The Optimization Layout Method of Intelligent Roadside Sensor System in Traffic Management and Control

Dong Chu Cui; Yue Yu

doi:10.4028/www.scientific.net/AMR.591-593.1251

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 591-593The Optimization Layout Method of Intelligent...

The Optimization Layout Method of Intelligent Roadside Sensor System in Traffic Management and Control

Abstract:

The vehicle sensor information and road side sensor information will be collaborative used in traffic management and control. In order to improve the comprehensiveness and economy of the traffic and road conditions’ information collection, we focus on the intelligent roadside system in this paper. Firstly, we analyse the functions of the intelligent roadside system. Through the analysis of the detection range, detection accuracy, price and applicable conditions of similar sensor, we delineate the selection range of the intelligent roadside sensor. Then we determine the layout scheme of the testing equipment sensors for different functions according to different types of network structure. Finally, we apply similarity analysis to optimize the configuration density to reduce system cost by selecting the sensor layout-intensive sections.

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Periodical:

Advanced Materials Research (Volumes 591-593)

Pages:

1251-1255

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.591-593.1251

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Online since:

November 2012

Authors:

Dong Chu Cui, Yue Yu

Keywords:

Configuration Density Optimization, Cooperative Infrastructure, Cooperative Vehicle, Intelligent Roadside System, Sensor

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References

[1] JIANG Gui-yan. Technologies and Applications of the Identification of Road Traffic Conditions[M]. Beijing: China Communications Press, (2004)

Google Scholar

[2] Dharia A, Adeli H. Neural network model for rapid forecasting of freeway link travel time[J]. Engineering Applications of Artificial Intelligence, 2003, 16 (7/8): 607-613

DOI: 10.1016/j.engappai.2003.09.011

Google Scholar

[3] Yu De-xin, Yang Zhao-sheng, Liu Xue-jie. Travel Time Indirect Prediction Method of Urban Traffic Flow Guidance System [J]. Computer and Communications, 2006, 24(6): 18-21

Google Scholar

[4] LIU Hao, Zhang Xiao-liang, Zhang Ke. Travel Time Prediction for Urban Arterials Based on Rough Set [J]. Systems Engineering Theory & Practice, 2008, 25(10): 117-122

Google Scholar

[5] Seri Park. Vehicle monitoring for traffic surveillance and performance using multi-sensor data fusion [D]. Doctor dissertation of University of California, Irvine, (2004)

Google Scholar