Time Optimization for Traffic Signal Control Using Genetic Algorithm
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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Article: Time Optimization for Traffic Signal Control Using Genetic Algorithm
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- "Reviewing some of works published, Barba et al. designed a smart city framework for VANETs that includes intelligent traffic lights that transmit warning messages and traffic statistics. Singh et al. used a genetic algorithm and traffic emulator, developed in JAVA, to represent dynamic traffic conditions. Lozano  presented an overview image of processing and analysis tools used for traffic applications on traffic monitoring and automatic vehicle guidance . "
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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- "A commonly used RL algorithm is Q-learning which is a model-free RL algorithm. 8 Algorithm-based models: the famous algorithm used is the genetic algorithm (Leena et al., 2009) that uses the rules of nature and it is great advantage is that solution through evolution, but this is also the greatest disadvantage because it not necessarily to evolve towards a good solution and may evolve into an evolutionary dead end. 9 Fuzzy logic models: Fuzzy logic (Zhang et al., 2007; Zhao et al., 2009), offers a formal way of handling terms like more, less, longer, …, etc. "
ABSTRACT: 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.International Journal of Systems Control and Communications 05/2013; 5(1):15-48. DOI:10.1504/IJSCC.2013.054139
- "In the past, some researchers have modified and improved the conventional GA in order to produce better optimization algorithm. Others applied GA into robotics research , gaming  , stock market prediction , bidding strategy , weather prediction , traffic monitoring and control . GA requires selection, crossover and mutation operators. "
Conference Paper: A comparison on the performance of crossover techniques in video game[Show abstract] [Hide abstract]
ABSTRACT: This paper describes the performance of four crossover operators used in evolving the required controllers in a video game. The crossover operators used in this research are the two-point crossover, the uniform crossover, the N-point crossover, and the single-point crossover. The performance of these crossover methods were tested using Infinite Mario Bros game. This video game was chosen due to the dynamicity and complexity of the game. This paper also presents a newly designed nondeterministic based Finite State Machine (FSM) method. The Mario character uses the proposed FSM as its strategy in the game. The proposed FSM is then optimized using a modified Genetic Algorithm (GA). The results showed that the required controllers were generated successfully using the proposed method. The results also showed that the N-point crossover performed well compared to the uniform crossover, the two-point crossover and the single-point crossover methods.Control System, Computing and Engineering (ICCSCE), 2013 IEEE International Conference on; 01/2013