Time Optimization for Traffic Signal Control Using Genetic Algorithm

Lecturer in Computer Sc. & Engg. deptt, Amity School of Engg. & Tech, New Delhi, India; Amity School of Engg. & Tech, Lecturer in Instrumentation &Control Engg. deptt, New Delhi, India; Amity School of Engg. & Tech, Student of Computer Sc. & Engg. deptt, New Delhi, India
LETTERS International Journal of Recent Trends in Engineering 12/2009; 2.

ABSTRACT In this paper, a "real-time" traffic signal control strategy is provided using genetic algorithms to provide near-optimal traffic performance for intersections. Real-time traffic signal control is an integral part of the urban traffic control system and providing effective real-time traffic signal control for a large complex traffic network is an extremely challenging distributed control problem. The developed "intelligent" system makes "real-time" decisions as to whether to extend green time for a set of signals. The model is developed using genetic algorithm implemented in MATLAB. A traffic emulator is developed in JAVA to represent dynamic traffic conditions. The emulator conducts surveillance after fixed interval of time and sends the data to genetic algorithm, which then provides optimum green time extensions and optimizes signal timings in real time. The optimization parameters are -total number of vehicles in a road and importance of the road in the intersection. In the end, by comparing the experimental result obtained by the fixed time and real time based traffic systems which improves significant performance for intersections, we confirmed the efficiency of our intelligent real time based control system.

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    ABSTRACT: Traditionally, only basic signal timings have been optimized in order to minimize delays and stops of private vehicles. Transit Signal Priority parameters and subsequent beneficiaries, such as transit vehicles and passengers, are usually neglected in the signal timing optimization process. Little research has been done to reveal specific benefits of optimizing Transit Signal Priorities and whether consideration should be given to both private vehicles and others when optimizing signal timings. Research presented here tests optimization of three performance measures (auto delay, transit delay, and person delay) by adjusting signal timings in different ways. A Genetic Algorithm formulation, coupled with a high-fidelity microsimulation model, is used to investigate benefits of each optimization on a large urban traffic corridor. The results show that basic signal timings are the most important measure to optimize when transit and private cars share a corridor. Also, the findings show that personal delay represents a suitable objective function for optimization of signal timings.
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    ABSTRACT: the use of wireless sensor network in the smart traffic control systems is very beneficial and starting to be very promising in the design and implementation for such systems. It will help in saving people time and adapt the intersections traffic lights to the traffic loads from each direction. In this paper we present an intelligent traffic signals control system based on a wireless sensor network (WSN). It uses the vehicle queue length during red cycle to perform better control in the next green cycle. The main objective is to minimize the average waiting time that will reduce the queues length and do better traffic management based on the arrivals in each direction. The system also includes an approach to alert the people about the red light crossing to minimize the possibility of accidents due to red light crossing violations. The system was simulated and results are shown in the end of this paper.
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    ABSTRACT: In this paper, an intelligent traffic light that controls the flow of traffic was introduced. The proposed system detects the level of congestion and the abnormal situations in two main highways and four intersections. The system collects the data and the information from a video imaging system, which captures and interprets images to detect, and count of the vehicles This data will be sent to another system based on genetic algorithm. This system makes a real time decision that determines the interval of green light time for each traffic light at each intersection. This system is simulated theoretically and the results indicate the efficiency of the system. We suggest other inputs from VANET techniques and Mobile track system, which can be added to the system, to improve its output.  Index Terms— Intelligent traffic ligh; Genetic algorith; VANET; and Video image detection system.

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May 21, 2014