CN103680130B - A kind ofly obtain lead the way expert's method of region based on floating car technology - Google Patents
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Abstract
The invention provides and a kind ofly obtain lead the way expert's method of region based on floating car technology, specifically comprise the steps: to utilize Floating Car board information terminal regularly to gather running information, and by the data upload gathering to data service center; Data service center filters invalid data, and active data is stored in corresponding travelling data storehouse, region; Data service center is average coverage strength, coverage rate and the mean residence time in described region by statistical analysis travelling data sequence; Data service center calculates spatial-temporal distribution characteristic in described region and obtains the expert's list list is carried out to updating maintenance by the cycle of leading the way in this region, pushes the optimum expert info of leading the way; The present invention provides the region expert info of leading the way for entering the driving user in region, is conducive to extract the normal driving behavior pattern in region, finds dangerous driving behavior, and provides empirical data for further realizing driving behavior Active Learning; To all kinds of transportation information service systems important in inhibitings.
Description
Technical field
The invention belongs to IT application, specifically relate in transportation information service systems based on floating car technologyObtain lead the way expert's method of region, thereby for providing through the vehicle of specific region based on the lead the way traffic of expert info of regionGuide service.
Background technology
Floating car technology, is also known as " probe vehicles ", is widely used, and its general principle is: vehicle-mounted according to equipmentThe vehicle location of the Floating Car of navigation system periodic logging in its driving process, direction and velocity information, application map match,Computation model and algorithm that path culculatings etc. are relevant are processed, and make Floating Car position data and urban road in time and spaceOn associate.
In real life, each driver has the most familiar separately region, and we are by the condition of road surface in regionThe driver the most familiar with environment is defined as " region lead the way expert ". If can, by floating car data mining analysis, effectively send outRegion in the existing each region expert that leads the way, can be the safe driving behavior of the expert that leads the way in the each region of further extractionInformation (as drive speed information, steering indicating light information etc. in specified link) and trip path planning information are (as special in processRouting behavioural information while determining region) important basic data is provided.
The present invention finds model by building " region lead the way expert ", provides and leads the way specially for entering the driving user in this regionFamily's information, is conducive to extract the normal driving behavior pattern in region, finds dangerous driving behavior, and is further to realize and drivingBehavior Active Learning provides empirical data.
Summary of the invention
The technical problem to be solved in the present invention, is to provide a kind of and obtains lead the way expert's side, region based on floating car technologyMethod, has improved the security of driver drives vehicle, and is conducive to extract the normal driving behavior pattern in region, finds dangerous drivingBehavior.
The present invention is achieved in that and a kind ofly obtains lead the way expert's method of region based on floating car technology, and its feature existsIn, comprise the steps:
Step 1, Floating Car be regular collection vehicle numbering, position, speed and temporal information in the process of moving, obtains rowCar data sequence, and by mobile cellular communication technology, collected travelling data sequence is sent to data service center;
Step 2, data service center carry out filtration treatment to travelling data sequence, and extract the position in travelling data sequencePut the figure layer region information matches in information and GIS-Geographic Information System GIS, obtain the affiliated region of travelling data sequence and will goCar data sequence stores in corresponding travelling data storehouse, region;
Step 3, data service center by statistical analysis travelling data sequence the average coverage strength in described region,Average coverage rate and mean residence time;
Step 4, data service center calculate the region that spatial-temporal distribution characteristic in described region obtains this region and lead the wayExpert's list is also carried out updating maintenance to list by the cycle, pushes the optimal region expert info of leading the way.
Tool of the present invention has the following advantages: the present invention utilizes Floating Car board information terminal to gather running information, passes through dataService centre excavates running information data, and the spatial-temporal distribution characteristic according to travelling data information in specific region obtainsThe region expert's list of leading the way, the driving user who has realized as entering this region provides the expert info of leading the way, and is conducive to extract regionInterior normal driving behavior pattern, finds dangerous driving behavior, and provides experience for further realizing driving behavior Active LearningData.
Brief description of the drawings
Fig. 1 of the present inventionly obtains lead the way expert's method system framework figure of region based on floating car technology.
Fig. 2 is travelling data sequence statistical analysis algorithms flow chart of the present invention.
Fig. 3 is optimum of the present invention expert's discovery algorithm flow chart of leading the way.
Detailed description of the invention
The present invention is a kind of region based on floating car technology expert's discover method of leading the way, for transportation information service systems is carriedFor traffic guiding information, the expert's discover method of leading the way of the region based on floating car technology, the method comprises the steps:
Step 1, Floating Car be regular collection vehicle numbering, position, speed and temporal information in the process of moving, obtains rowCar data sequence, and by mobile cellular communication technology, collected travelling data sequence is sent to data service center;
Step 2, data service center carry out filtration treatment to travelling data sequence, and extract the position in travelling data sequencePut the figure layer region information matches in information and GIS-Geographic Information System GIS, obtain the affiliated region of travelling data sequence and will goCar data sequence stores in corresponding travelling data storehouse, region;
Step 3, data service center by statistical analysis travelling data sequence the average coverage strength in described region,Average coverage rate and mean residence time;
Step 4, data service center calculate the region that spatial-temporal distribution characteristic in described region obtains this region and lead the wayExpert's list is also carried out updating maintenance to list by the cycle, pushes the optimal region expert info of leading the way.
Figure 1 shows that region based on floating car technology expert's discover method system framework figure that leads the way, wherein detail displayRegion based on floating car technology lead the way that expert finds and four included parts of the optimum expert's commending system of leading the way, itsIn the result that produces of each part as the object of next partial data processing.
What first part was carried out is to utilize the Floating Car of a large amount of equipment vehicle positioning systems with the regular collection vehicle of period tauBe numbered i, position l, speed v, and temporal information t, obtain Floating Car travelling data sequence xi＝<li,vi,ti>, will gatherThe data that arrive arrive data service center by mobile cellular communication technology transfer; Wherein said Floating Car given and for pointThe time slip-window T of section sampling1In, its sampled data set is combined into the n rank travelling data sequence of m Floating Car:
X(m,n)＝{xi,j|i∈[1,m],j∈[1,n]}，
Wherein,
Part II data service center carries out filtration treatment to travelling data sequence, and extracts in travelling data sequenceFigure layer region information matches in positional information and GIS-Geographic Information System GIS, obtains the affiliated region of travelling data sequence and also willTravelling data sequence stores in corresponding travelling data storehouse, region.
Part III data service center by statistical analysis travelling data sequence the average covering in described region strongDegree, average coverage rate and mean residence time;
Part IV data service center calculates spatial-temporal distribution characteristic in described region and obtains the region band in this regionLu expert's list is also carried out updating maintenance to list by the cycle, pushes the optimum expert info of leading the way;
As shown in Figure 2, be the travelling data sequence statistical analysis algorithms flow chart of the inventive method.
Step 21, data service center carry out data filtering processing to the velocity information in described travelling data sequence,To effective floating car data, then extract the data acquisition system that all Ultra-Low Speeds travel; Be specially: data service center is in the timeWindow Δ T1Interior by speed always all lower than v0Improper interfering data filtering of travelling, obtain the set of effective floating car data and be
Wherein, xi,j.v be the velocity information in floating car data,
Step 22, data service center are according to the set of effective floating car dataIn position attribution xi,j.l withFigure layer region in GIS-Geographic Information System GIS carries out coordinate matching, obtains the affiliated region of travelling data sequence, and by its storageTo corresponding travelling data storehouse, region gfIn, described region travelling data storehouse gfForm travelling data information bank G:
G＝{g1，g2，…，gf，…}，
Wherein gf＝{xi,j|xi,j.l∈area(gf)}；area(gf) represent the region that defines in GIS-Geographic Information System GISInformation, f is zone number, the corresponding unique number in each region; Described region is that GIS-Geographic Information System GIS divides path mapGained afterwards;
Obtain corresponding traffic route characteristic vector according to its car number i and position attribution l
And construct traffic route vector storehouse and be
S(m)＝{Li| i ∈ [1, m] }; To the set of effective floating car dataIn each element re-start volumeCode, code clerk is k, obtains gathering Xi：
Wherein ε represents XiThe number of middle element;xk.l be the positional information in floating car data; S (m) .LiFor the traffic route characteristic vector in traffic route vector storehouse.
Described step 3 further comprises:
Step 31, data service center zoning time of staying γi,f, the described time of staying is that Floating Car i is at region fMiddle experienced time span, from enter this region start to leave experienced time span be designated as one sub-region stop timeLong, be accumulated at time window T2Interior stop duration summation, and try to achieve T2Time period inner region stops the mean value of duration;
Extract traffic route characteristic vector LiMiddle position li,1, obtain first stored travelling data in affiliated area fThe temporal information of sequence is designated as entry time point t '1, due to traffic route characteristic vector LiPositional information under travelling dataThe temporal information of sequence is that in chronological sequence order is arranged, and therefore can extract from li,1Nearest position li,j, obtain position li,jInstituteBelong to region f+ Δ1The temporal information of interior first stored travelling data sequence is designated as time departure point t '2，Δ1Be one normalAmount, in like manner, Floating Car i enters region f for the q time, and the q time entry time point is designated as t '2q-1, time departure point is designated as for the second timet′2q, until added up time window T2Interior traffic route characteristic vector LiThe time point of all turnover region f, gatheredTime：
Time＝{t′1,t′2,…,t′c, wherein c is time window T2Interior Floating Car region stops the number of times of duration, onceStop duration is designated as | t 'c-t′c-1|, stop total duration and be, obtain mean residence timeDay is the time number of days stopping, and is positive integer;
Step 32, data service center are to effective travelling data set XiFurther analyze, to time window T2Time segment ΔT2Calculate the travelling data sequence of each car at the coverage strength α of region fi,f, described coverage strength represents that Floating Car i is in districtThe track total length travelling in the f of territory is the ratio of all road total lengths in region therewith, and try to achieve T2In time period, coverThe mean value of intensity;
Coverage strength αi,fComputational methods are as follows: extraction zone number is f, and Floating Car i is at d time period Δ TdInTravelling data collection
Calculate the row of vehicleSail track total length
xk.t-xk+1.t＝τ，xk.t represent travelling data sequence xkIn temporal information; xk+1.t represent travelling data sequencexk+1In temporal information; Wherein distance (xk.l,xk+1.l) represent wheelpath point xk.l,xk+1.l the distance between; GISSystem acquisition zone number is road total length R in the region of ff, time period Δ TdInterior coverage strength is
And then calculating T2Average coverage strength in time period;Wherein T2Day represents time window T2Number of days in conversion;
Step 33, data service center further calculate coverage rate βi,f, described coverage rate βi,fRepresent that Floating Car i is in regionThe track covering path total length ratio of all road total lengths in region therewith in f, and try to achieve T2Interior coverage rate of time periodMean value, in described track covering path length, same paths is not repeated to calculate; Using Path Clustering to extract operator calculates onlyOne path:
Step1: extracting zone number is that Floating Car i in f is at d time period Δ TdThe pair of interior travelling data collectionThis (be the copy of original data set, content is the same, is equivalent to copy portion again)
Step2: ifBe not empty, first get the copy of travelling data collectionIn first data sequence be designated asxfirst, deposit in unique path sequence point set Uni-path (unique path sequence point set Uni-path is a set that transition is used,Store array set) in, otherwise go to Step4;
Step2.1: to xfirstCluster analysis, is gathered
Wherein E is distance constant; xj.l be drivingData sequence xjPositional information; x0.l be travelling data sequence x0Positional information;
distance(xj.l,x0.l) represent xjAnd x .l0.l the distance between two positional informations;
Step3: fromMiddle rejecting subset A (xfirst) in point:
Go to Step2;
Step4: to unique path sequence point set Uni-path recompile, obtain unique path sequence Unipath=(x1,x2,...,xz) calculate unique path total length, according to set Time={t '1,t′3,…,t′c-1By temporal information t 'cRightThe travelling data sequence x ' answeringcInsert in unique path sequence Unipath, thereby path is cut apart, prevent that vehicle from leaving regionPoint and the distance entering between region point are counted in path, are gatheredAnd x 'c-1.t＝t′c-1，t′c-1∈ Time is in chronological sequence suitableOrder rearranges, and unique path is calculated as follows:
Wherein h representsMiddle element numberAnd
x′e∈{x1′,x′3,…,x′c-1, time period Δ TdInterior coverage rate is expressed as
And then calculating T2Average coverage rate in time period
As shown in Figure 3, be optimum of the present invention expert's discovery algorithm flow chart of leading the way.
Step 41, by the mode of average coverage strength, average coverage rate and mean residence time weighted sum, calculateThe spatial and temporal distributions metric M of Floating Cari,f, this spatial and temporal distributions metric is expressed as
Wherein, i is car number, and f is zone number;The coverage strength weights of Floating Car i at region f,The coverage rate weights of Floating Car i at region f,FloatingMotor-car i is at the time of staying of region f weights, and meets
Step 42, data service center judge the spatial and temporal distributions metric M of Floating Cari,fWhether be greater than the threshold of systemic presuppositionValue MoIf, Mi,f>Mo, this Floating Car is recommended as to the region expert that leads the way, and by lead the way expert's average covering of the region of recommendingIntensityAverage coverage rateMean residence timeAnd finally enliven the timeObtain the expert info of leading the waySequences yi,f, led the way in expert's list in its typing region, obtain lead the way expert set of region f
Describedly enliven the time recentlyFor the last driving recording time of Floating Car i vehicle in described region f;
Step 43, data service center are every cycle T3To region f, updating maintenance is carried out in the expert's list of leading the way, and will be recentlyEnliven the timeWith current system time T imesystemDifference is greater than time threshold θ,Expert info from expert's list is led the way in region, remove;
When data service center is led the way expert info in push area, nearest to enliven the time interval current system time,MinLead the way and recommend lead the way expert's condition of optimal region in expert's list as region.
In a word, the present invention utilize Floating Car board information terminal gather running information, by data service center to drivingInformation data is excavated, and the spatial-temporal distribution characteristic according to travelling data information in specific region must arrive lead the way expert row of regionTable, the driving user who has realized as entering this region provides the expert info of leading the way, and is conducive to extract normal driving in region capableFor pattern, find dangerous driving behavior, and provide empirical data for further realizing driving behavior Active Learning.
Although more than described the specific embodiment of the present invention, be familiar with those skilled in the art and should manageSeparate, our described specific embodiment is illustrative, instead of for the restriction to scope of the present invention, is familiar with thisThe technical staff in field, in equivalent modification and the variation done according to spirit of the present invention, should be encompassed in of the present inventionIn the scope that claim is protected.
Claims (5)
1. obtain lead the way expert's a method of region based on floating car technology, it is characterized in that, comprise the steps:
Step 1, Floating Car be regular collection vehicle numbering, position, speed and temporal information in the process of moving, obtains the number of driving a vehicleAccording to sequence, and by mobile cellular communication technology, collected travelling data sequence is sent to data service center;
Step 2, data service center carry out filtration treatment to travelling data sequence, and extract the position letter in travelling data sequenceFigure layer region information matches in breath and GIS-Geographic Information System GIS, obtains the affiliated region of travelling data sequence the number of driving a vehicleStore in corresponding travelling data storehouse, region according to sequence;
Step 3, data service center be the average coverage strength in described region, average by statistical analysis travelling data sequenceCoverage rate and mean residence time;
Step 4, data service center calculate region that spatial-temporal distribution characteristic in described region obtains this region expert that leads the wayList is also carried out updating maintenance to list by the cycle, pushes the optimal region expert info of leading the way.
2. according to claim 1ly a kind ofly obtain lead the way expert's method of region based on floating car technology, it is characterized in that:Described step 1 specifically comprises following content:
Utilize the Floating Car of multiple equipment vehicle positioning systems to be numbered i with the regular collection vehicle of period tau, position l, speed v, withAnd temporal information t, obtain Floating Car travelling data sequence xi＝<li,vi,ti>, the data that collect are led to by mobile cellularLetter technology transfer is to data service center; Wherein said Floating Car is given and for the time slip-window T of block sampling1In, its sampled data set is combined into the n rank travelling data sequence of m Floating Car:
X(m,n)＝{xi,j|i∈[1,m],j∈[1,n]}
Wherein,
3. according to claim 2ly a kind ofly obtain lead the way expert's method of region based on floating car technology, it is characterized in that:Described step 2 further comprises:
Step 21, data service center are combined into the speed in the n rank travelling data sequence of m Floating Car to described sampled data setInformation is carried out data filtering processing, obtains effective floating car data, then extracts the data acquisition system that all Ultra-Low Speeds travel; ToolBody is: data service center is at time window △ T1Interior by speed always all lower than v0Improper interfering data filtering of travelling,To the set of effective floating car data be
Wherein, xi,j.v be the velocity information in floating car data,
Step 22, data service center are according to the set of effective floating car dataIn position attribution xi,j.l believe with geographyFigure layer region in breath system GIS carries out coordinate matching, obtains the affiliated region of travelling data sequence, and is stored to correspondenceTravelling data storehouse, region gfIn, described region travelling data storehouse gfForm travelling data information bank G:
G＝{g1，g2，…，gf，…}，
Wherein gf＝{xi,j|xi,j.l∈area(gf)}；area(gf) represent the area information that defines in GIS-Geographic Information System GIS,F is zone number, the corresponding unique number in each region; Described region is after GIS-Geographic Information System GIS divides path mapGained;
According to the set of effective floating car dataMiddle car number i and position attribution l obtain corresponding traffic route featureVector
And construct traffic route vector storehouse and be
S(m)＝{Li| i ∈ [1, m] }; To the set of effective floating car dataIn each element re-start coding, compileCode number is k, obtains gathering Xi：
Wherein ε represents XiThe number of middle element; xk.l bePositional information in floating car data; S (m) .LiFor the traffic route characteristic vector in traffic route vector storehouse.
4. according to claim 3ly a kind ofly obtain lead the way expert's method of region based on floating car technology, it is characterized in that:Described step 3 further comprises:
Step 31, data service center zoning time of staying γi,f, the described time of staying is Floating Car i institute in the f of regionThe time span of experience, starts to be designated as a sub-region stop duration to leaving experienced time span from entering this region, tiredMeter is at time window T2Interior stop duration summation, and try to achieve T2Time period inner region stops the mean value of duration;
Extract traffic route characteristic vector LiMiddle position li,1, obtain first stored travelling data sequence in affiliated area fTemporal information be designated as entry time point t '1, due to traffic route characteristic vector LiPositional information under travelling data sequenceTemporal information be in chronological sequence order arrange, therefore extract from li,1Nearest position li,j, obtain position li,jAffiliated districtIn territory, the temporal information of first stored travelling data sequence is designated as time departure point t '2, in like manner, Floating Car i enters for the q timeEnter region f, the q time entry time point is designated as t '2q-1, time departure point is designated as t ' for the second time2q, until added up time window T2InTraffic route characteristic vector LiThe time point of all turnover region f, obtain gathering Time:
Time＝{t1′,t′2,…,t′c, wherein c is time window T2Interior Floating Car region stops the number of times of duration, while once stopLong being designated as | t 'c-t′c-1|, stop total duration and be
Obtain mean residence time
Day is the time number of days stopping, and is positive integer;
Step 32, data service center are to effective travelling data set XiFurther analyze, to time window T2Time segment △ T2MeterCalculate the travelling data sequence of each car at the coverage strength α of region fi,f, described coverage strength represents that Floating Car i is in the f of regionThe track total length travelling is the ratio of all road total lengths in region therewith, and try to achieve T2Interior coverage strength of time periodMean value;
Coverage strength αi,fComputational methods are as follows: extraction zone number is f, and Floating Car i is at d time period Δ TdInterior driving numberAccording to collection
Calculate the driving trace of vehicleTotal length
Wherein distance (xk.l,xk+1.l) represent wheelpath point xk.l,xk+1.l the distance between; Generalized information system is obtained regionBe numbered road total length R in the region of ff, time period △ TdInterior coverage strength is
And then calculating T2Average coverage strength in time period;
Wherein T2.day represent time window T2Number of days in conversion;
Step 33, data service center further calculate coverage rate βi,f, described coverage rate βi,fRepresent that Floating Car i is in the f of regionTrack covering path total length is the ratio of all road total lengths in region therewith, and try to achieve T2In time period, coverage rate is averageValue, in described track covering path total length, same paths is not repeated to calculate; Using Path Clustering to extract operator calculates uniquePath:
Step1: extracting zone number is that Floating Car i in f is at d time period △ TdThe copy of interior travelling data collection
Step2: ifBe not empty, first get the copy of travelling data collectionIn first data sequence be designated as xfirst, deposit inIn unique path sequence point set Uni-path, otherwise go to Step4;
Step2.1: to xfirstCluster analysis, is gathered
Wherein E is distance constant; xj.l be travelling dataSequence xjPositional information; x0.l be travelling data sequence x0Positional information;
distance(xj.l,x0.l) represent xjAnd x .l0.l the distance between two positional informations;
Step3: fromMiddle rejecting subset A (xfirst) in point:
Go to Step2;
Step4: to unique path sequence point set Uni-path recompile, obtain unique path sequence Unipath=(x1,x2,...,xz) calculate unique path total length, according to set Time={t1′,t′3,…,t′c-1By temporal information t 'cCorrespondingTravelling data sequence x 'cInsert in unique path sequence Unipath, thereby path is cut apart, prevent that vehicle from leaving regionPoint and the distance entering between region point are counted in path, are gatheredAnd x 'c-1.t＝t′c-1，t′c-1∈ Time is in chronological sequence suitableOrder rearranges, and unique path is calculated as follows:
Wherein h representsMiddle element number and
x′e∈{x1′,x′3,...,x′c-1, time period △ TdInterior coverage rate is expressed as
And then calculating T2Average coverage rate in time period
5. according to claim 4ly a kind ofly obtain lead the way expert's method of region based on floating car technology, it is characterized in that:Described step 4 further comprises:
Step 41, by the mode of average coverage strength, average coverage rate and mean residence time weighted sum, calculate unsteadyThe spatial and temporal distributions metric M of cari,f, this spatial and temporal distributions metric is expressed as
Wherein, i is car number, and f is zone number;The coverage strength weights of Floating Car i at region f,The coverage rate weights of Floating Car i at region f,Floating Car iAt the time of staying of region f weights, and meet
Step 42, data service center judge the spatial and temporal distributions metric M of Floating Cari,fWhether be greater than the threshold value M of systemic presuppositiono，If Mi,f>Mo, this Floating Car is recommended as to the region expert that leads the way, and by lead the way expert's average coverage strength of the region of recommendingAverage coverage rateMean residence timeAnd finally enliven the timeExpert info sequence obtains leading the wayyi,f, led the way in expert's list in its typing region, obtain lead the way expert set of region f
Describedly finally enliven the timeFor the last driving recording time of Floating Car i vehicle in described region f;
Step 43, data service center are every cycle T3To region f, updating maintenance is carried out in the expert's list of leading the way, and will finally enlivenTimeWith current system time T imesystemDifference is greater than time threshold θ,SpeciallyFamily's information removes from expert's list is led the way in region;
When data service center is led the way expert info in push area, nearest to enliven the time interval current system time,Lead the way and recommend lead the way expert's condition of optimal region in expert's list as region.
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| CN107272025A (en) * | 2017-06-05 | 2017-10-20 | 浙江百世技术有限公司 | A kind of driving trace detection method of regular bus/bus |
| US11257376B2 (en) * | 2017-11-06 | 2022-02-22 | Honda Motor Co., Ltd. | Mobile body distribution situation forecast device and mobile body distribution situation forecast method |
| CN108645422A (en) * | 2018-06-20 | 2018-10-12 | 郑州云海信息技术有限公司 | A kind of analysis method, system and the device of vehicle user behavioural characteristic |
| CN108765961B (en) * | 2018-08-03 | 2021-03-16 | 东华理工大学 | Floating car data processing method based on improved amplitude limiting average filtering |
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| CN102024325B (en) * | 2010-12-23 | 2012-07-25 | 福建工程学院 | Traffic jam point identification method based on floating car technology |
| CN103106794A (en) * | 2013-01-11 | 2013-05-15 | 福建工程学院 | Road traffic energy-saving method based on probe vehicle technology |
| CN103218915A (en) * | 2013-03-05 | 2013-07-24 | 中山大学 | Experience route generation method based on probe vehicle data |
| CN103366560A (en) * | 2012-04-01 | 2013-10-23 | 同济大学 | Vehicle-following detection method, system and application for road traffic state |
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| JP4977164B2 (en) * | 2009-03-31 | 2012-07-18 | クラリオン株式会社 | Car navigation method, car navigation system, center device, and car navigation device |
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| CN102024325B (en) * | 2010-12-23 | 2012-07-25 | 福建工程学院 | Traffic jam point identification method based on floating car technology |
| CN103366560A (en) * | 2012-04-01 | 2013-10-23 | 同济大学 | Vehicle-following detection method, system and application for road traffic state |
| CN103106794A (en) * | 2013-01-11 | 2013-05-15 | 福建工程学院 | Road traffic energy-saving method based on probe vehicle technology |
| CN103218915A (en) * | 2013-03-05 | 2013-07-24 | 中山大学 | Experience route generation method based on probe vehicle data |
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