Traffic Light Intersections Research Articles

The Design of Dual-Mode Traffic Lights Control System Based on FPGA

The traffic signal lights control system automatically controls the statues of the intersection traffic lights and dispatch the traffic vehicles normally that traffic scheduling is an important system and it is the most basic and most important tools in city traffic management. Traffic lights control system made by single chip microcomputer technology has a single working model, low reliability, slow speed, and it can't modify or upgrade online, which is difficult to adapt to changes at different times of the traffic flow. This design of the dual mode of traffic lights control system intelligently controls each intersection traffic lights on and off time in the different period, which will improve the scheduling efficiency of road intersections. The design is based on FPGA with a digital display of traffic lights control system, compile the program design and simulation in Quartus II development environment, and ultimately download to EP2C35F672C6 chip of Altera Company. By the DE2 experiments test platform, the system has stable performance, simple operation, easy to maintenance, high reliability, strong expansibility and good refactoring.

Realizing or Relinquishing Rights? Homeless Youth, Their Life on the Streets and Their Knowledge and Experience of Health and Social Services in Hillbrow, South Africa

Poverty and youth unemployment are critical issues in South Africa with homeless persons begging at traffic light intersections in all major cities. Support services represent one way of empowering homeless youth. The study therefore examined the experiences of 10 homeless young adult males in Hillbrow, Johannesburg and whether they were aware of local health and social services. Qualitative interviews revealed that participants experienced poor health, addiction, physical violence, psychological trauma, and public hostility. Despite limited education, they were aware of and utilized local health and social services. Findings are discussed in terms of their implications for social work.

OPTIMIZATION OF URBAN MULTI-INTERSECTION TRAFFIC FLOW VIA Q-LEARNING

Congestions of the traffic flow within the urban traffic network have been a challenging task for all the urban developers. Many approaches have been introduced into the current system to solve the traffic congestion problems. Reconfiguration of the traffic signal timing plan has been carried out through implementation of different techniques. However, dynamic characteristics of the traffic flow increase the difficulties towards the ultimate solutions. Thus, traffic congestions still remain as unsolvable problems to the current traffic control system. In this study, artificial intelligence method has been introduced in the traffic light system to alter the traffic signal timing plan to optimize the traffic flows. Q-learning algorithm in this study has enhanced the traffic light system with learning ability. The learning mechanism of Q-learning enables traffic light intersections to release itself from traffic congestions situation. Adjacent traffic light intersections will work independently and yet cooperate with each others to a common goal of ensuring the fluency of the traffic flows within the traffic network. The simulated results show that the Q-Learning algorithm is able to learn from the dynamic traffic flow and optimize the traffic flow accordingly.

Network-based strategies for signalised traffic intersections

In this paper, different traffic light control structures over communication links, including the decentralised, quasi-decentralised, distributed and hierarchical networked structures, are considered. These structures used for coordinating multiple intersections, which could be a great application of networked control problem control for the signalised traffic light intersections that will help the designer to achieve certain objectives. Some of these objectives are, minimise the waiting time during the red light period and perform better control in the next green cycle, maximise the flow between consecutive intersections which will minimise the number of stops, minimise the average waiting time and more will be highlighted in this paper. A quick literature about all models used for traffic control problem is done. A generic state space model of traffic dynamics under these different control structures is proposed that takes into account many effects of lossy communication links such as networked induced delays, packet dropout and varying sample interval. Also, a sufficient condition for system stability is provided based on LMI. Finally, comparison of different types of networked control systems was done using MATLAB simulation.

Intelligent Traffic Light System to Prioritized Emergency Purpose Vehicles Based on Wireless Sensor Network

The use of Wireless Sensor Network (WSN) has proved to be a very beneficial in the design of adaptive and dynamic traffic light intersection system that will minimize the waiting time of vehicles and also manage the traffic load at the intersection adaptively. In this paper, we propose an adaptive traffic intersection system based on Wireless Sensor Network where the traffic light of one intersection can communicate with the traffic light of the next neighboring intersections and traffic clearance will be prioritized for special vehicles with the help of sensors. General Terms: Wireless Sensor Network.

A novel neuro-cognitive approach to modeling traffic control and flow based on fuzzy neural techniques

In many developed and developing countries, efficient monitoring and controlling of the city’s traffic have become major challenges. Conventional traffic light control methods, Preset Cycle Timing and Preset Cycle Timing with proximity sensors used today are neither sufficiently efficient nor effective to manage different traffic conditions. One solution is to employ a human operator. Unfortunately this method is expensive and error-prone due to lapse in concentration and other factors. Another alternative is to introduce an intelligent controller using fuzzy neural learning memory techniques, which have the capability to mimic human intelligence in controlling the frequency of traffic light changes at a junction. The performances of four suitable soft-computing architectures are investigated in this study as a possible platform to model and develop an intelligent traffic light control regime. These neural fuzzy learning structures construct memories that possess the intelligence and capabilities of a human operator in monitoring and managing the traffic at road intersections under different traffic scenarios. An open source traffic light simulator, Green Light District, is used to create and simulate different traffic conditions at (i) a simple traffic light intersection and (ii) a complex traffic light intersection. Traffic data generated by the simulator under the control of a human operator is then used as inputs for the training and testing of four fuzzy neural network architectures. The four architectures are Generic Self-organizing Fuzzy Neural Network (GenSoFNN), Pseudo Outer Product based Fuzzy Neural Network (POPFNN), Fuzzy Adaptive Learning Control Network (Falcon) and Multilayer Perceptrons (MLP). The performance of each of the neural network architectures was found to be promising from the simulation results derived for both simple and complex traffic light intersections. Performance was based on the mean classified rate, mean training time, mean number of rules, and standard deviation of the classified rate across the traffic conditions simulated. A technique from each of the architectures with the best results is subsequently selected for more in-depth study on its performance in a complex traffic light intersection. Although all the selected techniques from the four architectures suffered a decline in performance in the complex traffic light intersection; architectures such as GenSoFNN and Falcon continue to produce good results. The POPFNN architecture generated a large number of rules and the MLP architecture produced poor classified rates. This work has demonstrated that it is highly feasible to develop neuro-cognitive traffic control regime that can mimic the behaviors of a human operator.

The Application of Detectors in Determining the Safety Intervals at Intersections with Traffic Lights

The paper presents a model of a detector-controlled trafficlight intersection as a complex traffic system. By analysing thekey factors of safety, the preliminary guidelines are given for theimplementation of the presented solution. The special emphasisis on defining the safety intervals as significant safety factorsin passing through an intersection.

Energy conservation in traffic

This paper discusses the energy that is being wasted in the transport section in Amman due to three main factors: namely, the time delay at the major traffic light intersections, the warming up periods of vehicles, and the drag force acting on heavy vehicles. It was found that the fuel which is being used due to the first and second factors contribute 2.2 and 3.0% to the total annual fuel cost in Jordan. Finally, recommendations and suggestions are given to cut down the fuel consumption in Amman due to the above mentioned factors.