高速公路交通事件影响范围的模糊预测
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摘要
高速公路交通事件的影响范围预测主要包括两个方面:排队长度预测和延误预测.在对排队长度和延误进行实时预测时,需精确预测出事件发生期间的交通流量和通行能力、事件持续时间及车辆到达事件点的时间等参数,而事实上,用于确定这些参数的信息是非常复杂的,得到的这些参数值是模糊的,但是其变化范围是可以精确确定的.充分考虑上述参数的模糊特性,及事件发生期间的交通流量和通行能力的模糊关系,运用α截集表示了车辆到达离去曲线,建立了交通事件排队长度模糊预测模型和延误模糊预测模型.利用2001年6月16日发生在美国210-E高速公路的交通事件验证了该模型的可行性,并分析了事件发生期间交通流量和通行能力及事件持续时间的模糊度变化对排队长度和延误预测结果的影响.
Fuzzy forecast of incident incidence on freeway includes queue and delay.We should forecast the volume,the capacity during the incident happening,the duration of the incident,the arriving time of vehicle and etc when we want to forecast incident queue and delay.In fact,these parameters are fuzzy because of their complexity,yet their change range is highly certain.The paper considers well enough the fuzzy characteristic of these parameters,the fuzzy relationship between the volume and the capacity during the incident,and uses α-cut set to denote the curve of the vehicle arriving-departure and founds the fuzzy forecasting model of queue and delay.It validates the model with the traffic incident on 210-E freeway of USA on 6.16.2001 and analyses the effect on the forecasting result of the length when the fuzziness of the volume,the capacity during the incident and the duration of the incident change.
Fuzzy forecast of incident incidence on freeway includes queue and delay.We should forecast the volume,the capacity during the incident happening,the duration of the incident,the arriving time of vehicle and etc when we want to forecast incident queue and delay.In fact,these parameters are fuzzy because of their complexity,yet their change range is highly certain.The paper considers well enough the fuzzy characteristic of these parameters,the fuzzy relationship between the volume and the capacity during the incident,and uses α-cut set to denote the curve of the vehicle arriving-departure and founds the fuzzy forecasting model of queue and delay.It validates the model with the traffic incident on 210-E freeway of USA on 6.16.2001 and analyses the effect on the forecasting result of the length when the fuzziness of the volume,the capacity during the incident and the duration of the incident change.
引文
[1]杨佩昆.智能交通运输系统体系结构[M].上海:同济大学出版社,2001.
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[8]Berkeley Pe M S Development Group.Freeway Data Collection,Storage Processing and Use[M].FreewayPerformance Measurement Project,2002.
[2]Chow W A.A study of traffic performance models under incident conditions[J].Highway Research Record,1973,567:31-36.
[3]Messer J C,Dudek C L,Friebele J D.Method for predicting travel time and other operational measures in real-timeduring freeway incident conditions[J].Highway Research Record,1973,461:1-16.
[4]Morales M J.Analytical procedures for estimating freeway traffic congestion[J].Public Road,1986,50(2):55-61.
[5]Al-Deek H,Garib A,Radawn A E.A new method for estimating freeway incident congestion[J].TransportationResearch Record,1995,1494:30-39.
[6]Kaufmann A,Gupta M M.Introduction to Fuzzy Arithmetic:Theory and Applications[M].New York:VanNostrand Reinhold Company,1985.
[7]冯德益.模糊数学方法与应用[M].北京:地震出版社,1983.
[8]Berkeley Pe M S Development Group.Freeway Data Collection,Storage Processing and Use[M].FreewayPerformance Measurement Project,2002.
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