Vision Based Adaptive Traffic Signal Control System Development

Traffic lights are installed at intersections mostly for traffic management. Traffic signals turn on during the amount of time determined. Intelligent traffic management systems emerge as a need to handle the dynamicity of traffic. These systems are first implemented on simulators in order to mimic the real life situations before realization.Yet, we have implemented a real time traffic simulator with an adaptive fuzzy inference algorithm that arranges the foreseen light signal duration. It changes the time duration of lights depending on waiting vehicles behind green and red lights at crossroad. The simulation has also been supported with real time graphical visualization. Given a scenario, it creates random traffic flows according to specified parameters. Next, obtained results have been interpreted in the simulation environment.According to inferences from adaptive environment, TSK (Takagi-Sugeno-Kang) and Mamdani models have also been implemented to give baselines for verification. Several experiments have been conducted and compared against classical techniques such as Webster (1958) Road research technical paper No 39 and HCM (2000) TRB, special report 209, statistically to demonstrate the effectiveness of the proposed method.

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“…Various algorithms have been developed for traffic management in the literature [7][8][9][10][11][12][13][14][15][16][17].…”

Section: Problem Definition and Motivationmentioning

confidence: 99%

A real time traffic simulator utilizing an adaptive fuzzy inference mechanism by tuning fuzzy parameters

2011

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“…[12]. An investigation on the vision based surveillance is discussed here [13]. The findings of an area verification [22] algorithm show that the static actuated-coordinated system timings are better than the dynamic one.…”

Section: Introductionmentioning

confidence: 99%

A generic model for camera based intelligent road crowd control

Zaman¹,

Ali²,

Rizman³

2018

J. Fundam and Appl Sci.

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Traffic flow control is often a big problem in many big cities in the world, especially during the peak and off-peak hours. Researchers are trying to find the optimal solution to solve this daily problem. Often, the problem is caused by the poor traffic signal light control system. Improper placement of the signal light and timing is the main issue. The problem can be solved by proper time management for the traffic signal through the congested and often over crowded areas. This research proposes a model for intelligent traffic flow control by implementing camera based surveillance and feedback system. A series of cameras are set minimum three signals ahead from the target junction. The complete software system is developed to help integrating the multiple camera on road as feedback to the signal light control systems.

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“…Vision-based vehicle detection systems have been used for various applications such as intelligent vehicles, driver assistance systems [1], [2], autonomous vehicles [3]- [5], traffic monitoring systems [6]- [8], traffic-signal control systems [9], [10], and so on.…”

Section: Introductionmentioning

confidence: 99%

State Machine and Downhill Simplex Approach for Vision-Based Nighttime Vehicle Detection

Choi

Kim

et al. 2014

ETRI J

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In this paper, a novel vision-based nighttime vehicle detection approach is presented, combining state machines and downhill simplex optimization. In the proposed approach, vehicle detection is modeled as a sequential state transition problem; that is, vehicle arrival, moving, and departure at a chosen detection area. More specifically, the number of bright pixels and their differences, in a chosen area of interest, are calculated and fed into the proposed state machine to detect vehicles. After a vehicle is detected, the location of the headlights is determined using the downhill simplex method. In the proposed optimization process, various headlights were evaluated for possible headlight positions on the detected vehicles; allowing for an optimal headlight position to be located. Simulation results were provided to show the robustness of the proposed approach for nighttime vehicle and headlight detection.

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Vision Based Adaptive Traffic Signal Control System Development

Cited by 10 publications

References 5 publications

A real time traffic simulator utilizing an adaptive fuzzy inference mechanism by tuning fuzzy parameters

A real time traffic simulator utilizing an adaptive fuzzy inference mechanism by tuning fuzzy parameters

A generic model for camera based intelligent road crowd control

State Machine and Downhill Simplex Approach for Vision-Based Nighttime Vehicle Detection

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