Urban Traffic Monitoring System

Objective: In the traffic monitoring system, the detection of stirring vehicles is monitored by fitting staticcameras in the traffic scenarios. The background subtraction, a commonly used method detaches poignant objects in the foreground from the background. The method applies a Gaussian Mixture Model, which can effortlessly be contaminated through slow-moving or momentarily stopped vehicles. For decades traffic vehicle-monitoring system follows fixedcamera surveillance for recording and extracting useful information. Calculating the number of Gaussian Models pixelwise the processing time of the observed scene can be calculated. However, an effective method to describe the smooth behavior of traffic scenes to handle critical situations is required. This paper proposes the method to effectively combine, track, and classify the traffic vehicles, resolving the contamination problem that occurred by slow-moving or momentarily stopped vehicles. Methods: The present study proposes an Enhanced Gaussian Mixture Model to overcome the addressed issue, efficiently detecting vehicles in complex traffic scenarios. The model was evaluated with experiments conducted using real-world on-road travel videos. Results: Compared with the existing GMM model to show contamination avoidance of vehicles that are motionless for a time gap. Conclusion: The findings present an improvement in the image processing technique for processing effective video scenes to eliminate frictional and noise variations. The Enhanced Gaussian Mixture Model shows a better accuracy of 0.9759 when compared with the existing state-of-the-art model and avoids contamination of slow-moving or momentarily stopped vehicles.

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A Participatory Urban Traffic Monitoring System: The Power of Bus Riders

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2017.2650215 ◽

2017 ◽

Vol 18 (10) ◽

pp. 2851-2864 ◽

Cited By ~ 15

Author(s):

Zhidan Liu ◽

Shiqi Jiang ◽

Pengfei Zhou ◽

Mo Li

Keyword(s):

Monitoring System ◽

Traffic Monitoring ◽

Urban Traffic

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Low rate DDoS mitigation using real-time multi threshold traffic monitoring system

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-020-02744-y ◽

2021 ◽

Author(s):

M. Baskar ◽

J. Ramkumar ◽

C. Karthikeyan ◽

V. Anbarasu ◽

A. Balaji ◽

...

Keyword(s):

Real Time ◽

Monitoring System ◽

Traffic Monitoring ◽

Low Rate

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Wireless Sensor Network-Based Urban Traffic Monitoring Using Inertial Reference Data

2015 International Conference on Distributed Computing in Sensor Systems ◽

10.1109/dcoss.2015.21 ◽

2015 ◽

Cited By ~ 3

Author(s):

Mustafa Mousa ◽

Mohammed Abdulaal ◽

Stephen Boyles ◽

Christian Claudel

Keyword(s):

Wireless Sensor Network ◽

Sensor Network ◽

Reference Data ◽

Traffic Monitoring ◽

Urban Traffic ◽

Wireless Sensor

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Traffic Monitoring and Congestion Management in the SCOOT Urban Traffic Control System

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1634-15 ◽

1998 ◽

Vol 1634 (1) ◽

pp. 118-122 ◽

Cited By ~ 7

Author(s):

David Bretherton ◽

Keith Wood ◽

Neil Raha

Keyword(s):

Control System ◽

Traffic Control ◽

Detection System ◽

Congestion Management ◽

Traffic Monitoring ◽

Urban Traffic ◽

Incident Detection ◽

The European Union ◽

Traffic Control System ◽

Urban Traffic Control

The SCOOT Urban Traffic Control system is now operating in over 170 cities worldwide, including 7 systems in North America. Since the first system was installed, there has been a continuous program of research and development to provide new facilities to meet the requirement of the traffic manager. The latest version of SCOOT (Version 3.1) incorporates a traffic information database, ASTRID, and an incident-detection system, INGRID, and provides a number of facilities for congestion control. The traffic monitoring facilities of SCOOT, including a new facility to estimate emissions from vehicles, and the current program of work to enhance the incident-detection system and to provide additional facilities to manage incidents and congestion are reported in this paper. The work is being carried out as part of the European Union, DGXIII 4th Framework project, COSMOS, with additional funding from the UK Department of Transport. The enhanced system is to be installed in the Kingston Borough of London, where it will be tested in combination with congestion warning information provided by variable message signs.

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