CN109257694A - A kind of vehicle OD matrix division methods based on RFID data - Google Patents
A kind of vehicle OD matrix division methods based on RFID data Download PDFInfo
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- CN109257694A CN109257694A CN201810964387.0A CN201810964387A CN109257694A CN 109257694 A CN109257694 A CN 109257694A CN 201810964387 A CN201810964387 A CN 201810964387A CN 109257694 A CN109257694 A CN 109257694A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/80—Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
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Abstract
The vehicle OD matrix division methods based on RFID data that the invention discloses a kind of, the first position of acquisition RFID collector and coordinate, using the RFID collector being arranged in road network as the origin and destination of vehicle driving；Secondly the multiple vehicle datas as unit of consecutive days of acquisition mainly include collection point number, and car number and vehicle pass through the time of each collection point, therefrom sample drawn；Then, based on data mining technology, it is matched by the license plate data to vehicle, obtain running track of the vehicle in road network and the time sequentially through each collector, the time interval and average running speed passed through according to vehicle may determine that between adjacent collection point with the presence or absence of dwell point, is adjusted using precision of the sensitivity analysis technology to judgment criteria；Commuter zone where finally RFID collector is matched to obtains entirety OD matrix using python tool.
Description
Technical field
The present invention relates to a kind of methods for obtaining vehicle the beginning and the end point data based on radio RF recognition technology (RFID), belong to
Urban highway traffic planning and administrative skill field.
Background technique
With the quickening of Urbanization in China, the effect for the main carriers that vehicle is run as Traffic Systems is more
Obviously, to meet the higher and higher trip requirements of traveler, the accurate travel behaviour for grasping resident and demand are in traffic programme
It is just particularly important in the process, the basis for only studying accurate vehicle driving demand analysis as all just can be carried out
Reasonable roading and traffic management measure are formulated, to alleviate urban traffic pressure, administer traffic congestion problem in all directions.
In traditional resident trip analysis, resident trip sample is generally obtained by resident's OD survey, further according to society
The data such as economy, land use, population development are predicted, so that the Urban Residential Trip OD matrix predicted, finally leads to
It crosses traffic modal splitting prediction model and obtains vehicle driving OD matrix.In the prior art, satellite positioning tech acquisition is generallyd use
Vehicle driving OD matrix, if vehicle GPS position, although be directed to the available high-frequency data of target vehicle, and positional accuracy compared with
Height, but that there are equipment integral installation rates is low, sample size is few and user considers that privacy is reluctant the problems such as sharing trip data.And nothing
Line Radio Frequency Identification Technology (RFID) is used as a kind of wireless communication technique, and specific objective can be identified by radio signals and is read and write
Related data, the technology are common in urban transportation bayonet and obtain vehicle information data, and having scanning, contamination resistance is strong rapidly,
The features such as data accumulating capacity is big, highly-safe, it is only necessary to identify the dwell point in vehicle travel, and according to dwell point by one
It stroke is broken into multiple trip, so that it may preferably solve the problems, such as that other methods exist.
Currently, less for the vehicle driving OD matrix acquisition methods based on RFID and big in the presence of calculating intensity, accuracy rate
There is limitation in use in the problems such as lower.It is, therefore, desirable to provide a kind of be based on radio RF recognition technology (RFID)
The method for obtaining vehicle the beginning and the end point data, can quickly determine the O point and D point of vehicle driving, and accurately obtain city vehicle and go out
Row OD matrix.
Summary of the invention
It is less to solve the above-mentioned vehicle driving OD matrix acquisition methods based on RFID referred to, and exist and calculate intensity
Greatly, the problems such as accuracy rate is lower, the present invention provides a kind of methods for obtaining vehicle the beginning and the end point data based on RFID data, are applicable in
City in all installation RFID collectors and the vehicle for being mounted with RFID tag, have universality strong, operation is quick, at low cost
The features such as honest and clean.
The vehicle OD matrix division methods based on RFID data that the invention discloses a kind of, comprising the following steps:
Step 1: obtaining position and the coordinate of RFID collector, the RFID collector being arranged in road network is gone out as vehicle
Capable origin and destination, acquire multiple vehicle datas as unit of consecutive days, and therefrom sample drawn, which includes acquisition
Point number, car number and vehicle pass through the time of each collection point；
Step 2: being matched by the license plate data to vehicle, obtain running track of the vehicle in road network and by suitable
Sequence is judged by time of each collector, according to vehicle by the time interval and average running speed of adjacent collector adjacent
With the presence or absence of trip dwell point between collection point；
Step 3: the trip dwell point that judgement obtains being matched with commuter zone information, obtains OD matrix.
The step 1 specifically includes the following steps:
S1-1, it is extracted using data of the data mining technology to same vehicle ID, is captured according still further to collected device
Time sequencing is ranked up data；
S1-2, collector is positioned on map according to the position and coordinate of collector, is gone out according to calculation of longitude & latitude
Actual range D of the adjacent collector in road network；
S1-3, time interval of the vehicle by adjacent collector is calculated, and according between collector number and collector
Actual range calculates the average speed that vehicle passes through adjacent collector.
The step 2 specifically includes the following steps:
S2-1, it sorts from low to high to actual range D of the adjacent collector in road network, takes 95% quantile D95；
S2-2, D is used95Peak period average vehicle speed is obtained divided by investigationVehicle is obtained by adjacent collector without stopping
The maximum time interval T stayedmax:
S2-3, the vehicle for calculating same number pass through adjacent acquisition with the vehicle by the time interval t of adjacent collector
Maximum time interval T of the device without stopmaxIt is compared, judges whether there is dwell point:
If t > Tmax, then the vehicle is by the way that there are dwell points between two adjacent collectors；
If t < Tmax, then further judge the vehicle by, with the presence or absence of dwell point, calculating between two adjacent collectors
The vehicle passes through the average speed between two adjacent collectorsIt is average without stopping by adjacent collector with the vehicle of setting
Speed of service VnsIt is compared:
IfThen the vehicle is by the way that there are dwell points between two adjacent collectors；
IfThen the vehicle is by being not present dwell point between two adjacent collectors；
The step 3 specifically includes the following steps:
S3-1, extract there are two adjacent collector ID of dwell point, using a forward collector of time of origin as
Starting point of the collector as the latter stroke rearward occurs for the terminating point of previous stroke, i.e. D point, time of origin,
That is O point；
S3-2, the point passed through in stroke is deleted, retains OD point collector number, acquisition time；
S3-3, RFID collector ID all in commuter zone are matched with the commuter zone where it, determines each row
Cell number in journey where O point, D point；
S3-4, it is counted using OD point of the python tool to all strokes, obtains the one day vehicle OD matrix in city.
The vehicle of setting is by adjacent collector without stop average running speed Vns, by obtaining as follows:
With dichotomy 0 to peak period average vehicle speedMiddle search speed vi, compare friction speed viUnder utilization
The dwell point division result that python tool obtains is with the division of artificial dwell point as a result, highest one group of division result accuracy
Speed viAs without stop average running speed Vns。
The utility model has the advantages that the present invention relates to the overall processes counted from dwell point identification, trip cutting, OD matrix.To be adopted
Time difference that storage captures and average speed repeatedly compare as evaluation criterion, propose the method for determining dwell point, thus
The starting point and ending point gone on a journey every time in a stroke has been determined, using data mining technology, has matched collector and commuter zone
Information, to obtain resident's vehicle driving OD matrix.Method universality of the invention is good, and operability is stronger, anti-interference ability
By force, operating cost is low, and obtained result can be used to instruct Urban Planner and manager according to resident's vehicle driving OD matrix
Estimate city vehicle trip requirements, so that the deficiency of conventional traffic requirement forecasting is made up, to formulate transport need analysis and predicting,
The construction plan of Traffic Development demand provides reasonable foundation, realizes the benign development of urban traffic network economic benefit.
Detailed description of the invention
Fig. 1 is flow diagram of the present invention；
Fig. 2 is the average running speed scatter plot of all sample vehicles.
Specific embodiment
The present invention is further explained with reference to the accompanying drawings and examples.
Embodiment:
By taking Chongqing City as an example, vehicle driving OD matrix of the city within the high-speed range of city is obtained, the present invention is illustrated with this.
Test object is wanted based on selection Wednesday routine work day, the identical vehicle data of ID is captured according to collected device
Time sequencing sequence can demarcate collector in GIS according to the positioning of collector longitude and latitude, calculate adjacent collector
Between actual range D.
Calculate time interval of the vehicle by adjacent collector, according to collector number collector between actually away from
From calculating vehicle passes through the average speed of adjacent collector.
Actual range D of the collector in road network is sorted from low to high, takes 95% quantile D95For 12km, i.e., 95%
Adjacent collector distance interval is less than 12km.
Use D95Peak period average vehicle speed is obtained divided by investigationVehicle is obtained by adjacent collector without stop
Maximum time interval Tmax:
Investigation learns, the vehicle average overall travel speed of Chongqing City's peak hourAbout 24km/h, therefore, vehicle pass through phase
Maximum time interval T of the adjacent collector without stopmaxFor 0.5h, i.e. 30min.
The vehicle of same number is calculated by the time interval t of adjacent collector, and with vehicle by adjacent collector without
The maximum time interval 30min of stop is compared, and judges whether there is dwell point:
If t > 30min, target vehicle is by the way that there are dwell points between two adjacent collectors；
If t < 30min, target vehicle between two adjacent collectors by, there may be dwell point, needing further
It calculates target vehicle and passes through the average speed between two adjacent collectorsWith the vehicle of setting by adjacent collector without stopping
Stay average running speed VnsIt is compared.
As shown in Fig. 2, being less than the scatterplot of 24km/h in gray area for average speed.
When then considering that speed is 12km/h, the dwell point that is divided automatically using python tool with manually draw
The dwell point got compares, and discovery is higher less than the accuracy of 12km/h；Therefore, when further considering that speed is 6km/h
The dwell point that program divides automatically compared with the dwell point manually divided, and so on.It is final to determine when distinguishing speed is
When 3km/h, program divides the accuracy highest of dwell point automatically, reaches 87.7%.Therefore, V in the present embodimentns=3km/h.
IfThen target vehicle is by the way that there are dwell points between two adjacent collectors；
IfThen target vehicle is by being not present dwell point between two adjacent collectors.
There are two adjacent collector ID of dwell point for extraction, and a forward collector of time of origin is considered previous
The terminating point of a stroke, i.e. D point, the collector that time of origin occurs rearward are considered the starting point of the latter stroke, i.e. O
Point deletes the point passed through in stroke, only retains the information such as OD point collector number, acquisition time.
There are collector 1092, traffic Division totally 463 within the scope of known region, each collector is corresponded to phase
The cell answered can obtain cell number where O, D point of each stroke of each target vehicle, using python tool to all
The OD point of stroke is counted, and the one day vehicle OD table in city is finally obtained.
Claims (5)
1. a kind of vehicle OD matrix division methods based on RFID data, it is characterised in that: the following steps are included:
Step 1: position and the coordinate of RFID collector are obtained, using the RFID collector being arranged in road network as vehicle driving
Origin and destination, acquire multiple vehicle datas as unit of consecutive days, and therefrom sample drawn, which includes that collection point is compiled
Number, car number and vehicle pass through time of each collection point；
Step 2: being matched by the license plate data to vehicle, obtain running track of the vehicle in road network and lead in order
The time for crossing each collector judges adjacent acquisition by the time interval and average running speed of adjacent collector according to vehicle
With the presence or absence of trip dwell point between point；
Step 3: the trip dwell point that judgement obtains being matched with commuter zone information, obtains OD matrix.
2. a kind of vehicle OD matrix division methods based on RFID data according to claim 1, it is characterised in that: described
Step 1 specifically includes the following steps:
S1-1, it is extracted using data of the data mining technology to same vehicle ID, the time captured according still further to collected device
Sequence is ranked up data；
S1-2, collector is positioned on map according to the position and coordinate of collector, it is adjacent out according to calculation of longitude & latitude
Actual range D of the collector in road network；
S1-3, time interval of the vehicle by adjacent collector is calculated, and according to the reality between collector number and collector
Distance calculates the average speed that vehicle passes through adjacent collector.
3. a kind of vehicle OD matrix division methods based on RFID data according to claim 2, it is characterised in that: described
Step 2 specifically includes the following steps:
S2-1, it sorts from low to high to actual range D of the adjacent collector in road network, takes 95% quantile D95；
S2-2, D is used95Peak period average vehicle speed is obtained divided by investigationVehicle is obtained by adjacent collector without stop
Maximum time interval Tmax:
S2-3, the vehicle for calculating same number pass through the time interval t of adjacent collector, with the vehicle pass through adjacent collector without
The maximum time interval T of stopmaxIt is compared, judges whether there is dwell point:
If t > Tmax, then the vehicle is by the way that there are dwell points between two adjacent collectors；
If t < Tmax, then further judge the vehicle by, with the presence or absence of dwell point, calculating the vehicle between two adjacent collectors
Pass through the average speed between two adjacent collectorsIt is averagely run by adjacent collector without stop with the vehicle of setting
Speed VnsIt is compared:
IfThen the vehicle is by the way that there are dwell points between two adjacent collectors；
IfThen the vehicle is by being not present dwell point between two adjacent collectors.
4. a kind of vehicle OD matrix division methods based on RFID data according to claim 1 or 3, it is characterised in that:
The step 3 specifically includes the following steps:
S3-1, there are two adjacent collector ID of dwell point for extraction, using a forward collector of time of origin as previous
Starting point of the collector as the latter stroke rearward, i.e. O occur for the terminating point of a stroke, i.e. D point, time of origin
Point；
S3-2, the point passed through in stroke is deleted, retains OD point collector number, acquisition time；
S3-3, RFID collector ID all in commuter zone are matched with the commuter zone where it, determines O in each stroke
Cell number where point, D point；
S3-4, it is counted using OD point of the python tool to all strokes, obtains the one day vehicle OD matrix in city.
5. a kind of vehicle OD matrix division methods based on RFID data according to claim 4, it is characterised in that: setting
Vehicle by adjacent collector without stop average running speed Vns, by obtaining as follows:
With dichotomy 0 to peak period average vehicle speedMiddle search speed vi, compare friction speed viUnder utilization
The dwell point division result that python tool obtains is with the division of artificial dwell point as a result, highest one group of division result accuracy
Speed viAs without stop average running speed Vns。
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| CN113284337A (en) * | 2021-04-19 | 2021-08-20 | 国交空间信息技术（北京）有限公司 | OD matrix calculation method and device based on vehicle track multidimensional data |
| CN113823081A (en) * | 2021-03-08 | 2021-12-21 | 上海评驾科技有限公司 | Space positioning method based on commercial vehicle travel starting and ending point |
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Cited By (7)
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