基于车牌数据的行程速度特性及交通状态评估
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摘要
针对目前基于车牌的数据信息量大、覆盖范围广,但数据利用率低、资源浪费严重等问题,进行基于车牌的数据挖掘研究。分析已有数据处理方法及可能存在的问题,提出基于聚类思想的时间间隔获取方法,并对提取的行程时间数据进行异常判别和修复。利用相关方法计算平均行程速度,采用统计学分析方法研究了行程速度的时变特性,得到不同天相同时段平均行程速度的稳态变化规律。通过平均行程速度与速度标准差的量化关系研究,获得一天中不同时段行程速度的波动情况,最后设计以速度阈值为判断准则的交通状态评估算法,实例分析结果与实际交通状况一致,说明算法的科学性和有效性,可为交通管理部门提供可靠的决策依据。
Aiming at the problems of large amount of data information based on license plate, wide coverage,but low data utilization rate and serious waste of resources, conducting data mining research based on license plate. Analyzing existing data processing methods and possible problems, a time interval acquisition method based on clustering ideas is proposed, and abnormality discrimination and repair is performed on the extracted travel time data. Using the relevant method to calculate the average travel speed, studying the time-varying characteristics of the travel speed by statistical analysis method, obtaining the steady-state variation of the average travel speed in the same period of different days. Through the quantitative relationship between the average travel speed and the standard deviation of speed, the fluctuation of the travel speed at different times of the day is obtained. Finally, the traffic state evaluation algorithm based on the speed threshold value is designed. The results of the example analysis are consistent with the actual traffic operation, which shows the scientificity and effectiveness of the algorithm and provides real-time and reliable decision-making basis for traffic management departments.
Aiming at the problems of large amount of data information based on license plate, wide coverage,but low data utilization rate and serious waste of resources, conducting data mining research based on license plate. Analyzing existing data processing methods and possible problems, a time interval acquisition method based on clustering ideas is proposed, and abnormality discrimination and repair is performed on the extracted travel time data. Using the relevant method to calculate the average travel speed, studying the time-varying characteristics of the travel speed by statistical analysis method, obtaining the steady-state variation of the average travel speed in the same period of different days. Through the quantitative relationship between the average travel speed and the standard deviation of speed, the fluctuation of the travel speed at different times of the day is obtained. Finally, the traffic state evaluation algorithm based on the speed threshold value is designed. The results of the example analysis are consistent with the actual traffic operation, which shows the scientificity and effectiveness of the algorithm and provides real-time and reliable decision-making basis for traffic management departments.
引文
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[2]刘聪,李娟,等.基于卡口系统车牌识别数据的交通状态判别方法[J].青岛理工大学学报,2017,38(2):90-91.
[3]谢方方.基于视频检测系统的城市道路交通状态估计[D].济南:山东大学,2015:37.
[4]GB T 33171-2016,城市交通运行状况评价规范[S].
[5]刘静.基于车牌分析的交通状态判别方法研究[D].北京:北方工业大学,2015:8.
[6]窦志伟.基于车牌识别数据的交通流参数短时预测[D].成都:西南交通大学,2016:9-10.
[7]陈小鸿,冯均佳,等.基于浮动车数据的行程时间可靠度特征研究[J].城市交通,2007,5(5):43-44.
[8]张和生,张毅,等.利用GPS数据估计路段的平均行程时间[J].吉林大学学报(工学版),2007,37(3):534-536.
[9]Clark S D,Grant-Muller S,Chen H.Cleaning of matched license plate date[J].Transportation Research Record:Journal of Transportation Research board,2002,1804(1):1-7.
[10]姜桂艳,张晓东,等.动态交通数据故障识别与修复方法[J].交通运输工程学报,2004,4(1):123.
[11]王静.基于RFID数据的城市道路交通状态判别方法[D].南京:东南大学,2015:21.
[12]郭恒明,张鹏飞.基于环形线圈的城市道路路段交通异常自动检测方法研究[J].上海公路,2001:33.
[13]尹婧,高军伟,等.城市交通状态判别方法的研究[J].青岛大学学报(工程技术版),2012,23(3):85.
[14]孙超.基于多源浮动车数据融合的道路交通运行评估[J].同济大学学报(自然科学版),2018,46(1):47-48.