data and use them for simulations using PTV Vissim simulator with MS Excel algorithmic optimisation. The location of the test was which St Marks Road in the Merton region of the UK. This is indeed an inlet into a major road (A217). A subset of data from the Department of Transport (2019) [1] was chosen which had an AADT of less than 2,000. In 2018, it was an AADT of 1,086 for this location. Simulation results showed the superiority of adaptive green signal timing over fixed timing signals. The simulation results were applied to optimisation to specify the green light timing requirements. Accordingly, congestion can be prevented using adaptive green signal timings. The results were discussed within the scope of many approaches used by different researchers. [ABSTRACT FROM AUTHOR]

As the global economy develops rapidly, traffic congestion has become a major problem for first-tier cities in various countries. In order to address the problem of failed real-time control of the traffic flow data by the traditional traffic light control as well as malicious attack and other security problems faced by the intelligent traffic light (ITL) control system, a multi-agent distributed ITL control method was proposed based on the fog computing platform and the Q learning algorithm used for the reinforcement learning in this study, and the simulation comparison was conducted by using the simulation platform jointly constructed based on the VISSIM-Excel VBA-MATLAB software. Subsequently, on the basis of puzzle difficulty of the computational Diffie–Helleman (CDH) and Hash Collision, the applicable security control scheme of ITL under the fog computing was proposed. The results reveal that the proposed intelligent control system prolongs the time of green light properly when the number of vehicles increases, thereby reducing the delay time and retention rate of vehicles; the security control scheme of ITL based on the puzzle of CDH is less efficient when the vehicle density increases, while that based on the puzzle of Hash collision is very friendly to the fog equipment. In conclusion, the proposed control method of ITL based on the fog computing and Q learning algorithm can alleviate the traffic congestion effectively, so the proposed method has high security. [ABSTRACT FROM AUTHOR]

The high growth of vehicular travel in urban areas, in particular, requires a traffic control system that optimizes traffic flow efficiency. Traffic congestion can also occur by large de-lays in Red Light etc. The delay in lighting is difficult to code and does not rely on real traffic density. It follows that traffic controls are simulated and configured to better meet this rising demand. So, in order to avoid the traffic control problem, the Adaptive Intelligent Traffic Light control system (AITLCS) has been proposed based on OpenCV and Image processing technique. The system proposed is designed to ensure smooth and efficient traffic flow for daily life as well as emergency and public transportation safety. Based on the road density instead of the levels set the proposed system provides the timing for the traffic light signal so that a highly loaded side switched on over long periods compared with the other lanes. It can also be used at an intersection with traffic signs, which controls the traffic light signal at the intersection. If timers are smart to predict the exact time, the system is more efficient because it reduces the time spent on unintended green signal significantly. With the help of OpenCV software, this paper aims to have a signal management SMART solution that will be cost-effective at the end. The system consists of a camera facing a lane taking pictures of the route we want to travel and then the density of the pedestrian and vehicle is taken and compared with each image employing image processing. Such images are processed effectively to learn the density of traffic. [ABSTRACT FROM AUTHOR]

For intersection traffic congestion phenomenon, combined fog computing and reinforcement learning theory, this paper proposed a traffic light control model FRTL. The model performed intelligent coordinated control of traffic lights based on real-time traffic flow information. The fog node uploaded the collected real-time traffic flow information to the fog server, and the fog server realized information sharing on the fog platform, and the fog platform combined the processed shared data and Q learning to formulate a traffic light control algorithm. The algorithm used the detected real-time traffic data to calculate a suitable traffic light timing scheme, which was finally applied to the traffic light. The simulation results show that compared with the traditional time-phase control method and the main road control method (ATL), the FRTL control method improves the throughput of intersections, reduces the average waiting time of vehicles, and achieves the goal of properly regulating traffic lights and alleviating traffic congestion. [ABSTRACT FROM AUTHOR]

Abstract: This research presents the application of intelligent computing models in Air Traffic Flow Management (ATFM). Firstly, multi-agent system in grid computing environment is applied to deal with the problem of ATFM synchronization. The developed system consists of software agents, which are implemented in a Computational Grid platform for congestions identification, conflicts resolution and agreements negotiation among the participating airports. A metric criterion, called Agent’s Balancing Standard (ABS), is used as a basic index to measure the effectiveness of reducing both the amount of communication among agents and the delay of flights. Secondly, a brief discussion about the Meta-Level Control model is introduced in ATFM issues to improve the efficiency of communication among the agents. The system is developed to analyze the traffic flow information received, identify its importance and process it in the most adequate order. [Copyright &y& Elsevier]