CN106971534A - Commuter characteristic analysis method based on number plate data - Google Patents
Commuter characteristic analysis method based on number plate data Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G—PHYSICS
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- G08G—TRAFFIC CONTROL SYSTEMS
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- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
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Abstract
The present invention provides a kind of commuter characteristic analysis method based on number plate data, and (1) to road network intelligent bayonet equipment by detecting that data and city static information are acquired；(2) quality of data inspection is carried out to the number plate of vehicle initial data that each intelligent bayonet equipment is detected, integrated in temporal sequence after obtaining effective number plate of vehicle data, analyze the trip type of vehicle；(3) city commuting number plate of vehicle data message is extracted, commuting vehicle Trip chain data are built；(4) count the trip information of commuter vehicle, analysis resident's extra-professional activity situation, the time of resident's commuter with apart from and resident trip demand, obtain resident trip rule.The present invention realizes resident trip demand statistical analysis by magnanimity number plate data analysis resident's private car commuter feature, provides support data for transportation planning model, improves Transportation Demand Management level, effectively alleviate traffic jam issue.
Description
Technical field
The present invention relates to a kind of commuter characteristic analysis method based on number plate data.
Background technology
With the propulsion and the stable development of social economy of urbanization process, the imbalance between supply and demand of Traffic Systems increasingly swashs
Change, traffic jam issue turns into each metropolitan common fault, affects the day-to-day operation of city dweller, is also the development band in city
Carry out hidden danger.Numerous studies show that solving Urban Traffic Jam Based can be from increase transportation supplies and the management side of transport need two
Face is started with, and Transportation Demand Management turns into the research emphasis of field of traffic, and the analysis to Resident Trip Characteristics is transport need pipe
Reason provides support.
Traditional Resident Trip Characteristics analysis obtains trip requirements by resident trip survey, expends a large amount of manpower and materials
Meanwhile, accuracy and actual effect can not all meet New Times traffic programme and regulatory requirement, it is therefore desirable to frequent, low cost,
Automation obtains the new technology of Resident Trip Characteristics.Current Resident Trip Characteristics analysis method mainly has by public vehicle GPS
Data and Based on Bus IC Card Data are special to resident trip to the public commuting pattern analysis of resident or by mobile phone mobile terminal location data
Levy extraction and analysis resident's duty settlement domain.But in face of car ownership growth and the diversity of resident's free life, one is existing
Analytical technology focus principally on public transport commuting, lack to resident's private car go on a journey trip pattern feature analyze, two
The development in city drives all kinds of extra-professional activities, and resident trip purpose, which is not limited only to working, goes to school and wait commuting, and existing trip characteristicses divide
Analysis lacks to the travel behaviour signature analysis such as extra-professional activity beyond resident's commuting, and three development for carrying out urban transportation drive appearance intercity
Between commute pattern, existing trip characteristicses analysis lacks to such intercity commuting statistical analysis.
The current development along with urban traffic control informatization, a large amount of intelligent traffic supervisory systems are progressively pushed away
Extensively use, urban road management traffic department grasps magnanimity traffic circulation related data, and such as intelligent bayonet, electronic police is known daily
A large amount of number plate data are not stored, how to be also that Current traffic big data is information-based by number plate data mining extraction and analysis effective information
Epoch important research content.
The content of the invention
It is an object of the invention to provide a kind of commuter characteristic analysis method based on number plate data, based on city intelligent
The mistake number plate data message of bayonet socket, integrates identification, matching reviews vehicle in road network driving trace, so as to pass through in temporal sequence
Magnanimity driving trace information and detection number plate time data analysis resident trip chain information, further analysis resident commuting, trip
The trip characteristicses such as demand.The analysis method can be traffic administration from magnanimity number plate extracting data Resident Trip Characteristics data
Person provides visual and clear Urban Residential Trip demand statistical analysis situation, further provides support sizes for Transportation Demand Management
According to effective to alleviate urban congestion problem.
The present invention technical solution be：A kind of commuter characteristic analysis method based on number plate data, including with
Lower step,
S1, data acquisition, detection data and city static information to road network intelligent bayonet equipment are acquired；
S2, data prediction, quality of data inspection is carried out to the number plate of vehicle initial data that each intelligent bayonet equipment is detected,
Integrated in temporal sequence after obtaining effective number plate of vehicle data, analyze the trip type of vehicle；
S3, trip link analysis, extract city commuting number plate of vehicle data message, and analysis vehicle driving trace and stop are small
Area, builds commuting vehicle Trip chain data；
S4, trip characteristicses analysis, the trip information of statistical analysis commuter vehicle are occupied according to vehicle driving information analysis
People's extra-professional activity situation, the time of resident's commuter with apart from and resident trip demand, obtain resident trip rule.
Further, in step S1, city static information be urban foundation geography information, including road network information and city knot
Structure layout information.
Further, step S2 is specially：
S21, the quality of data are examined：Quality of data inspection is carried out to the number plate of vehicle initial data that intelligent bayonet equipment is gathered
Survey, abnormal data is subjected to data isolation, obtain effective number plate of vehicle data；Wherein data quality checking includes abnormal data
Detection and number plate of vehicle identification situation detection, anomaly data detection include shortage of data, timestamp mistake, number plate of vehicle identification feelings
Condition detection includes license plate omission, dystopy, unusual character detection；
S22, Data Integration：The effective number plate of vehicle data message of extracting data is detected from intelligent bayonet, then on time
Between sequence integrate city road network in it is all detection data constitute number plate of vehicle databases；
S23, trip type analysis：Number plate of vehicle data are integrated by number plate of vehicle, vehicle driving type is analyzed, i.e., whether is
Transit vehicle, commuting vehicle or other current vehicles.
Further, step S23 is specially：
If S231, number plate of vehicle are outer city's vehicle, while number plate was only occurred in period one day, and in number plate data
There are the number plate of vehicle data of urban entrance and exit tollgate devices detection, then it is assumed that vehicle is transit vehicle, it is otherwise current for city
Vehicle, goes to next step；
If the number plate data of the continuous many Time of Day sequence distributions of S232, vehicle are identical, i.e., detection number plate data in certain period
Device numbering it is identical, number plate data equipment detection time difference be less than threshold value, then it is assumed that the trip be commuter, go to next
Step, is otherwise other in-trips vehicles；
If having the tollgate devices of urban entrance and exit in S233, the detection device for the number plate of vehicle data that commute, then it is assumed that be city
Border commutes, and is otherwise commuted for city, goes to step S3.
Further, step S3 is specially：
S31, vehicle driving trace analysis：Number plate of vehicle data, the number plate data that bayonet socket is obtained are integrated in temporal sequence
Correspondence bayonet socket data queue is put into, according to bayonet socket position and city road network structure, vehicle driving driving trace is generated；
S32, traffic zone analysis：Integrate the initial value of number plate data and the positional information of end value obtains resident's inhabitation friendship
Logical cell, analyzes stop traffic zone of the vehicle in addition to house, according to working day most according to the time difference of detection number plate data
The stop place statistical analysis of long residence time section obtains work traffic zone；
S33, link analysis of going on a journey all day：According to vehicle driving trace and the traffic zone of stop, resident all day is constituted small
Automobile Trip chain data, i.e. vehicle driving trace path and stop cell information；
Further, in step S32, if the time interval of continuous two number plate data exceedes operating range between test point
The threshold value of required time, then it is assumed that vehicle is stopped, it is determined that stop cell, wherein time threshold according to the test point near
Determined between each test point the time required to distance, wherein the threshold value between test point the time required to operating range is according to the test point
Determined between neighbouring each test point the time required to distance, i.e. T=max { ti }, ti running times for needed between test point, by
All vehicle different time sections of detection averagely travel time upper limit and obtained.
Further, step S4 is specially：
S41. extra-professional activity is analyzed：According to the location information in vehicle driving chain data in addition to residence and job site
Resident's extra-professional activity situation is analyzed, further statistics resident's extra-professional activity amount and extra-professional activity cell, it is amateurish that analysis obtains resident
The important thermal point structure area of activity ratio and city；
S42. commuter Distance Time is analyzed：According to trip trace information analytical integration vehicle commuter information, point
Analysis obtains vehicle average trip distance and travel time, statistics morning peak commuting track；Further statistics city private car commuting
The resident trip time and distance, analysis calculates the average commuting time and Commuting Distance for obtaining the commuting of all resident's private cars；
S43. travel demand analysis：Vehicle commuter track is integrated, is matched with road network and obtains vehicle driving section
Information, the driving trace of further all commuter vehicles of statistical analysis obtains city dweller's commuting period weight based on map
Go on a journey section and approach intersection, thus obtain the section that traffic congestion easily occurs for peak period morning and evening.
The beneficial effects of the invention are as follows：Commuter characteristic analysis method of this kind based on number plate data, based on number plate
Based on magnanimity bayonet socket detection data, integrate realize vehicle all fronts net tracking, analysis vehicle road network running orbit and
Resident trip information, generates Trip chain data, further statistics resident trip information, and the trip for analyzing resident's commuter is special
Levy.The present invention provides a kind of new by magnanimity number plate data analysis resident's private car commuter feature for traffic control department
Type Resident Trip Characteristics analysis method, realizes resident trip demand statistical analysis, provides support data for transportation planning model, carries
High Transportation Demand Management level, effectively alleviates traffic jam issue.
Brief description of the drawings
Fig. 1 is the structural representation of commuter characteristic analysis method of the embodiment of the present invention based on number plate data.
Fig. 2 is the explanation schematic diagram of traffic zone analysis in embodiment.
Embodiment
The preferred embodiment that the invention will now be described in detail with reference to the accompanying drawings.
Embodiment
Embodiment utilizes the magnanimity number plate data that intelligent bayonet equipment is gathered, and realizes all fronts net tracking of vehicle, passes through car
Track polymerize in the space-time of road network, and analysis is compared with urban geographic information, extracts and obtains vehicle in one day and substantially go on a journey the stage
And trip track, resident trip rule was grasped according to one week trace information, Resident Trip Characteristics is analyzed, is traffic programme and friendship
Logical demand management work provides resident trip demand relevant Decision support data, proposes that reasonable transportation planning model and traffic are needed
Management strategy is sought, realizes using resident trip demand as the traffic administration being oriented to, effectively alleviates traffic jam issue.
A kind of commuter characteristic analysis method based on number plate data, such as Fig. 1 specifically includes following steps：
S1. data acquisition：The detection data of road network intelligent bayonet equipment are accessed from external interface, including when date, detection
Quarter, number plate of vehicle, device numbering, device location, track number；The static informations such as urban foundation geography information are adopted simultaneously
Collection, including the road network information such as road network topology structure, road section length, the urban structural layout information such as residential quarter shopping centre.
S2. data prediction：Quality of data inspection is carried out to the original vehicle number plate data that each intelligent bayonet is detected, will be had
The number plate data of effect are integrated in temporal sequence, analyze the trip type of vehicle.
S21. the quality of data is examined：Quality of data inspection is carried out to the number plate of vehicle initial data that intelligent bayonet equipment is gathered
Survey, including the anomaly data detection such as shortage of data, timestamp mistake, and the vehicle number such as license plate omission, dystopy, unusual character
Board identification situation detection.
S22. Data Integration：Car data information is crossed from intelligent bayonet detection extracting data is effective, is integrated in temporal sequence
All detection data of city road network constitute number plate of vehicle database, including number plate of vehicle, track number, tollgate devices numbering with timely
Between.
S23. go on a journey type analysis：The vehicle number plate data of one week are integrated by number plate of vehicle, vehicle driving type are analyzed, i.e.,
Whether it is transit vehicle, commuting vehicle (interurban commuting, city commuting) or other current vehicles.
If S231. number plate of vehicle is outer city's vehicle, while number plate was only occurred in one week a certain period day, and number plate
There are the number plate of vehicle data of urban entrance and exit tollgate devices detection in data, then it is assumed that vehicle is transit vehicle, is otherwise city
Interior current vehicle, goes to next step.
S232. number plate of vehicle data are integrated in temporal sequence, if the vehicle number plate data that time series is distributed on weekdays
Identical, i.e., the device numbering of detection number plate data is identical in the period on working day, and number plate data equipment detection time is similar, then recognizes
It is commuter for the trip, goes to next step, is otherwise other in-trips vehicles.
If S233. having the tollgate devices of urban entrance and exit in the detection device of commuting number plate of vehicle data, then it is assumed that be city
Border commutes, and is otherwise commuted for city, goes to step S3.
S3. go on a journey link analysis：Trip chain is the description to one day different time trip order of traveler, while spatially
Reflect the travel activity rule of traveler.City commuting number plate of vehicle data message is extracted, the daily driving trace of vehicle is analyzed
And stop cell, construction work day commuting vehicle Trip chain data all day.
S31. vehicle driving trace is analyzed：Integrate number plate data in temporal sequence according to number plate of vehicle, bayonet socket is obtained
Number plate data are put into correspondence bayonet socket data queue, according to bayonet socket position and city road network structure, generation vehicle driving traveling rail
Mark.
S32. traffic zone is analyzed：Using crossing as fixed point, section is that city is divided into multiple traffic zones by border；Enter one
The initial value and end value of integration work in a few days daily number plate data are walked, i.e., the number plate data detected at first are with eventually detecting
Number plate data, according to Trip chain closing characteristics, resident is obtained by number plate data positional information and urban structure information and lived
Area；According to time difference dwell point of the analysis vehicle in addition to house of detection number plate data, if continuous two number plate data when
Between interval exceed threshold value between test point the time required to operating range, then it is assumed that vehicle is stopped, wherein time threshold root
Determined according to the test point and nearby between each test point the time required to distance, i.e. T=max { ti, tiThe row for needed between test point
The time is sailed, averagely travelling time upper limit by all vehicle different time sections of system detectio obtains；According to working day most long stop
The stop place of period, statistic analysis cell.
As shown in Fig. 2 wherein blue arrow circuit, green arrow circuit and orange arrows circuit are vehicle traveling on the one
Track, number plate data initial value by No. 4 intersections position tollgate devices detect, terminate number plate data by No. 4 with No. 5 it
Between section tollgate devices detection, according to the direction and position of detection data, comprehensive analysis obtains residential quarter for J cells；According to 2
The number plate data analysis of the tollgate devices detection of number intersection position reaches C cell operations place；According to road between No. 5 and No. 6
The tollgate devices detection data of section obtain resident and there is stop in G cells.
S33. go on a journey all day link analysis：According to vehicle driving trace and the traffic zone of stop, resident all day is constituted small
Automobile Trip chain data, i.e. vehicle driving trace path and stop cell information.
S4. trip characteristicses are analyzed：The trip information of statistical analysis all working day commuter vehicle, according to vehicle driving
Time and the distance of information analysis resident's extra-professional activity situation and resident's commuter, resident trip demand is analyzed with this, the palm
Resident trip rule is held, Auxiliary support data are provided for traffic planning and management work.
S41. extra-professional activity is analyzed：According to vehicle, daily Trip chain data message analysis resident whether there is extra-professional activity,
That is whether the trip all day of working day commuting vehicle occurs stops in addition to residence and job site, if thinking resident in the presence of if
There is business activity, i.e. resident in the presence of the transport need in addition to commuting；Further statistics resident's extra-professional activity amount and sparetime are living
Dynamic cell, determines the important thermal point structure area in city, while it is amateurish to obtain resident according to city city commuting vehicle data analysis
, that is, there is the ratio that extra-professional activity in-trips vehicles number accounts for total commuter in activity ratio, it is amateurish to provide resident for vehicle supervision department
Travel activity demand and emphasis trip regional statistics analyze data, Auxiliary support number is provided as Transportation Demand Management decision-making
According to.
S42. commuter Distance Time is analyzed：According to resident residential area, operational area and between trip trace information,
Positional information and temporal information that each tollgate devices in vehicle operation day commuting track detect number plate data, root are integrated by measurement period
Vehicle average trip distance and travel time, wherein root are obtained according to the time difference between detection device positional distance and detection data
According to resident trip characteristic, general statistics morning peak commuting track；The resident trip that further all private cars in statistics city commute
Time and distance, the average commuting time and Commuting Distance for calculating and obtaining all resident's private car commutings are analyzed by measurement period,
There is provided commuting time and distance statistics analyze data for vehicle supervision department, further improve traffic administration decision-making reasonability and
Validity.
S43. travel demand analysis：Vehicle commuter track in measurement period working day is integrated, is matched with road network
The main road section information of vehicle driving is obtained, the driving trace of all commuter vehicles, is based in the further statistical analysis cycle
Map obtains city dweller's commuting period important trip section and main path intersection, i.e. resident's car trip have friendship
Logical demand section, thus obtains the peak period morning and evening easy section for occurring traffic congestion, traffic pipe is provided for traffic administration person
Manage decision support data.
Claims (7)
1. a kind of commuter characteristic analysis method based on number plate data, it is characterised in that：Comprise the following steps,
S1, data acquisition, detection data and city static information to road network intelligent bayonet equipment are acquired；
S2, data prediction, carry out quality of data inspection to the number plate of vehicle initial data that each intelligent bayonet equipment is detected, obtain
Integrated in temporal sequence after effective number plate of vehicle data, analyze the trip type of vehicle；
S3, trip link analysis, extract city commuting number plate of vehicle data message, analysis vehicle driving trace and stop cell,
Build commuting vehicle Trip chain data；
S4, trip characteristicses analysis, the trip information of statistical analysis commuter vehicle, according to vehicle driving information analysis resident's industry
Remaining activity situation, the time of resident's commuter with apart from and resident trip demand, obtain resident trip rule.
2. the commuter characteristic analysis method as claimed in claim 1 based on number plate data, it is characterised in that：Step S1
In, city static information is urban foundation geography information, including road network information and urban structural layout information.
3. the commuter characteristic analysis method as claimed in claim 1 based on number plate data, it is characterised in that step S2 has
Body is：
S21, the quality of data are examined：Data quality checking is carried out to the number plate of vehicle initial data that intelligent bayonet equipment is gathered, will
Abnormal data carries out data isolation, obtains effective number plate of vehicle data；Wherein data quality checking includes anomaly data detection
With number plate of vehicle identification situation detection, anomaly data detection includes shortage of data, timestamp mistake, number plate of vehicle identification situation inspection
Survey includes license plate omission, dystopy, unusual character detection；
S22, Data Integration：The effective number plate of vehicle data message of extracting data is detected from intelligent bayonet, then temporally sequence
Row integrate all detection data in city road network and constitute number plate of vehicle database；
S23, trip type analysis：Number plate of vehicle data are integrated by number plate of vehicle, vehicle driving type is analyzed, i.e., whether is to pass by
Vehicle, commuting vehicle or other current vehicles.
4. the commuter characteristic analysis method as claimed in claim 3 based on number plate data, it is characterised in that step S23
Specially：
If S231, number plate of vehicle are outer city's vehicle, while number plate was only occurred in period one day, and exist in number plate data
The number plate of vehicle data of urban entrance and exit tollgate devices detection, then it is assumed that vehicle is transit vehicle, are otherwise the current vehicle in city,
Go to next step；
If the number plate data of the continuous many Time of Day sequence distributions of S232, vehicle are identical, i.e., detection number plate data sets in certain period
Standby numbering is identical, and number plate data equipment detection time difference is less than threshold value, then it is assumed that the trip is commuter, goes to next step
Suddenly, it is otherwise other in-trips vehicles；
If there is the tollgate devices of urban entrance and exit in S233, the detection device for the number plate of vehicle data that commute, then it is assumed that be interurban logical
Duty, otherwise commutes for city, goes to step S3.
5. the commuter characteristic analysis method as claimed in claim 1 based on number plate data, it is characterised in that step S3 has
Body is：
S31, vehicle driving trace analysis：Number plate of vehicle data are integrated in temporal sequence, and the number plate data that bayonet socket is obtained are put into
Correspondence bayonet socket data queue, according to bayonet socket position and city road network structure, generates vehicle driving driving trace；
S32, traffic zone analysis：It is small that the initial value of integration number plate data and the positional information of end value obtain resident's inhabitation traffic
Area, stop traffic zone of the vehicle in addition to house is analyzed according to the time difference of detection number plate data, most long is stopped according to working day
The stop place statistical analysis of period is stayed to obtain work traffic zone；
S33, link analysis of going on a journey all day：According to vehicle driving trace and the traffic zone of stop, resident's car all day is constituted
Trip chain data, i.e. vehicle driving trace path and stop cell information.
6. the commuter characteristic analysis method as claimed in claim 5 based on number plate data, it is characterised in that：Step S32
In, if the time interval of continuous two number plate data is more than the threshold value the time required to operating range between test point, then it is assumed that car
Stop, it is determined that stopping cell, wherein according to the test point and nearby between each test point, distance is taken time threshold
Between determine, wherein according to the test point and neighbouring distance between each test point of the threshold value between test point the time required to operating range
Required time determination, i.e. T=max { ti, tiThe running time for needed between test point, by all vehicle different times detected
Duan Pingjun traveling time upper limits are obtained.
7. the commuter characteristic analysis method as claimed in claim 1 based on number plate data, it is characterised in that step S4 has
Body is：
S41. extra-professional activity is analyzed：According to the location information analysis in vehicle driving chain data in addition to residence and job site
Resident's extra-professional activity situation, further statistics resident's extra-professional activity amount and extra-professional activity cell, analyze and obtain resident's extra-professional activity
The important thermal point structure area of rate and city；
S42. commuter Distance Time is analyzed：According to trip trace information analytical integration vehicle commuter information, analyze
To vehicle average trip distance and travel time, statistics morning peak commuting track；The residence that further statistics city private car commutes
People's travel time and distance, analysis calculate the average commuting time and Commuting Distance for obtaining all resident's private car commutings；
S43. travel demand analysis：Vehicle commuter track is integrated, is matched with road network and obtains vehicle driving road section information,
The driving trace of all commuter vehicles of further statistical analysis, city dweller's commuting period important trip is obtained based on map
Section and approach intersection, thus obtain the peak period morning and evening easy section for occurring traffic congestion.
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| CN115775449A (en) * | 2021-09-07 | 2023-03-10 | 青岛海信网络科技股份有限公司 | Vehicle type detection method and device |
| CN116206452A (en) * | 2023-05-04 | 2023-06-02 | 北京城建交通设计研究院有限公司 | Sparse data characteristic analysis method and system for urban traffic travel |
| CN116862097A (en) * | 2023-06-08 | 2023-10-10 | 深圳市蕾奥规划设计咨询股份有限公司 | Information determination method and equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101930668A (en) * | 2009-04-29 | 2010-12-29 | 上海电器科学研究所（集团）有限公司 | Road traffic OD (Optical Density) information collection system for license plate recognition and processing method thereof |
| CN102682041A (en) * | 2011-03-18 | 2012-09-19 | 日电（中国）有限公司 | User behavior identification equipment and method |
| CN104766473A (en) * | 2015-02-09 | 2015-07-08 | 北京工业大学 | Traffic trip feature extraction method based on multi-mode public transport data matching |
| CN105206057A (en) * | 2015-09-30 | 2015-12-30 | 哈尔滨工业大学深圳研究生院 | Detection method and system based on floating car resident trip hot spot regions |
| CN105513351A (en) * | 2015-12-17 | 2016-04-20 | 北京亚信蓝涛科技有限公司 | Traffic travel characteristic data extraction method based on big data |
-
2017
- 2017-02-09 CN CN201710072342.8A patent/CN106971534B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101930668A (en) * | 2009-04-29 | 2010-12-29 | 上海电器科学研究所（集团）有限公司 | Road traffic OD (Optical Density) information collection system for license plate recognition and processing method thereof |
| CN102682041A (en) * | 2011-03-18 | 2012-09-19 | 日电（中国）有限公司 | User behavior identification equipment and method |
| CN104766473A (en) * | 2015-02-09 | 2015-07-08 | 北京工业大学 | Traffic trip feature extraction method based on multi-mode public transport data matching |
| CN105206057A (en) * | 2015-09-30 | 2015-12-30 | 哈尔滨工业大学深圳研究生院 | Detection method and system based on floating car resident trip hot spot regions |
| CN105513351A (en) * | 2015-12-17 | 2016-04-20 | 北京亚信蓝涛科技有限公司 | Traffic travel characteristic data extraction method based on big data |
Non-Patent Citations (1)
| Title |
|---|
| 王龙飞: "基于车牌照的车辆出行轨迹分析方法与实践研究", 《中国博士学位论文全文数据库 工程科技Ⅱ辑》 * |
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