基于互联网数据城市快速路地点安全分析方法
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摘要
现有的城市交通安全分析主要考虑人财物的直接损失,却忽略了事故产生的交通延误等间接损失,同时也较少利用互联网海量数据进行分析.本文建立了基于互联网文本数据的城市交通事故属性模型,采用模糊系统聚类法划分事故交通影响等级,构建了基于绝对事故次数、损害后果和交通影响的等效事故次数模型,并将其应用于累积频率曲线和K-means聚类的城市快速路地点安全组合评价方法中.北京市快速路地点安全评价结果表明,本文所提出的方法可有效地将互联网安全文本数据应用于城市交通安全分析中,分析结果可为城市交通安全管理提供有益的借鉴.
Most urban traffic safety analysis considered accident's direct losses, but neglected accident's indirect losses such as traffic delay, and seldom used internet big data. Based on internet text data, a traffic accident attribute model of urban expressway is established, and the accident impact rank of traffic flow is classified by the fuzzy system clustering method. An equivalent accident frequency model considering absolute accident frequency,accident consequence and accident impact to traffic flow is proposed and used to location safety combined evaluation of urban expressway based on cumulative frequency curve and K-means clustering method. The evaluation results of Beijing expressway location safety show that the method could effectively apply the internet safety text data to urban traffic safety analysis and the evaluation conclusions could provide the beneficial references for urban traffic safety management.
Most urban traffic safety analysis considered accident's direct losses, but neglected accident's indirect losses such as traffic delay, and seldom used internet big data. Based on internet text data, a traffic accident attribute model of urban expressway is established, and the accident impact rank of traffic flow is classified by the fuzzy system clustering method. An equivalent accident frequency model considering absolute accident frequency,accident consequence and accident impact to traffic flow is proposed and used to location safety combined evaluation of urban expressway based on cumulative frequency curve and K-means clustering method. The evaluation results of Beijing expressway location safety show that the method could effectively apply the internet safety text data to urban traffic safety analysis and the evaluation conclusions could provide the beneficial references for urban traffic safety management.
引文
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[2]SANDHU H A S,SINGH G,SISODIA M S,et al Identification of black spots on highway with kerne density estimation method[J].Journal of the Indian Society of Remote Sensing,2016,44(3):457-464.
[3]DERELI,MEHMET ALI,ERDOGAN,et al.A new model for determining the traffic accident black spots using GIS-aided spatial statistical methods[J]Transportation Research Part A:Policy and Practice2017(103):106-117.
[4]CHA MINKI,KWON JUNG-HYOK,LEE SOL-BEEet al.Integrated framework for keyword-based text data collection and analysis[J].Sensors and Materials,201830(3):439-445.
[5]SEONG-HUN PARK,SUNG-MIN KIM,YOUNG-GUKHA.Highway traffic accident prediction using VDS big data analysis[J].The Journal of Supercomputing,2016,72(7):2815-2831.
[6]HASHMIENEJAD S H-A,HASHEMINEJAD S M H.Traffic accident severity prediction using a novel multiobjective genetic algorithm[J].International Journal of Crashworthiness,2017,22(4):425-440.
[7]肖贵平,朱晓宁.交通安全工程(第二版)[M].北京:中国铁道出版社,2011.[XIAO G P,ZHU X N.Traffic safety engineering(The second edition)[M].Beijing:China Railway Press,2011.]
[8]GB/T 6441-1986.企业职工伤亡事故分类[S].[GB/T6441-1986,Enterprise casualties classification[S].]
[9]赵小强,李瑞敏.城市快速路交通事故持续时间预测[J].公路工程,2010,35(1):42-44,55.[ZHAO X Q,LI R M.Incident duration models of urban expressway[J].Highway Engineering,2010,35(1):42-44,55.]
[10]温慧敏,刘阳,杜华兵,等.2016年北京市交通发展年度报告[R].北京:北京交通发展研究院,2016.[WENH M,LIU Y,DU H B,et al.Beijing annual report on traffic development in 2016[R].Beijing:Beijing Transport Institute,2016.]
[11]胡新民,刘涛,张天华,等.道路黑点识别与改善[J].交通运输工程学报,2004,4(1):106-109.[HU X M,LIUT,ZHANG T H,et al.Highway black spot recognition and improvement[J].Journal of Traffic and Transportation Engineering,2004,4(1):106-109.]