CN105261212B - A kind of trip space-time analysis method based on GPS data from taxi map match - Google Patents
A kind of trip space-time analysis method based on GPS data from taxi map match Download PDFInfo
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Abstract
The present invention proposes that one kind based on the matched trip space-time analysis method of taxi GPS map, extracts original GPS data from taxi, data are pre-processed；Analysis of history data determine taxi GPS positioning deviation accumulation distribution form, build secondary buffer area searching method；Taxi trip data is extracted, fusion GPS data from taxi direction element establishes map-matching method apart from element, and anchor point is matched on section；Trip spatial distribution characteristic recognition method is established, utilizes GPS data from taxi analysis trip spatial-temporal distribution characteristic after matching.The present invention makes full use of mass historical data to analyze taxi GPS positioning error distribution characteristics, taxi GPS positioning point matching efficiency is improved by building secondary buffer area searching method, the effective discriminance analysis trip distribution special efficacy of distribution characteristics recognition methods of foundation, this law is of great significance to raising taxi map match effect, Floating Car information collection, trip space-time analysis effect, and basic data support is provided for Urban Traffic Planning.
Description
Technical field
The present invention relates to traffic programme applied technical fields, and GPS data from taxi map is based on more particularly, to one kind
Matched trip space-time analysis method.
Background technology
Taxi performer key player in Urban Transportation, by taking Guangzhou as an example, taxi about more than 1.7 ten thousand daily
Traveling almost covers city main roads, and whole day has car operation for 24 hours on road, data in city road network
Magnanimity, coverage rate is high, and the duration is long.Relative to other as the data such as cell phone data, bluetooth, public transport reservation data acquire
Taxi is carried out traffic information collection by method, can realize that traffic state analysis is predicted, space division during trip
Analysis, road speeds estimation and taxi operation management etc., has unique advantage.It is perceived improving urban road traffic state,
Alleviate traffic congestion, reduce traffic pollution and convenience-for-people trip, provide data supporting for smart city, wisdom traffic construction, especially
Be improve traffic planning and management it is scientific, become more meticulous aspect be of great significance.
The information such as the floating vehicle data record car plate of vehicle, longitude and latitude, time, direction, speed, vehicle running state,
Wherein location information of the longitude and latitude as original records vehicle location, location information error is big, often deviating road it is larger away from
From, particularly built-up urban road center, the drift distance of generation bigger due to signal blocks etc..This is because
On the one hand civilian GPS positioning device precision is generally relatively low and device itself failure etc. caused by error；On the other hand, city
The building of road both sides, trees block can all weaken GPS device send and receive signal so as to generate drift.Therefore, it floats
The initial data at vehicle returned data center is often second-rate, it is difficult to accurately reflect vehicle traveling-position and driving trace, need
Necessary correcting process is carried out by technologies such as map match.
As universal and GIS-Geographic Information System (GIS) of global positioning system (GPS) technology in mobile unit is in traffic
The expansion application in field carries out the floating car technology of information collection to be mounted with the taxi of GPS R-T units or bus
Obtained unprecedented development, by extract Floating Car acquire data returned data administrative center, then carry out data processing,
Figure matching, data mining obtain the traffic informations such as traffic behavior, can realize urban highway traffic situation dynamic sensing, Yi Jijin
Row strategic road, junction traffic stream real-time monitoring.One of vehicles mainly gone on a journey as city dweller, taxi, which has, to be divided
Cloth range is wide, the features such as data magnanimity, can more accurate response resident trip space-time characteristic (such as spatial-temporal distribution characteristic, path
Select preference etc.).
Invention content
For overcome the deficiencies in the prior art, the present invention proposes a kind of based on the matched trip space-time of taxi GPS map
Analysis method, the method are that the information acquiring technology based on floating car data has low cost, are had a very wide distribution, data magnanimity, reality
The advantages that Shi Xinggao, is extracted, the process analysis procedure analyses such as map match, space-time analysis trip spatial-temporal distribution characteristic by data, for analysis
Urban transportation operation feature, traffic programme and management provide effective data supporting.
To achieve these goals, the technical scheme is that：
A kind of trip space-time analysis method based on GPS data from taxi map match, includes the following steps：
S1. GPS data from taxi is extracted, and GPS data from taxi is pre-processed；
S2. first buffering area searching is carried out to pretreated GPS data from taxi using buffering area searching method, is judged
Whether anchor point is confidence point, and when anchor point is confidence point, then candidate matches section is unique, which is vehicle driving trace
Section；When anchor point is untrusted, then candidate matches section is not unique, and secondary buffer is carried out using buffering area searching method
Area searching, and anchor point is matched in road；
S3. the GPS data from taxi after being matched using trip space-time analysis method to step S2 is analyzed, analysis trip
Spatial-temporal distribution characteristic.
Preferably, the data prediction mode in the step S1 is：According to the time of GPS data from taxi record, speed
Invalid, drift that degree and initial positioning information remove and the data point of exception.
Preferably, the exceptional data point refers to velocity anomaly point, i.e. speed is higher than V1It is data exception point, V1Unit
For km/h；Invalid number strong point refers to that position error is higher than h1Data point, h1Unit be m；Drift data point refers to and former point
The abnormal data point of distance, the data point for meeting formula (1) is drift data point：
(tc-tc-i)vmax≥Dis(Gc,Gc-i) (1)
Wherein, tc-tc-1≤Δtmax, i=1,2, L, m, c>M, vmaxFor the maximum speed of vehicle, Dis (Gc,Gc-i) be before
The air line distance of two anchor points afterwards, Δ tmaxTime difference for adjacent two anchor point；C is c-th of GPS point, and m is GPS data point
Number, tc-iRepresent the time of c-i data point record.
Preferably, the buffering area searching method in the step S2 is specially：Centered on anchor point, position error conduct
Search radius；The position error is distance of the anchor point to road axis.
Preferably, in the step S2 when anchor point is untrusted, then candidate matches section is not unique, using buffering
Area searching method carries out secondary buffer area searching, and anchor point is matched in road：It is therein that anchor point is matched into road
In refer to anchor point matching the higher section of matching weight, matching weighing computation method is as follows：W=e(d/r)/ln(θ/90)；
In formula, d is the shortest distance of the GPS point to section；θ represents the angle between vehicle course and section, 0≤θ<90；R is GPS poles
Limit reasonable error.
Preferably, in the step S3, trip space-time analysis method includes the recognition methods of spatial distribution average characteristics and sky
Between clustering method；
Spatial distribution characteristic recognition methods is using average arest neighbors index, is defined as formula (2)；
In formula,Represent taxi on-board and off-board point actual average nearest neighbor distance For a on-board and off-board
The expection average distance of pointdiFor the Euclidean distance between taxi on-board and off-board point i and closest approach, S is research
The area of road area, n are the number for studying on-board and off-board point in road web area；Finally, it calculates Z and obtains fraction (3), for judging
The spatial characteristics of on-board and off-board point are in aggregation or discrete state；
If zANN>1, object is discrete；If zANN<1, target point is aggregation；
The spatial clustering method is K-means spatial clustering methods, for after whether judgement on-board and off-board point is aggregation,
Calculate the cluster centre of on-board and off-board point.
K mean cluster algorithm is a kind of clustering algorithm of partition type, and principle is utilizes the Europe between Floating Car anchor point
Formula distance is clustered as similarity, sets up trip space Clustering Model on this basis, process is as follows：
Then setting stopping criterion for iteration (such as maximum cycle) first determines that initial cluster center (can select at random
Take), and other points are calculated with the Euclidean distance of initial cluster center as similarity, anchor point is distributed according to similarity size
To new one kind is generated near cluster, update cluster centre re-starts classification, until meeting end condition position.
The present invention is a kind of new map-matching method, can improve map match efficiency, and establishes trip space analysis
Method analyzes trip of taxi space-time analysis, and to hold city operations feature, traffic programme provides necessary basic data branch
Support.
Compared with prior art, advantages of the present invention is mainly reflected in the following aspects：
1) data volume is sufficient, and primary GPS data is sent per 10-20s using taxi, carries out analysis trip space-time characteristic
Signature analysis, data are sufficient, and conclusion is reliable.
2) it is efficient, by historical data analysis, trip of taxi error distribution characteristics is analyzed, builds binary search mould
Type improves the efficiency of road map match.
3) method operability is high.Method fully takes into account drifting about, being different for data based on common GPS data from taxi
Features, the operability such as normal are strong.
4) by identifying taxi spatial-temporal distribution characteristic, cluster operation, effecting reaction urban transportation operation conditions and can go out
Row spatial and temporal distributions characteristic.
Description of the drawings
Fig. 1 is the flow chart of the present invention.
Fig. 2 is taxi GPS positioning point map match schematic diagram.
Fig. 3 is taxi GPS positioning cumulative errors distribution map.
Fig. 4 is positioning dot buffer zone-candidate road section analysis chart.
Fig. 5 is taxi spatial distribution characteristic identification figure.
Fig. 6 is trip of taxi time distribution map.
Fig. 7 is trip space cluster result schematic diagram.
Specific embodiment
The present invention will be further described below in conjunction with the accompanying drawings, but embodiments of the present invention are not limited to this.
Such as Fig. 1, a kind of trip space-time analysis method based on GPS data from taxi map match includes the following steps：
S1. GPS data from taxi is extracted, and GPS data from taxi is pre-processed；
S2. first buffering area searching is carried out to pretreated GPS data from taxi using buffering area searching method, is judged
Whether anchor point is confidence point, and when anchor point is confidence point, then candidate matches section is unique, which is vehicle driving trace
Section；When anchor point is untrusted, then candidate matches section is not unique, and secondary buffer is carried out using buffering area searching method
Area searching, and anchor point is matched in road；
S3. the GPS data from taxi after being matched using trip space-time analysis method to step S2 is analyzed, analysis trip
Spatial-temporal distribution characteristic.
In the present embodiment, the data prediction mode in step S1 is：According to GPS data from taxi record when
Between, the data point of speed and initial positioning information remove invalid, drift and exception；The exceptional data point refers to that speed is different
Chang Dian, i.e. speed are higher than V1It is data exception point, V1Unit is km/h；V1=200km/h, invalid number strong point refer to that positioning misses
Difference is higher than h1Data point, h1Unit be m, h1=100m；Drift data point refers to the data point abnormal with former point distance,
The data point for meeting formula (1) is drift data point：
(tc-tc-i)vmax≥Dis(Gc,Gc-i) (1)
Wherein, tc-tc-1≤Δtmax, i=1,2, L, m, c>M, vmaxMaximum speeds of=the 100km/h for vehicle, Dis (Gc,
Gc-i) be front and rear two anchor point air line distance, Δ tmaxFor the time difference of adjacent two anchor point, it is set as 180min；C is c
A GPS point, numbers of the m for GPS data point, tc-iRepresent the time of c-i data point record.
Buffering area searching method in the step S2 is specially：Centered on anchor point, position error is as search half
Diameter；The position error is distance of the anchor point to road axis.
In the step S2 when anchor point is untrusted, then candidate matches section is not unique, using buffering area searching
Method carries out secondary buffer area searching, and anchor point is matched in road：Therein match anchor point in road refers to
Anchor point is matched into the higher section of matching weight, matching weighing computation method is as follows：W=e(d/r)/ln(θ/90)；In formula, d
The shortest distance for GPS point to section；θ represents the angle between vehicle course and section, 0≤θ<90；R is reasonable for the GPS limit
Error.
In the step S3, trip space-time analysis method includes the recognition methods of spatial distribution average characteristics and space clustering side
Method；
Spatial distribution characteristic recognition methods is using average arest neighbors index, is defined as formula (2)；
In formula,Represent taxi on-board and off-board point actual average nearest neighbor distance For a on-board and off-board
The expection average distance of pointdiFor the Euclidean distance between taxi on-board and off-board point i and closest approach, S is research
The area of road area, n are the number for studying on-board and off-board point in road web area；Finally, it calculates Z and obtains fraction (3), for judging
The spatial characteristics of on-board and off-board point are in aggregation or discrete state；
If zANN>1, object is discrete；If zANN<1, target point is aggregation；
The spatial clustering method is K-means spatial clustering methods, for after whether judgement on-board and off-board point is aggregation,
Calculate the cluster centre of on-board and off-board point.
Embodiment 1
Using all GPS data from taxi of Guangzhou, Guangdong Province, China city electronic map and Guangzhou some day as Floating Car
Data carry out route matching simulation analysis.Initial data sample rate is 20~30s, when taxi is in passenger carrying status, vehicle
A GPS data is sent per 20s, a GPS data is sent during in complete vehicle curb condition per 30s, when the state of vehicle changes
When, GPS device sends a GPS data immediately, but due to that partial data point can be caused to lose in acquisition transmission process,
GPS sample rates are relatively low used by experiment, belong to the floating car data of low sampling rate.Concrete operation step is：
Step 1：GPS data from taxi pre-processes
1) data used are valid data, that is, the data effective word segment value in testing is 1；
2) leave out repeated data, if two GPS data car plates are identical, the time is identical, then this two data is repeats
Data；
3) aerial in traveler Path selection, the state of location data is carrying, i.e., (4 are passenger carrying status data for 4
Finger-like state digit value)；
4) leave out drift data, by the location data of same vehicle, the time in sample range, and with former point apart from different
Normal data point is defined as shift point, is represented with following formula (1)：
C is c-th of GPS point, and m is the number of GPS data point, tc-iRepresent the time of c-i data point record.Wherein vmax
=100km/h, for the maximum speed of vehicle, Dis (Gc,Gc-i) be front and rear two anchor point air line distance, Δ tmaxAdjacent two is fixed
The time difference in site, it is set as 180min.tcRepresent c-th of anchor point time.
Step 2：Historical data analysis position error is distributed
Distance using anchor point to road axis is for statistical analysis as error.The results are shown in Figure 3, can see
Go out, most Floating Car position error accounts for 85% within 20m, more than 95% anchor point position error within 40m,
Floating Car within 30m accounts for more than 94%, and Floating Car of the position error within 70m accounts for 99.6%, therefore sets first
Buffering area radius is 30m, and secondary buffer area radius is 70m；Guangzhou 4925 GPS datas some day are chosen, are by error
The GPS data of more than 100m deletes residue GPS numbers totally 4082 after shift point as elegant point deletion.Then GPS point is carried out
Buffer zone analysis, respectively with 5m, 10m, 15m, 20m, 30m, 90m as radius analyze the relationship in point to be matched and section,
The results are shown in Figure 4, when buffer size increases to 30m, confidence point (positioning dot buffer zone internal candidates section is 1) institute's accounting
Example highest, but still it is 0 to have partial data candidate road section；As buffering area radius further increases, confidence is counted out reduction, and is delayed
The section that area's internal link number is 0 is rushed also to reduce therewith.Under normal circumstances, candidate road section is more, and calculation amount is also bigger, but zero
Matched data can not match anchor point in correct section, therefore 30m is set as initial search radius, work as candidate road section
When number is 0, then 70m is selected as search radius, into row buffer binary search.
Step 3：Map match
GPS data from taxi has recorded the state of vehicle, and mode field CAR_STATE1 has 7 kinds of states, respectively
" 1 " is anti-robbery；" 2 " register；" 3 " are sign-out；" 4 " empty wagons, i.e. empty driving；" 5 " loaded vehicle, i.e. carrying；" 6 " light a fire；" 7 " are flame-out；Vehicle is set
The point when value of mode bit is switched to " 5 " by " 4 " is upper passenger station's point, and the point that " 4 " are switched to by " 5 " is lower passenger station's point, is carried out
On-board and off-board point data is extracted, then into matching.
Step 4：Trip Spatial And Temporal Characteristics
By extracting 6 in one day:00-24:00 Floating Car on-board and off-board locating point data is matched, and calculates on-board and off-board point
Average arest neighbors rate.The intraday taxi on-board and off-board space of points is calculated using average arest neighbors statistical method to be distributed, as a result
As shown in figure 5, average arest neighbors index is 0.097506, far below 1, and z is scored at -440.699338, and value is less than 1.Cause
This can show that taxi on-board and off-board point is assembled in space layout listing, and p is scored at 0, i.e. taxi on-board and off-board point is discrete
Probability is 0.
Often with the population in the region, economy and land character are related for taxi on-board and off-board hot spot region.Fig. 1 Greens
Represent the high region of line density, yellow is second highest region domain, the trip main integrated distribution of hot spot in the collecting and distributing place of stream of people's high speed,
Such as regions such as airport, railway station, commercial center, city function core spaces.
Travel time distributional analysis is the important embodiment of trip of taxi space-time analysis, when probing into taxi on-board and off-board website
The empty regularity of distribution, prostitution of rationally cruising for taxi driver is made rational planning for taxi waiting region to be of great significance.It is real
It tests and acquires (working day) 63 days June 2014 Guangzhou festivals or holidays:00:00-24:00:00 Floating Car on-board and off-board website is seen
Its Time-distribution is examined, carries out statistics below figure 6.
On-board and off-board space of points cluster result is created into figure layer and is loaded on ArcGIS road networks, can intuitively be identified
Go out the main region of on-board and off-board, be illustrated in fig. 7 shown below, circle represents cluster centre in figure, and the size of the area of a circle represents case number (week
Lower passenger station is placed to count out size), it can be seen from the figure that, cluster centre is mainly distributed on the stream of peoples such as railway station sport west at a high speed
Collecting and distributing region.
Claims (3)
- A kind of 1. trip space-time analysis method based on GPS data from taxi map match, which is characterized in that including following step Suddenly：S1. GPS data from taxi is extracted, and GPS data from taxi is pre-processed；S2. first buffering area searching is carried out to pretreated GPS data from taxi using buffering area searching method, judges to position Whether point is confidence point, and when anchor point is confidence point, then candidate matches section is unique, which is vehicle driving trace road Section；When anchor point is untrusted, then candidate matches section is not unique, and secondary buffer area is carried out using buffering area searching method Search, and anchor point is matched in road；S3. the GPS data from taxi after being matched using trip space-time analysis method to step S2 is analyzed, analysis trip space-time Distribution characteristics；Data prediction mode in the step S1 is：Determine according to the time of GPS data from taxi record, speed and initially Invalid, drift that position information is removed and the data point of exception；The exceptional data point refers to velocity anomaly point, i.e. speed is higher than V1It is data exception point, V1Unit is km/h；In vain Data point refers to that position error is higher than h1Data point, h1Unit be m；Drift data point refers to and former point distance exception Data point, the data point for meeting formula (1) are drift data point：(tc-tc-i)vmax≥Dis(Gc,Gc-i) (1)Wherein, tc-tc-1≤Δtmax, i=1,2 ..., m, c ＞ m, vmaxFor the maximum speed of vehicle, Dis (Gc,Gc-i) it is front and rear The air line distance of two anchor points, Δ tmaxFor the time difference of adjacent two anchor point, c is c-th of GPS point, and m is the number of GPS data point Mesh, tc-iRepresent the time of c-i data point record；Buffering area searching method in the step S2 is specially：Centered on anchor point, position error is as search radius；Institute It is distance of the anchor point to road axis to state position error.
- 2. the trip space-time analysis method according to claim 1 based on GPS data from taxi map match, feature exist In, in the step S2 when anchor point be untrusted when, then candidate matches section is not unique, using buffering area searching method into Row secondary buffer area searching, and anchor point is matched in road：Therein match anchor point refers to position in road Point matches the higher section of matching weight, and matching weighing computation method is as follows：W=e(d/r)/ln(θ/90)；In formula, d GPS Point arrives the shortest distance in section；θ represents the angle between vehicle course and section, 0≤θ ＜ 90；R is GPS limit reasonable errors.
- 3. the trip space-time analysis method according to claim 1 based on GPS data from taxi map match, feature exist In in the step S3, trip space-time analysis method includes the recognition methods of spatial distribution average characteristics and spatial clustering method；Spatial distribution characteristic recognition methods is using average arest neighbors index, is defined as formula (2)；In formula,Represent taxi on-board and off-board point actual average nearest neighbor distance For the pre- of a on-board and off-board point Phase average distancediFor the Euclidean distance between taxi on-board and off-board point i and closest approach, S is research roadway area The area in domain, n are the number for studying on-board and off-board point in road web area；Finally, it calculates Z and obtains fraction (3), for judging on-board and off-board The spatial characteristics of point are in aggregation or discrete state；If zANN＞ 1, object are discrete；If zANN＜ 1, target point are aggregation；Whether the spatial clustering method is K-means spatial clustering methods, for being calculating after aggregation in judgement on-board and off-board point The cluster centre of on-board and off-board point.
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| CN106127662A (en) * | 2016-06-23 | 2016-11-16 | 福州大学 | A kind of system of selection of the K means initial cluster center for taxi track data |
| CN109923595B (en) * | 2016-12-30 | 2021-07-13 | 同济大学 | Urban road traffic abnormity detection method based on floating car data |
| CN106970945A (en) * | 2017-02-24 | 2017-07-21 | 河海大学 | A kind of track preprocess method of taxi data set |
| CN108253974B (en) * | 2017-12-29 | 2019-09-10 | 深圳市城市交通规划设计研究中心有限公司 | Floating Car location data automatic adaptation cushion route matching system and method |
| CN109035783B (en) * | 2018-09-17 | 2021-06-11 | 东南大学 | Virtual road network missing road section automatic identification method based on bus GPS track |
| CN109727449A (en) * | 2019-01-15 | 2019-05-07 | 安徽慧联运科技有限公司 | A kind of analysis method judging car operation situation according to vehicle driving position |
| CN109859516B (en) * | 2019-03-13 | 2021-06-15 | 重庆皓石金科技有限公司 | Taxi abnormal gathering identification method and device |
| CN109903561B (en) * | 2019-03-14 | 2021-05-07 | 智慧足迹数据科技有限公司 | Method and device for calculating pedestrian flow between road sections and electronic equipment |
| CN109979198B (en) * | 2019-04-08 | 2021-07-09 | 东南大学 | Urban expressway vehicle speed discrete identification method based on large-scale floating vehicle data |
| CN110264744B (en) * | 2019-06-13 | 2022-05-27 | 同济大学 | Traffic flow prediction algorithm based on multivariate data |
| CN110555992B (en) * | 2019-09-11 | 2021-05-28 | 中国矿业大学(北京) | Taxi driving path information extraction method based on GPS track data |
| CN110849379B (en) * | 2019-10-23 | 2023-04-25 | 南通大学 | Entrance and exit traffic state symbol expression method for navigation map |
| CN112712701B (en) * | 2021-01-06 | 2022-12-23 | 腾讯科技（深圳）有限公司 | Route determining method, device, equipment and storage medium based on identification device |
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