Intelligent Traffic Light Control Research Articles

The Application of The Double Queue Asymmetric Gated Service Polling Control Theory in Intelligent Traffic System

paper presents a new use of double queues asymmetric gated service polling system in the intelligent traffic light control system.Usually there are more vehicles in main road than minor road,so there are more green light time be needed in the main road.From the computer simulation and theory analysis,we can find that the application of double queues asymmetric gated service polling theory in intelligent traffic system can balance intersections load and set suitable passing time for vehicles to assure the roads open.

The Application of the Exhaustive Polling Theory in Intelligent Traffic System

This paper presents a new use of exhaustive service polling system in the intelligent traffic light control system.Vehicles arrival rate is measured in the system. Through the relationship between arrival rate and mean waiting time ,mean queue length in exhaustive service polling system, achieved an technology that intelligent adjust the length of traffic light time according to the arrive rate.The more arrive rate the longer green light time. With the intelligent control, the road capacity is more.

WSN based Cost Effective Intelligent Traffic Light Control System based on Image Processing

In this paper image processing based intelligent traffic light control system is designed. Inductive loop method is mostly used for traffic detection. But it is physically large, hard to install and maintain. Present system has fixed timing for traffic control so the present traffic system is not much efficient to manage or control the traffic. Also inductive loops cannot communicate with each other. The wireless sensor network (WSN) has features like fault tolerance, scalability, real-time, and coordination. So the traffic light control system as per the traffic density with WSN has been designed in this paper. C++ programming language is used for simulation.

Real-Time Intelligent Traffic Light Monitoring and Control System to Predict Traffic Congestion Using Data Mining and WSN

Real-Time Intelligent Traffic Light Monitoring and Control System to Predict Traffic Congestion Using Data Mining and WSN

Design of Intelligent Traffic Light Control System Based on Multi CPU

The intelligent traffic light control is the core problem in the intelligent traffic research field, in order to solve this problem, the intelligent traffic light control system is proposed based on multi CPU. In a multi processor system, aiming at the intelligent traffic light, the reasonable control is taken. In the intelligent traffic light control system, the related principles of multi processor system design and shared memory are elaborated in detail. The BP neural network self-tuning PID control algorithm is applied in the traffic lights control process, reasonable control of traffic lights is obtained. The experiment results show that the principle is applied in the intelligent traffic light control system, it can greatly improve the control accuracy, so it can meet the actual demand of intelligent traffic management.

The Application of Polling in Intelligent Transportation System

This paper presents a new use of polling system[1] in the intelligent traffic light control system[2]. vehicle arrival rate is measured in this system.Through the relationship between arrival rate and waiting time in polling system, achieved an technology that different arrival rate correspond to different length of time[3] of traffic light.With the intelligent control, we can well solve the problem of traffic jams during different periods.

An Advanced Fuzzy Logic Based Traffic Controller

Traffic light plays an important role in the urban traffic management. Therefore, it is necessary to improve the traffic controller for effective traffic management and better traffic flow leading to greener environment. In this paper, an advanced and intelligent traffic light controller is proposed, utilising the fuzzy logic technology and image processing technique. A fuzzy logic control has been implemented to provide the attribute of intelligence to the system. For real-time image acquisition, the process is further linked to the fuzzy logic controller which generates a unique output for each input pattern. Here image processing and fuzzy logic tool boxes of MATLAB are used where the final output is sent to Peripheral Interface Controller (PIC) microcontroller to drive the traffic signals in the desired manner. The results obtained show an improvement of 44% in the overall outcome of traffic management as compared to the conventional traffic controller, marking great feasibility and practicality of the current model.

Intelligent Traffic Light Controller Based on MCA Associative Memory

Traffic in urban areas is mainly regularized by traffic lights, which may lead to the unnecessary long waiting times for vehicles if not efficiently configured. This inefficient configuration is unfortunately still the case in a lot of urban areas where most of the traffic lights are based on a ‘fixed cycle’ protocol. This paper aims to design an intelligent controller of an intersection in a specific city using associative memory with multi-connect architecture via using this structure of neural network the intelligent controller can adapt to all street cases, which may be faced during its work. Not like other controllers, this work uses small associative memory. It will learn all street traffic conditions. The controller uses virtual data about the traffic condition of each street in the intersection. Thus, in an image processing module this video camera will provide visual information. This information will be processed to extract data about the traffic jam. This data will be represented in a look- up table, then smart decisions are taken when the intersection management determines the street case of each street at the intersection based on this look- up table.

Research of Intelligent Traffic Light Control System Design Based on the NI ELVIS II Platform

In this paper, introduces a crossroads two-way intelligent traffic light control system based on the virtual instrument technology. The programming language Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW) is used and the NI Educational Laboratory Virtual Instrumentation Suite II (NI ELVIS II) platform is chosen as target device in designing this control system. The control kernel of the system is LabVIEW program, it can realize lots of functions, such as adjusting the passing time in all directions and the duration of the yellow light, passing time count down and treatment of abnormal traffic. The experiment proves, NI ELVIS II platform than traditional experimental teaching equipment more innovative, flexibility and practicality, be helpful for students in the field of innovation ability training.

Algorithm development of an intelligent traffic light control system.

This study presents an implementation of an intelligent traffic light control system for a crossroads based on combination of image processing and fuzzy controller. After certain seconds of red light, traffic densities are estimated from captured images. Based on this estimated result, a fuzzy controller determines the length of the next green light period in order to generate longer period for higher density road, vice versa. A software was developed to evaluate the suggestion algorithm, and to compare it with traditional timer-based traffic light system. Simulation results indicate that the road with higher traffic flow has a longer green light period, and vice versa. When the traffic’s densities of 2 roads are 5 times different, the vehicle red-light waiting times of the suggested system are reduced from 15 to 20 times better than the traditional system.

PRIORITY BASED TRAFFIC LIGHTS CONTROLLER USING WIRELESS SENSOR NETWORKS

Vehicular traffic is continuously increasing around the world, especially in large urban areas. The resulting congestion has become a major concern to transportation specialists and decision makers. The existing methods for traffic management, surveillance and control are not adequately efficient in terms of performance, cost, maintenance, and support. In this paper, the design of a system that utilizes and efficiently manages traffic light controllers is presented. In particular, we present an adaptive traffic control system based on a new traffic infrastructure using Wireless Sensor Network (WSN). These techniques are dynamically adaptive to traffic conditions on both single and multiple intersections. An intelligent traffic light controller system with a new method of vehicle detection and dynamic traffic signal time manipulation is used in the project. The project is also designed to control traffic over multiple intersections and follows international standards for traffic light operations. A central monitoring station is designed to monitor all access nodes..

Speed Range Prediction for Traffic Light Control System

This paper discuses a proposed systems for acquiring the next intersection timing and generating the required speed at current intersection to cross next intersection without stopping at it. The system is speed module for next intersection prediction embedded in intelligent traffic light control system at intersection. It can also be designed for GPS based navigation system. For efficiently predicting the time and speed required for crossing next intersection without stopping at it centralized approach is taken into account, the distance between current intersection and next intersection and traffic signal timings of next intersection are considered as input to the system. The traffic signal timings are more on highway than on city road. System then generates the required speed in range to cross next intersection without stopping at it. Speed generated by the system is in specified range like 32Km/Hr to 40 Km/Hr. Also it can’t exceed the speed limit of road.

Design and Implementation of Intelligent Traffic Control System Based on Mathematica

Intelligent traffic light control system based on fuzzy control was designed and the implementation of it was also discussed. The system can alter the signal light time according to the number of automobile waiting for passage. The simulation based on Mathematica software show that this method has better effect than traditional way in increase the automobile traffic efficiency and energy saving, what’s more it can adapt to complex traffic conditions.

Modeling and analysis of urban traffic light control systems

Timed Coloured Petri nets (TCPN) have been utilized as visual formalism for the modeling of complex systems. This paper focuses on the use of TCPNs to model an intelligent urban traffic light control system. The intelligent traffic light controller proposes a solution to regulate the problem of traffic congestion. For this purpose, this paper also presents an intelligent control methodology which is constructed by the alternation of two different sub‐models.

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.