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Research on the Comparison and Improvement of Road Traffic Black-Spots Identification Methods

Author: WuLiNa
Tutor: PeiYuLong
School: Harbin Institute of Technology
Course: Traffic Information Engineering \u0026 Control
Keywords: Accident-prone points Methods of identification Fuzzy Clustering Programming
CLC: U491.31
Type: Master's thesis
Year: 2007
Downloads: 451
Quote: 6
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Abstract


Road traffic accidents was listed as one of the world public nuisance, only our nearly two thousand casualties a day, pose a great threat to the people's production and life. Therefore, based on the characteristics of road traffic accidents, the study reduce the number and severity of road traffic accidents, reduce the economic losses resulting methods and means, thereby increasing the level of road traffic safety is a very urgent and important research work . Road traffic accidents with multiple points less share of road length, but to concentrate a larger proportion of traffic accidents, The research road accident blackspots characteristics, help reveal the cause of the accident and characteristics, so as to obtain the regularity of the accident. Correctly identify accident-prone points and targeted improvement measures taken to minimize the number of road accident and the severity of the target to the minimum investment. Comprehensive analysis of typical road traffic accidents at home and abroad multiple methods of identification papers, comparative study of methods of identification selected technical indicators, applicable conditions, advantages and disadvantages, and accident-prone the point identification methods are classified. Consider the severity of the accident, the number of accidents into the equivalent number of accidents, analysis of the statistical distribution of the equivalent number of accidents, and improved quality control. Departure from the angle of the accident prediction, identify technical indicators is selected using the fuzzy statistical method to determine the membership function, using the Delphi method to determine the the indicators right of weight coefficients, thus fuzzy evaluation method to improve; based on the principle of data mining is proposed to identify accident-prone point of fuzzy clustering method. Multiple methods of identification of typical accident computer programming. Finally, improved quality control, improved fuzzy evaluation method based on fuzzy clustering analysis, accident-prone points to identify instances analysis. The research results obtained by the paper: According to the accident-prone point authentication method selected to identify technical indicators, accident-prone point identification method is divided into the law of the number of accidents, the accident rate law and law and summarizes the applicable conditions and the advantages and disadvantages; application Weibull distribution fitting equivalent number of accidents, and to amend the accident rate and thus improve the quality control method; fuzzy evaluation method to improve road traffic safety, for the identification of potential accident-prone points; proposed fuzzy clustering method to identify accident-prone points; development road accident blackspots identification systems.

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CLC: > Transportation > Road transport > Technical management of traffic engineering and road transport > Traffic engineering and traffic management > Traffic Accidents > Dealing with traffic accidents,analysis and statistics
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