[go: up one dir, main page]

CN102368354A - Road security evaluation method based on floating vehicle data acquisition - Google Patents

Road security evaluation method based on floating vehicle data acquisition Download PDF

Info

Publication number
CN102368354A
CN102368354A CN2011103191488A CN201110319148A CN102368354A CN 102368354 A CN102368354 A CN 102368354A CN 2011103191488 A CN2011103191488 A CN 2011103191488A CN 201110319148 A CN201110319148 A CN 201110319148A CN 102368354 A CN102368354 A CN 102368354A
Authority
CN
China
Prior art keywords
road
floating car
evaluation
floating
speed
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN2011103191488A
Other languages
Chinese (zh)
Inventor
鲁光泉
田大新
王云鹏
余贵珍
张喆
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beihang University
Original Assignee
Beihang University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beihang University filed Critical Beihang University
Priority to CN2011103191488A priority Critical patent/CN102368354A/en
Publication of CN102368354A publication Critical patent/CN102368354A/en
Pending legal-status Critical Current

Links

Landscapes

  • Traffic Control Systems (AREA)

Abstract

The invention provides a road security evaluation method based on floating vehicle data acquisition. The method is characterized in that: considering that unsafety of a road can be reflected on an operation of a driver which drives a vehicle, the unsafety is reflected on parameters of floating vehicle speed, acceleration and the like. Thus, through installing a data acquisition apparatus on the floating vehicle, data is acquired and processed, an evaluation index reflecting road unsafety is obtained and is subjected to a gray cluster analysis, security of the road is divided into four levels of excellent, good, fair and poor, and traffic accident data in previous years is utilized to examine a road safety threshold. By utilizing floating vehicle acquisition data, security of the road is subjected to evaluation, an evaluation result is more visual and accurate, simultaneously timely discovering an unsafe road is facilitated, the unsafe rode is improved, drivers on the road are prompted to pay attention to driving safety, and generation of traffic accidents are reduced.

Description

A kind of road safety evaluation method of gathering based on floating car data
Technical field
The present invention relates to a kind of road safety evaluation method of gathering based on floating car data.Because the insecurity of road can be reflected in driver's the operation; Installation data harvester on Floating Car; Image data is also handled, and obtains reflecting the evaluation index of road insecurity, like percentage speed variation, add/the deceleration frequency, bearing circle rotary speed etc.Through on highway section to be evaluated, going, utilize grey clustering analysis with the security of road be divided into excellent, good, in, differ from four grades, and safety threshold is tested.
Background technology
Along with expanding economy, newly-built road and motor vehicle quantity are on the increase, and the road safety problem also increases gradually.The security of road is estimated and analyzed, important meaning is arranged, road safety evaluation and Floating Car image data aspect have all been launched correlative study both at home and abroad for minimizing traffic hazard number, raising road safety.
Further investigate the road safety evaluation since nineteen ninety abroad, Australia, Britain, New Zealand, Norway etc. implement road safety evaluation country early, and the U.S. has introduced the road safety evaluation in the nineties.Nineteen ninety, Britain proposed the method that road safety is estimated, and rose in England and Scotland in April, 1991 and carried out the safety evaluation of arterial road and highway projects, again road safety was estimated instruction and had carried out revising in 1996.Australia approximately in the past 15 in the period of; Stress that always the road safety engineering is to reduce the road accident rate; The action investigation accident prone location that many Highway Administration Bureaus all take the initiative; And worked out the road safety evaluation guide, each state of Australia all in various degree begin to adopt Road Safety Audit.In Canada, in order to eliminate the traffic safety hidden danger of road itself, the effective measures that Canadian government is taked in the road traffic construction are carried out the road safety evaluation system exactly.In the 21 world's road meeting in 1999; Marc GAUDRY and Karine VERNIER are on the research basis of original path management system; Use the mathematical statistics scheduling theory,, set up the relation of accident rate and accident (Crash) severity and road traffic environment etc. respectively through a large amount of investigation; Relational model between the speed of a motor vehicle and the road traffic environment, and done preliminary identification.On this basis, the speed of a motor vehicle and accident risk relational model some Primary Study have been done.Foreign study shows that the road safety evaluation can effectively prevent traffic hazard, reduces the road accident rate and the order of severity, reduces road and opens the back reconstruction, improves and the operation management expense, promotes the traffic safety culture.According to the national statistics of carrying out safety evaluation, the return rate of safety evaluation is 15～40 times of investment.
Contrast foreign study present situation, domestic research still is in the starting stage.The Guo Zhongyin of Tongji University professor research direction mainly is the relation of road alignment and traffic safety; Nineteen ninety-five takes the lead in road safety is carried out systematic research at home; What at first carry out is that road safety evaluation and road stain are differentiated and improved Study on Technology; Further disclosed the relation of road traffic environment and road safety afterwards, for design provides foundation to road safety, card is in the research of carrying out in a deep going way to concern between road traffic environment, road travelling speed, the road safety three.Xiong Jian teach in 2006 on the basis of " people's---car---environment " road traffic drive simulation experiment porch, the correlation technique of Urban Road Traffic Design virtual reality system has been accomplished in research, has realized the digitizing of urban highway traffic and visual.Traffic Institutes Of Chongqing has accomplished the linear safety evaluatio research of mountainous area highway in 2005.This research has proposed to set up theory and the method based on the linear security model of mountainous area highway of artificial neural network first; And set up the linear security relationship model of mountainous area highway operating speed forecast model, operating speed and mountainous area highway on this basis, disclosed that mountainous area highway is linear, concerned between operating speed and the operation security property.
At present, the Floating Car technology is also carried out correlative study both at home and abroad, set up the Floating Car system that is fit to own characteristic of city.The ADVANCE plan in Chicago,U.S area, through merging data and the historical accident examining report research Floating Car technology that annular detector is gathered, wherein the Floating Car report can be estimated journey time more accurately than toroid winding.Korea S has begun in 1996 that " Korea S's Traffic Information " center " (KORTIC) systematic research main is just carried out traffic data collection through moving detection vehicle annular detector and closed loop circuit TV monitoring and controlling.The Oregon of Poland adopts motorbus to estimate that as Floating Car the average overall travel speed of road section has had actual result; Engineering staff's research has drawn under certain highway section motorbus goes top speed and can be used as the theory of this highway section vehicle average overall travel speed, and extends to other roads in this city.At home; Utilize Floating Car to analyze traffic and be still a newer field; Guangzhou, Hangzhou, Beijing and Chongqing have begun at traffic and transportation system GPS to be installed on a large scale, and existing unit utilizes Floating Car to hire a car in Hangzhou, utilizes the analysis of public transport Floating Car research road traffic condition in Guangzhou.
At present; More existing patent Applying Floating car data image data carry out that the road is clear Journal of Sex Research and traffic evaluation; Like number of patent application: 201010601798.7 " based on the traffic jam point recognition methods of Floating Car technology "; Utilization utilizes the regular in the process of moving collection vehicle numbering of Floating Car, Position, Velocity and Time information, obtains traffic jam point position after the processing; Number of patent application: 201110030820.1 " the traffic flow crossing based on floating car data turns to time-delay to obtain system and method " can provide a kind of new traffic flow crossing to turn to delay data; " based on the method for evaluating macroscopic road network traffic state of floating car data " of number of patent application: CN200910088917.0 utilizes Floating Car to obtain each item traffic behavior evaluation index, and road network is estimated; " traffic evaluation method and the system " of number of patent application: CN201010520218.1 utilizes floating car data to obtain transport information and it estimated.
Analyze domestic and international research present situation and patent situation; Floating Car mainly is used for image data; Aspect traffic, mainly use Floating Car to carry out road management and traffic evaluation; Therefore the correlative study that does not at present also utilize floating car data that road safety is estimated, utilizes floating car data that road safety is estimated and analysis has crucial meaning.
Summary of the invention
The present invention realizes through following technology:
1, utilizes the security of floating car data evaluation path
On unsafe highway section, the driver need take corresponding operation to avoid the generation of accident, and these operations are different with operation under normal circumstances.As when traffic conflict took place, the driver was for fear of bumping against with opposed vehicle, can suddenly slow down, suddenly beat bearing circle, get over line etc.Therefore, the insecurity of road can be reflected in the specific operation of driver, and driver's operation can be with the changes in vehicle speed rate, add/and deceleration frequency etc. representes.Utilize the corresponding data of Floating Car collection reflection driver's operation, promptly can utilize these data that road safety is estimated.
The data that need to gather are seen table 1 with the employed equipment of collection.
Data and equipment that table 1 is gathered
The data of gathering The equipment that uses
The speed of Floating Car, position, road curvature GPS
The acceleration, deceleration degree of Floating Car Gyroscope
Floating Car steering wheel angle, braking number of times The backguy displacement transducer
Driver's sighting distance, Floating Car are got over the line situation Camera
Road grade Slope Transducer
2, evaluation index rationally chooses
Rationally choose evaluation index, help accurately to judge the security of road.Evaluation index needs to reflect road conditions comprehensively, from people, car, three aspects, road road is estimated.Data to Floating Car collects are handled, and can extract evaluation index.The evaluation index of choosing is seen table 2.
Table 2 evaluation index
Figure BDA0000100180030000041
3, the standardization of evaluation index
For increasing the science and the accuracy of evaluation result, need carry out standardization to index.Can know that by table 2 evaluation index of choosing all is a quantitative target, and the implication of each index is different, computing method are different, causes the dimension of each index also different, can not measure each other between the index, brought inconvenience to evaluation.
The general profitable type of the type of quantitative target, cost type, fixed, interval type etc.Benefit type attribute is meant the attribute that property value is the bigger the better, like driver's sighting distance value; Cost type attribute is meant the more little good more attribute of property value, like the rotary speed of bearing circle; The fixed attribute is meant that property value is more near the good more attribute of certain fixed value; Depart from the type attribute and be meant that property value departs from the good more attribute of certain fixed value; Interval type attribute is meant that property value is more near the good more attribute in certain interval, like Floating Car speed.In order to reflect actual conditions as much as possible, get rid of the influence that brings owing to the difference of unit difference and numerical value magnitude between each index, each index unification is changed in [0,1] scope.According to dissimilar indexs, normalization function has following several types:
The normalization function of cost type index:
a i = u d i ( x i ) = 1 x i ≤ m i M i - x i M i - m i x i ∈ d i 0 x i ≥ M i
The normalization function of benefit type index:
a i = u d i ( x i ) = 1 x i ≤ m i x i - m i M i - m i x i ∈ d i 0 x i ≥ M i
The normalization function of fixed index:
Figure BDA0000100180030000052
The normalization function of interval type index:
a i = u d i ( x i ) = 1 otherwise 1 - max { m 1 i - x i , x i - m 2 i } max { m 1 i - m i , M i - m 2 i } x i ∈ [ m 1 i , m 2 i ]
In the formula: M iBe the maximal value of evaluation index i, m iBe the minimum value of evaluation index i, d i=[m i, M i] be the threshold value of evaluation index i, x iBe the property value of evaluation index i, m *Be fixed value,
Figure BDA0000100180030000054
Be fixed interval.
4, grey clustering analysis
After index carried out standardization, can carry out safety evaluatio to road.Grey cluster is that the albefaction weight function according to grey correlation matrix or grey number is gathered into several definable class method for distinguishing with some observation indexs and object of observation.Floating Car is gone on many highway sections, and the evaluation index when obtaining on these highway sections, going adopts GRAY CLUSTER that the security in highway section to be evaluated is classified, with road safety be divided into excellent, good, in, differ from four grades.
The highway section that n is to be evaluated is as cluster object { x i(i=1,2 ..., n); M evaluation index is as cluster index { u j(j=1,2 ... m); S evaluation criterion (promptly excellent, good, in, differ from four grades) is as cluster ash type { z k(k=1,2 ..., s).Then according to the sample value x of i cluster object for j cluster index Ij, confirm that the cluster sample matrix is X:
Adopt probabilistic method to confirm evaluation index; Real data with evaluation index; Handle through nondimensionalization, analyze the cumulative percentage frequency of data, draw the cumulative percentage frequency curve; On curve, confirm the pairing numerical value of different specific cumulative percentage frequencies, in the road safety excellent, good, in, differ from the pairing values of respectively corresponding 15%, 40%, 60%, the 85% cumulative percentage frequency curve of four grades.
Adopt Fuzzy consistent matrix and root method to confirm the weight of each index, structure cluster vector can carry out cluster to certain object, and safety evaluatio is carried out in highway section to be evaluated.
5, the accuracy of check threshold value
The grey clustering analysis method road safety is divided into " in " and the value of the overall target of " poor ", be the threshold value of dividing road safety.According to cluster result, the gps data that will be divided into the highway section of " poor " extracts, and these highway sections are shown on map.Extract traffic hazard in the former years data in all highway sections, and the branch highway section compares.If the traffic hazard number in " poor " highway section is apparently higher than other highway sections, then threshold value is correct, otherwise just need reselect threshold value.
Performing step:
(1) relevant device is installed on Floating Car,, and is debugged successfully, on highway section to be evaluated, go like GPS, gyroscope etc., and measurement data;
(2) data that obtain are handled, obtained the value of evaluation index on each highway section;
(3) evaluation index is carried out standardization;
(4) utilize the grey clustering analysis method, treat evaluation path and carry out safety evaluatio;
(5) extract all highway section traffic hazard in former years data, the correctness of check threshold value and evaluation result.
The present invention has following characteristics: driver's operation is the main cause that causes traffic hazard; The present invention with driver's maloperation with percentage speed variation, add/the deceleration frequency, the bearing circle velocity of rotation waits and quantizes; With grey clustering method road safety is estimated; And test with traffic hazard in former years number, make evaluation result more accurately with directly perceived.
Embodiment
(1) GPS, gyroscope, backguy displacement transducer, camera and Slope Transducer are installed on Floating Car, and debug successfully.On highway section to be evaluated, go, read Floating Car speed on these highway sections, acceleration-deceleration, position, steering wheel angle, braking number of times, line situation, road grade, road curvature, driver's sighting distance information more.
(2) the data branch highway section that obtains is handled, obtain the value of each evaluation index: difference, the acceleration-deceleration of Floating Car, bearing circle rotary speed, the Floating Car of difference, Floating Car speed, highway section fixing speed and the Floating Car speed of real-time driver's sighting distance value, driver's sighting distance and regulation driving sighting distance are got over linear distance, road grade, curvature on the highway section; This highway section come-up motor-car acceleration-deceleration frequency, braking frequency and line frequency more.
(3) evaluation index is carried out standardization: wherein, driver's sighting distance value belongs to benefit type index, is the bigger the better; The difference of difference, highway section fixing speed and the Floating Car speed of driver's sighting distance and regulation driving sighting distance, bearing circle rotary speed, braking frequency, Floating Car more line frequency, Floating Car more linear distance, road grade belong to cost type index, more little good more; Floating Car speed, Floating Car acceleration-deceleration, Floating Car acceleration-deceleration frequency, road curvature belong to interval type index, and be good more near certain interval more.Dissimilar indexs is carried out standardization according to different standardization formula, unified changing in [0,1] scope.
(4) with each evaluation index as evaluation criterion, adopt the grey clustering analysis method, with the security in different highway sections be divided into excellent, good, in, differ from four grades.
(5) road safety is divided into " in " and the value of the overall target of " poor ", be the road safety threshold value.All highway section traffic hazard in former years numbers are extracted, if the traffic hazard number in highway section that is cited as " poor " obviously more than other highway sections, then selection of threshold is correct, evaluation result is accurate.And the highway section that will be cited as " poor " is presented on the map.

Claims (6)

1.一种基于浮动车数据采集的道路安全性评价分析方法，其特征在于：1. A road safety evaluation and analysis method based on floating car data collection, characterized in that: 包括如下步骤：Including the following steps: (1)在浮动车上安装相应设备，如GPs、陀螺仪等，并调试成功，在待评价路段上行驶，并测量数据；(1) Install corresponding equipment on the floating car, such as GPs, gyroscope, etc., and debug successfully, drive on the road section to be evaluated, and measure the data; (2)将得到的数据进行处理，得到各路段上评价指标的值；(2) Process the obtained data to obtain the value of the evaluation index on each road section; (3)将评价指标进行标准化处理；(3) Standardize the evaluation indicators; (4)利用灰色聚类分析法，对待评价道路进行安全性评价；(4) Using the gray cluster analysis method to evaluate the safety of the road to be evaluated; (5)提取所有路段往年交通事故数据，检验阈值和评价结果的正确性。(5) Extract the traffic accident data of all road sections in the past years, and check the correctness of the threshold and evaluation results. 2.根据权利要求1所述的基于浮动车数据采集的道路安全性评价分析方法，其特征在于，所述的步骤(1)，具体是指：在浮动车上安装GPs、陀螺仪、拉线位移传感器、摄像头和坡度传感器，并调试成功。在待评价路段上行驶，读取这些路段上的浮动车速度、加减速度、位置、方向盘转角、制动次数、越线情况、道路坡度、道路曲率、驾驶员视距信息。2. the road safety evaluation analysis method based on floating car data acquisition according to claim 1, is characterized in that, described step (1), specifically refers to: install GPs, gyroscope, stay wire displacement on floating car sensor, camera and slope sensor, and debugged successfully. Drive on the road sections to be evaluated, and read the floating car speed, acceleration and deceleration, position, steering wheel angle, braking times, line crossing, road slope, road curvature, and driver sight distance information on these road sections. 3.根据权利要求1所述的基于浮动车数据采集的道路安全性评价分析方法，其特征在于，所述的步骤(2)，具体是指：对得到的数据分路段进行处理，得到各个评价指标的值：路段上实时的驾驶员视距值、驾驶员视距与规定行车视距的差值、浮动车速度、路段规定速度与浮动车速度的差值、浮动车的加减速度、方向盘转速度、浮动车越线距离、道路坡度、曲率；该路段上浮动车加减速度频率、制动频率和越线频率。3. the road safety evaluation and analysis method based on floating car data collection according to claim 1, is characterized in that, described step (2), specifically refers to: the data that obtains is divided road section and is processed, obtains each evaluation Index value: the real-time driver's sight distance value on the road section, the difference between the driver's sight distance and the specified driving sight distance, the speed of the floating car, the difference between the specified speed of the road section and the speed of the floating car, the acceleration and deceleration of the floating car, the steering wheel Rotation speed, floating car crossing distance, road slope, curvature; floating car acceleration and deceleration frequency, braking frequency and line crossing frequency on this road section. 4.根据权利要求1所述的基于浮动车数据采集的道路安全性评价分析方法，其特征在于，所述的步骤(3)，具体是指：对评价指标进行标准化处理：其中，驾驶员视距值属于效益型指标，越大越好；驾驶员视距与规定行车视距的差值、路段规定速度与浮动车速度的差值、方向盘转速度、制动频率、浮动车越线频率、浮动车越线距离、道路坡度属于成本型指标，越小越好；浮动车速度、浮动车加减速度、浮动车加减速度频率、道路曲率属于区间型指标，越接近某个区间越好。将不同类型的指标按照不同的标准化公式进行标准化，统一变化到[0，1]范围内。4. the road safety evaluation and analysis method based on floating car data collection according to claim 1, is characterized in that, described step (3), specifically refers to: carry out standardized processing to evaluation index: wherein, the driver sees The distance value belongs to the benefit index, the bigger the better; the difference between the driver's sight distance and the specified driving sight distance, the difference between the specified speed of the road section and the speed of the floating car, the steering wheel speed, the braking frequency, the frequency of the floating car crossing the line, the floating Vehicle crossing distance and road slope are cost-type indicators, and the smaller the better; floating vehicle speed, floating vehicle acceleration and deceleration, floating vehicle acceleration and deceleration frequency, and road curvature are interval-type indicators, and the closer to a certain interval, the better. Standardize different types of indicators according to different normalization formulas, and uniformly change them within the range of [0, 1]. 5.根据权利要求1所述的基于浮动车数据采集的道路安全性评价分析方法，其特征在于，所述的步骤(4)，具体是指：将各个评价指标作为评价标准，采用灰色聚类分析法，将不同路段的安全性分为优、良、中、差四个等级。5. the road safety evaluation and analysis method based on floating car data collection according to claim 1, is characterized in that, described step (4), specifically refers to: each evaluation index is used as evaluation standard, adopts gray clustering Using the analysis method, the safety of different road sections is divided into four grades: excellent, good, medium and poor. 6.根据权利要求1所述的基于浮动车数据采集的道路安全性评价分析方法，其特征在于，所述的步骤(5)，具体是指：将道路安全性分为“中”和“差”的综合指标的值，为道路安全性阈值。将所有路段往年交通事故数目提取出来，如果被评为“差”的路段的交通事故数目明显多于其他路段，则阈值选取正确，评价结果准确。并将被评为“差”的路段显示在地图上。6. The road safety evaluation and analysis method based on floating car data collection according to claim 1, characterized in that, described step (5) specifically refers to: dividing road safety into "medium" and "poor" "The value of the comprehensive index is the road safety threshold. Extract the number of traffic accidents of all road sections in the past years. If the number of traffic accidents in the road section rated as "poor" is obviously more than that of other road sections, the threshold value is selected correctly and the evaluation result is accurate. And the road sections rated as "poor" are displayed on the map.
CN2011103191488A 2011-10-19 2011-10-19 Road security evaluation method based on floating vehicle data acquisition Pending CN102368354A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN2011103191488A CN102368354A (en) 2011-10-19 2011-10-19 Road security evaluation method based on floating vehicle data acquisition

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN2011103191488A CN102368354A (en) 2011-10-19 2011-10-19 Road security evaluation method based on floating vehicle data acquisition

Publications (1)

Publication Number Publication Date
CN102368354A true CN102368354A (en) 2012-03-07

Family

ID=45760914

Family Applications (1)

Application Number Title Priority Date Filing Date
CN2011103191488A Pending CN102368354A (en) 2011-10-19 2011-10-19 Road security evaluation method based on floating vehicle data acquisition

Country Status (1)

Country Link
CN (1) CN102368354A (en)

Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102750757A (en) * 2012-06-28 2012-10-24 福建工程学院 Audit method for monitoring event of commercial vehicle
CN103226666A (en) * 2013-05-10 2013-07-31 天津市市政工程设计研究院 Method for evaluating effect of complex function interflow vertical crossing system
CN103810849A (en) * 2012-11-15 2014-05-21 北京掌城科技有限公司 Traffic flow change trend extraction method based on floating car data
CN104567898A (en) * 2013-10-17 2015-04-29 中国移动通信集团公司 Traffic route planning method, system and device
CN105139657A (en) * 2015-10-21 2015-12-09 重庆云途交通科技有限公司 V2I (Vehicle to Infrastructure)-based road boundary and accident black spot extraction method and system
CN105632327A (en) * 2015-12-24 2016-06-01 东南大学 Making method of enhanced digital vector map with road curvature
CN107331191A (en) * 2017-08-15 2017-11-07 北京汽车集团有限公司 Abnormal driving vehicle localization method, Cloud Server and system
CN108182802A (en) * 2017-12-26 2018-06-19 夏莹杰 A kind of Traffic Safety Analysis method driven based on information attenuation model and by car plate data mining
CN108711281A (en) * 2018-03-23 2018-10-26 武汉理工大学 A method of with section actual driving speed data evaluation Highway Safety
CN108765956A (en) * 2018-06-14 2018-11-06 广东工业大学 A kind of traffic status of express way comprehensive estimation method
CN110264825A (en) * 2019-07-31 2019-09-20 交通运输部公路科学研究所 A kind of drive simulation safe evaluation method, apparatus and system
CN111179590A (en) * 2019-12-24 2020-05-19 山东国惠安创智能物联发展有限公司 Remote control method and server for driving speed of electric vehicle
CN114037268A (en) * 2021-11-08 2022-02-11 哈尔滨工业大学 Highway prior safety evaluation system suitable for design phase
CN116884225A (en) * 2023-09-05 2023-10-13 天津大学 Method and device for evaluating influence of proportion of automatic driving vehicles on traffic efficiency

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2007207083A (en) * 2006-02-03 2007-08-16 Denso Corp Probe information collection system and FCD in-vehicle device
CN101794507A (en) * 2009-07-13 2010-08-04 北京工业大学 Method for evaluating macroscopic road network traffic state based on floating car data
CN101853575A (en) * 2010-05-31 2010-10-06 北京航空航天大学 Traffic conflict detection and safety evaluation method at intersection based on two-dimensional laser scanner
CN101976505A (en) * 2010-10-25 2011-02-16 中国科学院深圳先进技术研究院 Traffic evaluation method and system

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2007207083A (en) * 2006-02-03 2007-08-16 Denso Corp Probe information collection system and FCD in-vehicle device
CN101794507A (en) * 2009-07-13 2010-08-04 北京工业大学 Method for evaluating macroscopic road network traffic state based on floating car data
CN101853575A (en) * 2010-05-31 2010-10-06 北京航空航天大学 Traffic conflict detection and safety evaluation method at intersection based on two-dimensional laser scanner
CN101976505A (en) * 2010-10-25 2011-02-16 中国科学院深圳先进技术研究院 Traffic evaluation method and system

Cited By (22)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102750757A (en) * 2012-06-28 2012-10-24 福建工程学院 Audit method for monitoring event of commercial vehicle
CN102750757B (en) * 2012-06-28 2014-07-30 福建工程学院 Audit method for monitoring event of commercial vehicle
CN103810849A (en) * 2012-11-15 2014-05-21 北京掌城科技有限公司 Traffic flow change trend extraction method based on floating car data
CN103810849B (en) * 2012-11-15 2015-10-28 北京掌城科技有限公司 A kind of traffic flow variation tendency extracting method based on floating car data
CN103226666A (en) * 2013-05-10 2013-07-31 天津市市政工程设计研究院 Method for evaluating effect of complex function interflow vertical crossing system
CN103226666B (en) * 2013-05-10 2016-12-28 天津市市政工程设计研究院 Sophisticated functions intercommunication road crossing system efficacy assessments method
CN104567898A (en) * 2013-10-17 2015-04-29 中国移动通信集团公司 Traffic route planning method, system and device
CN104567898B (en) * 2013-10-17 2017-09-12 中国移动通信集团公司 A kind of traffic route planing method, system and device
CN105139657A (en) * 2015-10-21 2015-12-09 重庆云途交通科技有限公司 V2I (Vehicle to Infrastructure)-based road boundary and accident black spot extraction method and system
CN105632327A (en) * 2015-12-24 2016-06-01 东南大学 Making method of enhanced digital vector map with road curvature
CN107331191A (en) * 2017-08-15 2017-11-07 北京汽车集团有限公司 Abnormal driving vehicle localization method, Cloud Server and system
CN107331191B (en) * 2017-08-15 2020-08-25 北京汽车集团有限公司 Abnormal running vehicle positioning method, cloud server and system
CN108182802A (en) * 2017-12-26 2018-06-19 夏莹杰 A kind of Traffic Safety Analysis method driven based on information attenuation model and by car plate data mining
CN108182802B (en) * 2017-12-26 2020-10-16 杭州远眺科技有限公司 Traffic safety analysis method based on information attenuation model and driven by license plate data mining
CN108711281A (en) * 2018-03-23 2018-10-26 武汉理工大学 A method of with section actual driving speed data evaluation Highway Safety
CN108765956A (en) * 2018-06-14 2018-11-06 广东工业大学 A kind of traffic status of express way comprehensive estimation method
CN110264825A (en) * 2019-07-31 2019-09-20 交通运输部公路科学研究所 A kind of drive simulation safe evaluation method, apparatus and system
CN111179590A (en) * 2019-12-24 2020-05-19 山东国惠安创智能物联发展有限公司 Remote control method and server for driving speed of electric vehicle
CN114037268A (en) * 2021-11-08 2022-02-11 哈尔滨工业大学 Highway prior safety evaluation system suitable for design phase
CN114037268B (en) * 2021-11-08 2022-08-19 哈尔滨工业大学 Highway prior safety evaluation system suitable for design phase
CN116884225A (en) * 2023-09-05 2023-10-13 天津大学 Method and device for evaluating influence of proportion of automatic driving vehicles on traffic efficiency
CN116884225B (en) * 2023-09-05 2023-12-26 天津大学 Method and device for evaluating influence of proportion of automatic driving vehicles on traffic efficiency

Similar Documents

Publication Publication Date Title
CN102368354A (en) Road security evaluation method based on floating vehicle data acquisition
CN104809878B (en) Method for detecting abnormal condition of urban road traffic by utilizing GPS (Global Positioning System) data of public buses
CN105374211B (en) The system and method for driving risk and aiding in vehicle insurance price is calculated based on multi-source data
CN102360525B (en) Real-time Traffic Accident Risk Prediction Method for Expressway Based on Discriminant Analysis
Jiang et al. Trip-level energy consumption prediction model for electric bus combining Markov-based speed profile generation and Gaussian processing regression
Ye et al. Prediction and early warning of wind-induced girder and tower vibration in cable-stayed bridges with machine learning-based approach
CN102360526B (en) Real-time monitoring method for road section state of high road
CN108665191B (en) Calculation method of associated area outside subway station based on aggregation effect
CN104200267A (en) Vehicle driving economy evaluation system and vehicle driving economy evaluation method
CN106530717A (en) Construction road section risk evaluating method based on cloud model
Collins et al. Preliminary validation of a speed-profile model for design consistency evaluation
Hoogendoorn et al. Longitudinal driving behavior under adverse weather conditions: Adaptation effects, model performance and freeway capacity in case of fog
Paľo et al. The comparison of automatic traffic counting and manual traffic counting
CN112542049A (en) Comprehensive management and control platform for intelligent traffic
CN113642845A (en) Quality evaluation method of road traffic perception track data
Bodini et al. Techniques for on-board vibrational passenger comfort monitoring in public transport
Ansariyar et al. Investigating the accuracy rate of vehicle-vehicle conflicts by LIDAR technology and microsimulation in VISSIM and AIMSUN.
CN116956044A (en) Autonomous driving vehicles and their performance evaluation methods and evaluation systems
Cohen et al. Travel time estimation between loop detectors and FCD: A compatibility study on the Lille network, France
CN109583081A (en) A kind of prediction model of vehicle speed construction method
CN104484549A (en) Multi-task driving safety state identification method based on visual attention mechanism of driver
CN103198709A (en) Vehicle regulating and controlling method for reducing traffic accidents under raining conditions
CN106651133B (en) Method for achieving user driving behavior scoring based on ADAS by using mobile phone
CN117196153A (en) A smart traffic comprehensive management system based on traffic big data
Bohan et al. Short-term traffic flow prediction based on bus floating car

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
C12 Rejection of a patent application after its publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20120307