基于GPS轨迹数据的超速事件探测方法试验研究
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摘要
目前,交警部门对超速事件的探测主要是通过定点测速或区间测速来实现的,但该方法不仅误差大,还难以探测车辆所发生的全部超速事件。随着GPS设备的广泛使用,一些学者尝试利用GPS轨迹数据中的速度信息来探测车辆所发生的全部超速事件,但单一点速度误差大。为及时、准确、全面地探测车辆所发生的全部超速事件,通过行车试验研究基于N(N≥2)个连续超速GPS轨迹点的超速事件探测方法。将超速事件视为一个包含起止点的线事件,并由超速GPS轨迹点描述,据此设计行车试验方案。选择成都市郊区6条道路为试验路线,试验车在每条试验路线上多次往返行驶,利用车载GPS设备采集试验车的行驶轨迹,并由随车试验人员记录该车超速事件发生的时间及地点。基于行车试验数据,构建匹配度指标,设计基于连续超速GPS轨迹点的超速事件探测算法,经比较分析确定超速事件探测的最佳连续超速GPS轨迹点个数,并验证探测算法的精度。结果表明:超速事件的判定条件为5个连续超速GPS轨迹点,基于5个连续超速GPS轨迹点的超速事件探测算法的精度为96%。研究结果可为超速事件的动态监测、实时呈现、时空分布规律揭示、致因因素挖掘、干预方法提出奠定坚实的理论基础,具有较好的实际应用价值。
Recently, the detection of speeding events from traffic police department is mainly achieved by fixed-point speed measurement or interval speed measurement, but this method is not only large in error, but also difficult to detect all the vehicle speeding events. With the widespread use of GPS devices, some scholars attempted to use the speed information of GPS trajectory data to detect all the vehicle speeding events, but the single point speed is large in error. In order to detect all the vehicle speeding events in a promptly, accurate and comprehensive manner, the speeding event detection method based on N(N≥2)consecutive speeding GPS trajectory points was studied through the driving test. The speeding event was considered as a linear event containing the starting and ending points and described by the speeding GPS trajectory points, and the driving test plan was designed based on that. Six roads in the suburbs of Chengdu were selected as test routes. The test vehicle traveled multiple times on each test route, using the vehicle-mounted GPS device to collect the driving trajectory of the test vehicle, and the on-vehicle personnel recorded the time and location of the vehicle speeding events. Based on the driving test data, the matching degree index was constructed, and the speeding event detection algorithm based on consecutive speeding GPS trajectory points was designed. The optimal consecutive speeding GPS trajectory points of speeding event detection were determined by comparative analysis, and the accuracy of the detection algorithm was validated. The results show that the criterion of speeding event is 5 consecutive speeding GPS trajectory points; the accuracy of the speeding event detection algorithm based on 5 consecutive speeding GPS trajectory points is 96%. The research results are able to lay a solid theoretical foundation for the dynamic monitoring of the speeding events, real-time presenting, the distribution of time and space revealing,contributing factors mining, and intervention methods proposing, which has a good practical application value.
Recently, the detection of speeding events from traffic police department is mainly achieved by fixed-point speed measurement or interval speed measurement, but this method is not only large in error, but also difficult to detect all the vehicle speeding events. With the widespread use of GPS devices, some scholars attempted to use the speed information of GPS trajectory data to detect all the vehicle speeding events, but the single point speed is large in error. In order to detect all the vehicle speeding events in a promptly, accurate and comprehensive manner, the speeding event detection method based on N(N≥2)consecutive speeding GPS trajectory points was studied through the driving test. The speeding event was considered as a linear event containing the starting and ending points and described by the speeding GPS trajectory points, and the driving test plan was designed based on that. Six roads in the suburbs of Chengdu were selected as test routes. The test vehicle traveled multiple times on each test route, using the vehicle-mounted GPS device to collect the driving trajectory of the test vehicle, and the on-vehicle personnel recorded the time and location of the vehicle speeding events. Based on the driving test data, the matching degree index was constructed, and the speeding event detection algorithm based on consecutive speeding GPS trajectory points was designed. The optimal consecutive speeding GPS trajectory points of speeding event detection were determined by comparative analysis, and the accuracy of the detection algorithm was validated. The results show that the criterion of speeding event is 5 consecutive speeding GPS trajectory points; the accuracy of the speeding event detection algorithm based on 5 consecutive speeding GPS trajectory points is 96%. The research results are able to lay a solid theoretical foundation for the dynamic monitoring of the speeding events, real-time presenting, the distribution of time and space revealing,contributing factors mining, and intervention methods proposing, which has a good practical application value.
引文
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[18]CHEVALIE A,COXON K,WALL J,et al.Exploration of older drivers’speeding behavior[J].Transportation Research Part F,2016,42:532-543.
[19]HUANG Y,SUN D,TANG J.Taxi driver speeding:who,when,where and how?a comparative study between Shanghai and New York City[J].Traffic Injury Prevention,2018,19(3):311-316.
[20]YEH M,TSENG C,LIU H,et al.The factors of female taxi drivers’speeding offenses in Taiwan[J].Transportation Research Part F:Traffic Psychol Behav,2015,32:35-45.
[21]李娟,高山,闫广锋,等.基于GPS的高速公路车速前程监控方法研究[J].测绘工程,2017,26(1):47-50.
[22]BOLDERDIJK W,KNOCKAERT J,STEG M,et al.Effects of pay-as-you-drive vehicle insurance on young drivers’speed choice:results of a dutch field experiment[J].Accident Analysis and Prevention,2011,43:1181-1186.
[23]LAHRMANNA H,AGERHOLMA N,TRADISAUSKAS N,et al.Pay as you speed,IS-Awith incentives for not speeding:a case of test driver recruitment[J].Accident Analy-sis and Prevention,2012,48:10-16.
[24]LAHRMANNA H,AGERHOLMA N,TRADISAUSKASN,et al.Pay as you speed,is-a with incentives for not speeding:Results and interpretation of speed da-ta[J].Accident Analysis and Prevention,2012,48:17-28.
[25]唐炉亮,郑文斌,王志强,等.城市出租车上下客的GPS轨迹时空分布探测方法[J].地球信息科学,2015,17(10):1179-1186.
[26]刘汇慧,阚子涵,吴华意,等.车辆GPS轨迹加油行为建模与时空分布分析[J].测绘通报,2016,(9):29-34.
[2]NILSSON G.Traffic safety dimensions and the power model to describe the effect of speed on safety[D].Lund.:Lund Institute of Technology,2004.
[3]OGLE J.Quantitative assessment of driver speeding behavior using instrumented vehicles[D].Atlanta:Georgia Institute of Technology,2005.
[4]WUNDERSITZ L,HIRANANDANI K,BALDOCKM.Annual performance indicators of enforced driver behaviors in South Australia[R].Adleaide:University of Adelaide:2004.
[5]ELVIK R.Dimensions of road safety problems and their measurement[J].Accident Analysis&Prevention,2008,40(3):1200-1210.
[6]公安部道路交通安全研究中心.中国大城市道路交通发展研究报告之四[M].北京:中国建筑工业出版社,2018.
[7]HUANG Y Z,SUN D J,TANG J Y.Taxi driver speeding:Who,when,where and how?Acomparative study between Shanghai and New York City[J].Traffic Injury Prevention,2018,19(3):311-316.
[8]ELLISON A,GREAVES S.Speeding in urban environments:are the time savings worth the risk?[J].Accident Analysis and Prevention,2015,85:239-247.
[9]TSENG C M.Operating styles,working time and daily driving distance in relation to a taxi driver's speeding offenses in Taiwan[J].Accident Analysis and Prevention,2013,52:1-8.
[10]LIU C,CHEN C L,Subramanian R,et al.Analysis of speeding-related fatal motor vehicle traffic crashes[R].Washington D.C.:Mathematical Statisticians,Mathematical Analysis Division,National Center for Statistics and Analysis,2005.
[11]AARTS L,SCHAGEN I.Driving speed and the risk of road crashes:a review[J].Accident Analysis&Prevention,2006,38:215-224.
[12]姜华平,许洪国,李浩,等.高速公路车辆超速行驶交通事故分析[J].交通运输系统工程与信息,2004,3(3):49-51.
[13]ALONSO F,ESTEBAN C,CALATAYUD C,et al.Speed and road accidents:behaviors,motives,and assessment of the effectiveness of penalties for speeding[J].American Journal of Applied Psychology,2013,1(3):58-64.
[14]WATSON B.Assessing specific deterrence effects of increased speeding penalties using four measures of recidivism?[J]Accident Analysis and Prevention,2015,84:27-37.
[15]ELVIK R.Speed enforcement in Norway:testing a game-theoretic model of the interaction between drivers and the police.[J]Accident Analysis and Prevention,2015,84:128-133.
[16]CHRISTIE S M,LYONS R A,DUNSTAN F D.“Are mobile speed cameras effective?a controlled before and after study”[J].British Medical Journals,2003,(9):302-306.
[17]DU S,IBRANHIM M,SHEHATA M.Automatic license plate recognition(ALPR):a state-of-the-art review[J].IEEE Transactions on Circuits and System for Video Technology,2013,23(2):311-325.
[18]CHEVALIE A,COXON K,WALL J,et al.Exploration of older drivers’speeding behavior[J].Transportation Research Part F,2016,42:532-543.
[19]HUANG Y,SUN D,TANG J.Taxi driver speeding:who,when,where and how?a comparative study between Shanghai and New York City[J].Traffic Injury Prevention,2018,19(3):311-316.
[20]YEH M,TSENG C,LIU H,et al.The factors of female taxi drivers’speeding offenses in Taiwan[J].Transportation Research Part F:Traffic Psychol Behav,2015,32:35-45.
[21]李娟,高山,闫广锋,等.基于GPS的高速公路车速前程监控方法研究[J].测绘工程,2017,26(1):47-50.
[22]BOLDERDIJK W,KNOCKAERT J,STEG M,et al.Effects of pay-as-you-drive vehicle insurance on young drivers’speed choice:results of a dutch field experiment[J].Accident Analysis and Prevention,2011,43:1181-1186.
[23]LAHRMANNA H,AGERHOLMA N,TRADISAUSKAS N,et al.Pay as you speed,IS-Awith incentives for not speeding:a case of test driver recruitment[J].Accident Analy-sis and Prevention,2012,48:10-16.
[24]LAHRMANNA H,AGERHOLMA N,TRADISAUSKASN,et al.Pay as you speed,is-a with incentives for not speeding:Results and interpretation of speed da-ta[J].Accident Analysis and Prevention,2012,48:17-28.
[25]唐炉亮,郑文斌,王志强,等.城市出租车上下客的GPS轨迹时空分布探测方法[J].地球信息科学,2015,17(10):1179-1186.
[26]刘汇慧,阚子涵,吴华意,等.车辆GPS轨迹加油行为建模与时空分布分析[J].测绘通报,2016,(9):29-34.