CN105261212A - Travel space-time analysis method based on taxi GPS data map matching - Google Patents
Travel space-time analysis method based on taxi GPS data map matching Download PDFInfo
- Publication number
- CN105261212A CN105261212A CN201510564032.9A CN201510564032A CN105261212A CN 105261212 A CN105261212 A CN 105261212A CN 201510564032 A CN201510564032 A CN 201510564032A CN 105261212 A CN105261212 A CN 105261212A
- Authority
- CN
- China
- Prior art keywords
- point
- data
- taxi
- board
- gps data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 34
- 238000000034 method Methods 0.000 claims abstract description 47
- 238000009826 distribution Methods 0.000 claims abstract description 29
- 230000008878 coupling Effects 0.000 claims description 10
- 238000010168 coupling process Methods 0.000 claims description 10
- 238000005859 coupling reaction Methods 0.000 claims description 10
- HUTDUHSNJYTCAR-UHFFFAOYSA-N ancymidol Chemical compound C1=CC(OC)=CC=C1C(O)(C=1C=NC=NC=1)C1CC1 HUTDUHSNJYTCAR-UHFFFAOYSA-N 0.000 claims description 6
- 238000011160 research Methods 0.000 claims description 6
- 238000013459 approach Methods 0.000 claims description 5
- 230000002159 abnormal effect Effects 0.000 claims description 4
- 238000005303 weighing Methods 0.000 claims description 3
- 238000007667 floating Methods 0.000 abstract description 15
- 238000009825 accumulation Methods 0.000 abstract 1
- 238000005516 engineering process Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 230000008901 benefit Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000007726 management method Methods 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 239000000523 sample Substances 0.000 description 2
- 230000002123 temporal effect Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 241001269238 Data Species 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 238000013075 data extraction Methods 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000012217 deletion Methods 0.000 description 1
- 230000037430 deletion Effects 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000003064 k means clustering Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 230000029305 taxis Effects 0.000 description 1
Landscapes
- Traffic Control Systems (AREA)
- Navigation (AREA)
Abstract
The invention provides a travel space-time analysis method based on taxi GPS data map matching. Original taxi GPS data are extracted and pretreatment on the data is carried out; historical data are analyzed, a taxi GPS positioning error accumulation distribution mode is determined, and a secondary buffer region searching method is constructed; taxi travel data are extracted and a positioning point is matched to a road by combining a taxi GPS data direction element and a distance element map establishment matching method; and a travel space distribution feature identification method is established and a travel space-time distribution feature is analyzed by using the matched taxi GPS data. According to the invention, the taxi GPS positioning error distribution feature is analyzed by using massive historical data fully; a taxi GPS positioning point matching efficiency is improved by constructing the secondary buffer region searching method; and a travel distribution effect can be identified and analyzed effectively by using the established distribution feature identification method. The method has the great significance in improving the taxi map matching effect, floating vehicle information collection, and a travel space-time analysis effect and a data support can be provided for the city traffic planning.
Description
Technical field
The present invention relates to traffic programme applied technical field, more specifically, relate to a kind of trip space-time analysis method based on GPS data from taxi map match.
Background technology
Taxi is performer key player in Urban Transportation, and for Guangzhou, every day, about more than 1.7 ten thousand taxi travelled in city road network, nearly cover city main roads, and whole day all has car operation for 24 hours on road, data magnanimity, coverage rate is high, and the duration is long.Relative to other as cell phone data, bluetooth, the collecting methods such as public transport reservation data, taxi is carried out traffic information collection as Floating Car, traffic state analysis prediction can be realized, trip space-time analysis, road speeds is estimated and taxi operation management etc., has unique advantage.In raising urban road traffic state perception, alleviate congested in traffic, reduce traffic pollution and convenience-for-people trip, for smart city, wisdom transport development provide data supporting, particularly scientific at raising traffic planning and management, become more meticulous in significant.
The floating vehicle data record information such as the car plate of vehicle, longitude and latitude, time, direction, speed, vehicle running state, wherein longitude and latitude is as the locating information of original records vehicle location, locating information error is large, the often larger distance of deviating road, particularly built-up urban road center, because the reasons such as signal blocks produce larger drift distance.This is because on the one hand civilian GPS locating device precision is general lower, and the error that the own fault of device etc. causes; On the other hand, the building of urban road both sides, trees block the reception and transmission signal thus generation drift that all can weaken GPS device.Therefore, the raw data at Floating Car returned data center is often second-rate, is difficult to accurately reflect vehicle traveling-position and driving trace, needs to carry out necessary correcting process by technology such as map match.
Along with universal and Geographic Information System (GIS) expansive approach at field of traffic of GPS (GPS) technology in mobile unit, the floating car technology carrying out information acquisition with the taxi or bus that are mounted with GPS R-T unit obtains unprecedented development, by extracting the data returned data administrative center that Floating Car gathers, then the transport information such as data processing, map match, data mining acquisition traffic behavior are carried out, urban highway traffic situation dynamic sensing can be realized, and carry out strategic road, junction traffic stream real-time monitoring.As one of vehicles that city dweller mainly goes on a journey, taxi has and has a very wide distribution, the features such as data magnanimity, can comparatively accurate response resident trip space-time characteristic (as spatial-temporal distribution characteristic, routing preference etc.).
Summary of the invention
In order to overcome the deficiencies in the prior art, the present invention proposes a kind of trip space-time analysis method based on taxi GPS map match, this method has low cost based on the information acquiring technology of floating car data, have a very wide distribution, data magnanimity, real-time advantages of higher, by process analysis procedure analysis trip spatial-temporal distribution characteristic such as data extraction, map match, space-time analysis, for analysis city traffic operation feature, traffic programme and management provide effective data supporting.
To achieve these goals, technical scheme of the present invention is:
Based on a trip space-time analysis method for GPS data from taxi map match, comprise the following steps:
S1. extract GPS data from taxi, and pre-service is carried out to GPS data from taxi;
S2. adopt buffer zone searching method to carry out the search of first buffer zone to pretreated GPS data from taxi, judge whether anchor point is confidence point, when anchor point is confidence point, then candidate matches section is unique, and this section is vehicle driving trace section; When anchor point is non-confidence point, then candidate matches section is not unique, adopts buffer zone searching method to carry out secondary buffer area searching, and matches in road by anchor point;
S3. adopt trip space-time analysis method to analyze the GPS data from taxi after step S2 coupling, analyze trip spatial-temporal distribution characteristic.
Preferably, the data prediction mode in described step S1 is: the data point removing invalid, drift and exception according to time of GPS data from taxi record, speed and initial positioning information.
Preferably, described exceptional data point refers to velocity sag point, and namely speed is higher than V
1be data exception point, V
1unit is km/h; Invalid number strong point refers to that positioning error is higher than h
1data point, h
1unit be m; Drift data point refers to the data point abnormal with more front distance, and the data point meeting formula (1) is drift data point:
(t
c-t
c-i)v
max≥Dis(G
c,G
c-i)(1)
Wherein, t
c-t
c-1≤ Δ t
max, i=1,2, L, m, c>m, v
maxfor the maximal rate of vehicle, Dis (G
c, G
c-i) be the air line distance of front and back two anchor point, Δ t
maxfor the mistiming of adjacent two anchor points; C is c GPS point, and m is the number of gps data point, t
c-irepresent the time of c-i data point record.
Preferably, the buffer zone searching method in described step S2 is specially: centered by anchor point, positioning error is as search radius; Described positioning error is the distance of anchor point to road axis.
Preferably, in described step S2 when anchor point is non-confidence point, then candidate matches section is not unique, buffer zone searching method is adopted to carry out secondary buffer area searching, and anchor point is matched in road: being matched in road by anchor point wherein refers to and anchor point is matched the higher section of coupling weight, and coupling weighing computation method is as follows: W=e
(d/r)/ ln (θ/90); In formula, d is the bee-line of GPS point to section; θ represents the angle between vehicle course and section, 0≤θ <90; R is GPS limit reasonable error.
Preferably, in described step S3, trip space-time analysis method comprises the recognition methods of space distribution average characteristics and spatial clustering method;
Spatial distribution characteristic recognition methods utilizes average arest neighbors index, is defined as formula (2);
In formula,
indicate on-board and off-board point actual average nearest neighbor distance of hiring a car
for the expection mean distance of individual on-board and off-board point
d
ifor the Euclidean distance between taxi on-board and off-board point i and closest approach, S is the area of research road area, and n is the number of on-board and off-board point in research road net region; Finally, calculating Z and obtain fraction (3), being used for judging that the spatial characteristics of on-board and off-board point is in assembling or discrete state;
If z
aNN>1, object is discrete; If z
aNN<1, impact point is for assembling;
Described spatial clustering method is K-means spatial clustering method, for after judging that on-board and off-board point is whether as gathering, calculates the cluster centre of on-board and off-board point.
K means clustering algorithm is a kind of clustering algorithm of partition type, and its principle is utilize the Euclidean distance between Floating Car anchor point to carry out cluster as similarity, and set up trip space Clustering Model on this basis, its process is as follows:
First stopping criterion for iteration (as maximum cycle etc.) is set, then initial cluster center (can random selecting) is determined, and calculate other points with the Euclidean distance of initial cluster center as similarity, be assigned to apart from generating a new class near cluster according to similarity large young pathbreaker anchor point, upgrade cluster centre and re-start classification, until meet end condition position.
The present invention is a kind of new map-matching method, can improve map match efficiency, and sets up trip space analytical approach, analyzes trip of taxi space-time analysis, and for holding city operations feature, traffic programme provides necessary basic data to support.
Compared with prior art, advantage of the present invention is mainly reflected in the following aspects:
1) data volume is sufficient, and utilize the every 10-20s transmission of taxi gps data once, carry out analysis trip space-time characteristic signature analysis, data are sufficient, and conclusion is reliable.
2) efficiency is high, by historical data analysis, analyzes trip of taxi error distribution characteristics, builds binary search model, improve the efficiency of road map match.
3) method operability is high.Method, based on common GPS data from taxi, fully takes into account the features such as the drift of data, exception, workable.
4) by identifying spatial-temporal distribution characteristic of hiring a car, cluster computing, can effecting reaction urban transportation operation conditions and trip spatial and temporal distributions characteristic.
Accompanying drawing explanation
Fig. 1 is process flow diagram of the present invention.
Fig. 2 is taxi GPS anchor point map match schematic diagram.
Fig. 3 is that taxi GPS locates cumulative errors distribution plan.
Fig. 4 is anchor point 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.
Embodiment
Below in conjunction with accompanying drawing, the present invention will be further described, but embodiments of the present invention are not limited to this.
As Fig. 1, a kind of trip space-time analysis method based on GPS data from taxi map match, comprises the following steps:
S1. extract GPS data from taxi, and pre-service is carried out to GPS data from taxi;
S2. adopt buffer zone searching method to carry out the search of first buffer zone to pretreated GPS data from taxi, judge whether anchor point is confidence point, when anchor point is confidence point, then candidate matches section is unique, and this section is vehicle driving trace section; When anchor point is non-confidence point, then candidate matches section is not unique, adopts buffer zone searching method to carry out secondary buffer area searching, and matches in road by anchor point;
S3. adopt trip space-time analysis method to analyze the GPS data from taxi after step S2 coupling, analyze trip spatial-temporal distribution characteristic.
In the present embodiment, the data prediction mode in step S1 is: the data point removing invalid, drift and exception according to time of GPS data from taxi record, speed and initial positioning information; Described exceptional data point refers to velocity sag point, and namely speed is higher than V
1be data exception point, V
1unit is km/h; V
1=200km/h, invalid number strong point refers to that positioning error is higher than h
1data point, h
1unit be m, h
1=100m; Drift data point refers to the data point abnormal with more front distance, and the data point meeting formula (1) is drift data point:
(t
c-t
c-i)v
max≥Dis(G
c,G
c-i)(1)
Wherein, t
c-t
c-1≤ Δ t
max, i=1,2, L, m, c>m, v
max=100km/h is the maximal rate of vehicle, Dis (G
c, G
c-i) be the air line distance of front and back two anchor point, Δ t
maxfor the mistiming of adjacent two anchor points, be set to 180min; C is c GPS point, and m is the number of gps data point, t
c-irepresent the time of c-i data point record.
Buffer zone searching method in described step S2 is specially: centered by anchor point, positioning error is as search radius; Described positioning error is the distance of anchor point to road axis.
In described step S2 when anchor point is non-confidence point, then candidate matches section is not unique, buffer zone searching method is adopted to carry out secondary buffer area searching, and anchor point is matched in road: being matched in road by anchor point wherein refers to and anchor point is matched the higher section of coupling weight, and coupling weighing computation method is as follows: W=e
(d/r)/ ln (θ/90); In formula, d is the bee-line of GPS point to section; θ represents the angle between vehicle course and section, 0≤θ <90; R is GPS limit reasonable error.
In described step S3, trip space-time analysis method comprises the recognition methods of space distribution average characteristics and spatial clustering method;
Spatial distribution characteristic recognition methods utilizes average arest neighbors index, is defined as formula (2);
In formula,
indicate on-board and off-board point actual average nearest neighbor distance of hiring a car
for the expection mean distance of individual on-board and off-board point
d
ifor the Euclidean distance between taxi on-board and off-board point i and closest approach, S is the area of research road area, and n is the number of on-board and off-board point in research road net region; Finally, calculating Z and obtain fraction (3), being used for judging that the spatial characteristics of on-board and off-board point is in assembling or discrete state;
If z
aNN>1, object is discrete; If z
aNN<1, impact point is for assembling;
Described spatial clustering method is K-means spatial clustering method, for after judging that on-board and off-board point is whether as gathering, calculates the cluster centre of on-board and off-board point.
Embodiment 1
Guangzhou, Guangdong Province, China city electronic chart and all GPS data from taxis in Guangzhou some day is adopted to carry out route matching simulation analysis as floating car data.Raw data sampling rate is 20 ~ 30s, when taxi is in passenger carrying status, the every 20s of vehicle sends a gps data, when being in complete vehicle curb condition, every 30s sends a gps data, when the state of vehicle changes, GPS device sends a gps data immediately, but due to partial data point can be caused in collect and transmit process to lose, therefore test the GPS sampling rate adopted lower, belong to the floating car data of low sampling rate.Concrete operation step is:
Step 1: GPS data from taxi pre-service
1) data used are valid data, and the data effective word segment value namely in test is 1;
2) leave out repeating data, if two gps data car plate is identical, the time is identical, so these two data are attached most importance to complex data;
3) aerial when traveler routing, the state of locator data is carrying, and namely passenger carrying status data are 4 (4 refer to status number place value);
4) leave out drift data, by the locator data of same car, the time is in sample range, and the data point abnormal with more front distance is defined as shift point, represents by formula (1) below:
C is c GPS point, and m is the number of gps data point, t
c-irepresent the time of c-i data point record.Wherein v
max=100km/h is the maximal rate of vehicle, Dis (G
c, G
c-i) be the air line distance of front and back two anchor point, Δ t
maxthe mistiming of adjacent two anchor points, be set to 180min.T
crepresent c anchor point time.
Step 2: historical data analysis positioning error distributes
Using anchor point to the distance of road axis as error, carry out statistical study.Result as shown in Figure 3, can find out, most Floating Car positioning error is within 20m, account for 85%, the anchor point positioning error of more than 95% is within 40m, and the Floating Car within 30m account for more than 94%, and the Floating Car of positioning error within 70m accounts for 99.6%, therefore arranging first buffer zone radius is 30m, and secondary buffer district radius is 70m; Choose Guangzhou 4925 gps datas some day, using error be the gps data of more than 100m as elegant point deletion, delete after shift point and remain GPS number totally 4082.Then the analysis of GPS dot buffer zone is carried out, respectively with 5m, 10m, 15m, 20m, 30m, 90m carries out as radius the relation analyzing point to be matched and section, result as shown in Figure 4, when buffer size is increased to 30m, confidence point (internal candidates section, anchor point buffer zone is 1) proportion is the highest, but still has partial data candidate road section to be 0; Along with buffer zone radius increases further, confidence is counted out minimizing, and the section that buffer zone internal link number is 0 is also reduced thereupon.Generally, candidate road section is more, and calculated amount is also larger, but anchor point cannot match in correct section by zero matched data, therefore 30m is set to initial search radius, when candidate road section number is 0, then select 70m to be search radius, carry out buffer zone binary search.
Step 3: map match
GPS data from taxi have recorded the state of vehicle, and mode field is CAR_STATE1, has 7 kinds of states, be respectively " 1 " anti-robbery; " 2 " register; " 3 " are sign-out; " 4 " empty wagons, namely sky is sailed; " 5 " loaded vehicle, i.e. carrying; " 6 " light a fire; " 7 " stop working; The point when value arranging vehicle-state position is switched to " 5 " by " 4 " is upper passenger station point, and the point being switched to " 4 " by " 5 " is lower passenger station point, carries out the extraction of on-board and off-board point data, then enters coupling.
Step 4: trip Spatial And Temporal Characteristics
Mated by the Floating Car on-board and off-board locating point data extracting 6:00-24:00 in a day, calculate the average arest neighbors rate of on-board and off-board point.Utilize average arest neighbors statistical method to calculate the distribution of the intraday taxi on-board and off-board space of points, as shown in Figure 5, average arest neighbors index is 0.097506 to result, and far below 1, and z must be divided into-440.699338, and its value is less than 1.Therefore can draw, taxi on-board and off-board point is assembled in space layout's listing, and p must be divided into 0, and the probability that namely taxi on-board and off-board point is discrete is 0.
Taxi on-board and off-board hot spot region often with the population in this region, economic and land character is correlated with.Fig. 1 Green indicates the high region of line density, and yellow is territory, second highest region, trip focus main integrated distribution in the collecting and distributing place of stream of people's high speed, as airport, railway station, the regions such as commercial center, city function core space.
Travel time distributional analysis is the important embodiment of trip of taxi space-time analysis, probes into taxi on-board and off-board website time space distribution, rationally cruises to receive lodgers make rational planning for significant with taxi waiting region for taxi driver.The Floating Car on-board and off-board website that experiment acquires Guangzhou June 3 (working day) 6:00:00-24:00:00 2014 festivals or holidays observes its Time-distribution, carries out adding up following Fig. 6.
The newly-built layer of on-board and off-board space of points cluster result is loaded on ArcGIS road network, the main region of on-board and off-board can be identified intuitively, be illustrated in fig. 7 shown below, in figure, circle represents cluster centre, the size of the area of a circle represents case number (station number of on-board and off-board around size), can find out in figure, cluster centre is mainly distributed in the region that physical culture west, railway station waits stream of people's high speed collecting and distributing.
Claims (6)
1., based on a trip space-time analysis method for GPS data from taxi map match, it is characterized in that, comprise the following steps:
S1. extract GPS data from taxi, and pre-service is carried out to GPS data from taxi;
S2. adopt buffer zone searching method to carry out the search of first buffer zone to pretreated GPS data from taxi, judge whether anchor point is confidence point, when anchor point is confidence point, then candidate matches section is unique, and this section is vehicle driving trace section; When anchor point is non-confidence point, then candidate matches section is not unique, adopts buffer zone searching method to carry out secondary buffer area searching, and matches in road by anchor point;
S3. adopt trip space-time analysis method to analyze the GPS data from taxi after step S2 coupling, analyze trip spatial-temporal distribution characteristic.
2. the trip space-time analysis method based on GPS data from taxi map match according to claim 1, it is characterized in that, the data prediction mode in described step S1 is: the data point removing invalid, drift and exception according to time of GPS data from taxi record, speed and initial positioning information.
3. the trip space-time analysis method based on GPS data from taxi map match according to claim 2, it is characterized in that, described exceptional data point refers to velocity sag point, and namely speed is higher than V
1be data exception point, V
1unit is km/h; Invalid number strong point refers to that positioning error is higher than h
1data point, h
1unit be m; Drift data point refers to the data point abnormal with more front distance, and the data point meeting formula (1) is drift data point:
(t
c-t
c-i)v
max≥Dis(G
c,G
c-i)(1)
Wherein, t
c-t
c-1≤ Δ t
max, i=1,2 ..., m, c>m, v
maxfor the maximal rate of vehicle, Dis (G
c, G
c-i) be the air line distance of front and back two anchor point, Δ t
maxfor the mistiming of adjacent two anchor points, c is c GPS point, and m is the number of gps data point, t
c-irepresent the time of c-i data point record.
4. the trip space-time analysis method based on GPS data from taxi map match according to any one of claims 1 to 3, it is characterized in that, the buffer zone searching method in described step S2 is specially: centered by anchor point, positioning error is as search radius; Described positioning error is the distance of anchor point to road axis.
5. the trip space-time analysis method based on GPS data from taxi map match according to claim 4, it is characterized in that, in described step S2 when anchor point is non-confidence point, then candidate matches section is not unique, buffer zone searching method is adopted to carry out secondary buffer area searching, and anchor point is matched in road: being matched in road by anchor point wherein refers to and anchor point is matched the higher section of coupling weight, and coupling weighing computation method is as follows: W=e
(d/r)/ ln (θ/90); In formula, d is the bee-line of GPS point to section; θ represents the angle between vehicle course and section, 0≤θ <90; R is GPS limit reasonable error.
6. the trip space-time analysis method based on GPS data from taxi map match according to claim 1, is characterized in that, in described step S3, trip space-time analysis method comprises the recognition methods of space distribution average characteristics and spatial clustering method;
Spatial distribution characteristic recognition methods utilizes average arest neighbors index, is defined as formula (2);
In formula,
indicate on-board and off-board point actual average nearest neighbor distance of hiring a car
for the expection mean distance of individual on-board and off-board point
d
ifor the Euclidean distance between taxi on-board and off-board point i and closest approach, S is the area of research road area, and n is the number of on-board and off-board point in research road net region; Finally, calculating Z and obtain fraction (3), being used for judging that the spatial characteristics of on-board and off-board point is in assembling or discrete state;
If z
aNN>1, object is discrete; If z
aNN<1, impact point is for assembling;
Described spatial clustering method is K-means spatial clustering method, for after judging that on-board and off-board point is whether as gathering, calculates the cluster centre of on-board and off-board point.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510564032.9A CN105261212B (en) | 2015-09-06 | 2015-09-06 | A kind of trip space-time analysis method based on GPS data from taxi map match |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510564032.9A CN105261212B (en) | 2015-09-06 | 2015-09-06 | A kind of trip space-time analysis method based on GPS data from taxi map match |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105261212A true CN105261212A (en) | 2016-01-20 |
| CN105261212B CN105261212B (en) | 2018-06-19 |
Family
ID=55100879
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510564032.9A Expired - Fee Related CN105261212B (en) | 2015-09-06 | 2015-09-06 | A kind of trip space-time analysis method based on GPS data from taxi map match |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105261212B (en) |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN106970945A (en) * | 2017-02-24 | 2017-07-21 | 河海大学 | A kind of track preprocess method of taxi data set |
| CN108253974A (en) * | 2017-12-29 | 2018-07-06 | 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 |
| CN109035783A (en) * | 2018-09-17 | 2018-12-18 | 东南大学 | A kind of virtual networks missing section automatic identifying method based on public transport 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 |
| CN109844832A (en) * | 2016-12-30 | 2019-06-04 | 同济大学 | A kind of multi-modal accident detection method based on journey time distribution |
| CN109859516A (en) * | 2019-03-13 | 2019-06-07 | 重庆皓石金科技有限公司 | A kind of taxi abnormal aggregation recognition methods and device |
| CN109903561A (en) * | 2019-03-14 | 2019-06-18 | 智慧足迹数据科技有限公司 | Flow of the people calculation method, device and electronic equipment between section |
| CN109979198A (en) * | 2019-04-08 | 2019-07-05 | 东南大学 | Urban express way speed scattering discrimination method based on large scale floating vehicle data |
| CN110264744A (en) * | 2019-06-13 | 2019-09-20 | 同济大学 | A kind of Traffic volume forecasting algorithm based on multivariate data |
| CN110555992A (en) * | 2019-09-11 | 2019-12-10 | 中国矿业大学(北京) | taxi driving path information extraction method based on GPS track data |
| CN110849379A (en) * | 2019-10-23 | 2020-02-28 | 南通大学 | Access and exit traffic state symbol expression method for navigation map |
| CN112712701A (en) * | 2021-01-06 | 2021-04-27 | 腾讯科技（深圳）有限公司 | Route determining method, device, equipment and storage medium based on identification device |
| CN113743908A (en) * | 2021-09-13 | 2021-12-03 | 中化现代农业有限公司 | Method and system for automatically matching plots |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103218672A (en) * | 2013-03-24 | 2013-07-24 | 吉林大学 | Taxi cruising action analytical method based on global position system (GPS) data grid statistic |
-
2015
- 2015-09-06 CN CN201510564032.9A patent/CN105261212B/en not_active Expired - Fee Related
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103218672A (en) * | 2013-03-24 | 2013-07-24 | 吉林大学 | Taxi cruising action analytical method based on global position system (GPS) data grid statistic |
Non-Patent Citations (2)
| Title |
|---|
| 吴旭彦; 罗德安: "GPS实时地图匹配技术中缓冲区分析的研究", 《西南交通大学学报》 * |
| 涂家伟: "基于浮动车技术的道路交通特性分析及系统实现", 《中国优秀硕士学位论文全文数据库》 * |
Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| CN109844832B (en) * | 2016-12-30 | 2021-06-15 | 同济大学 | Multi-mode traffic anomaly detection method based on travel time distribution |
| CN109844832A (en) * | 2016-12-30 | 2019-06-04 | 同济大学 | A kind of multi-modal accident detection method based on journey time distribution |
| CN106970945A (en) * | 2017-02-24 | 2017-07-21 | 河海大学 | A kind of track preprocess method of taxi data set |
| CN108253974A (en) * | 2017-12-29 | 2018-07-06 | 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 |
| 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 |
| CN109035783A (en) * | 2018-09-17 | 2018-12-18 | 东南大学 | A kind of virtual networks missing section automatic identifying method based on public transport 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 |
| CN109859516A (en) * | 2019-03-13 | 2019-06-07 | 重庆皓石金科技有限公司 | A kind of taxi abnormal aggregation recognition methods and device |
| CN109903561A (en) * | 2019-03-14 | 2019-06-18 | 智慧足迹数据科技有限公司 | Flow of the people calculation method, device and electronic equipment between section |
| CN109979198A (en) * | 2019-04-08 | 2019-07-05 | 东南大学 | Urban express way speed scattering discrimination method based on large scale floating vehicle data |
| CN110264744A (en) * | 2019-06-13 | 2019-09-20 | 同济大学 | A kind of Traffic volume forecasting algorithm based on multivariate data |
| CN110264744B (en) * | 2019-06-13 | 2022-05-27 | 同济大学 | Traffic flow prediction algorithm based on multivariate data |
| CN110555992A (en) * | 2019-09-11 | 2019-12-10 | 中国矿业大学(北京) | taxi driving path information extraction method based on GPS track 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 |
| CN110849379A (en) * | 2019-10-23 | 2020-02-28 | 南通大学 | Access and exit traffic state symbol expression method for navigation map |
| CN110849379B (en) * | 2019-10-23 | 2023-04-25 | 南通大学 | Entrance and exit traffic state symbol expression method for navigation map |
| CN112712701A (en) * | 2021-01-06 | 2021-04-27 | 腾讯科技（深圳）有限公司 | Route determining method, device, equipment and storage medium based on identification device |
| CN113743908A (en) * | 2021-09-13 | 2021-12-03 | 中化现代农业有限公司 | Method and system for automatically matching plots |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105261212B (en) | 2018-06-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105261212B (en) | A kind of trip space-time analysis method based on GPS data from taxi map match | |
| CN103177561B (en) | Method for generating bus real-time traffic status | |
| CN111091720B (en) | Congestion road section identification method and device based on signaling data and floating car data | |
| CN105608505B (en) | Resident rail transit trip mode identification method based on mobile phone signaling data | |
| CN102509470B (en) | System and method for realizing energy conservation and emission reduction of vehicle based on dynamic path planning | |
| CN103177575B (en) | System and method for dynamically optimizing online dispatching of urban taxies | |
| CN104574967B (en) | A kind of city based on Big Dipper large area road grid traffic cognitive method | |
| CN110667428B (en) | Electric vehicle charging station recommendation method based on real-time positioning data | |
| CN105844362B (en) | Urban traffic comprehensive trip decision-making device | |
| CN111653099B (en) | Bus passenger flow OD obtaining method based on mobile phone signaling data | |
| CN100589143C (en) | Method and appaatus for judging the traveling state of a floating vehicle | |
| CN111340415B (en) | Vehicle spontaneous formation mode mining method based on freight track data | |
| CN108761509A (en) | A kind of traval trace and mileage prediction technique based on historical data | |
| CN104778834A (en) | Urban road traffic jam judging method based on vehicle GPS data | |
| CN112036757B (en) | Mobile phone signaling and floating car data-based parking transfer parking lot site selection method | |
| CN103310651A (en) | Bus arrival prediction method based on real-time traffic status information | |
| CN104197945A (en) | Global voting map matching method based on low-sampling-rate floating vehicle data | |
| CN110555992B (en) | Taxi driving path information extraction method based on GPS track data | |
| CN111210612A (en) | Method for extracting bus route track based on bus GPS data and station information | |
| CN103854471B (en) | A kind of dissemination method of transport information | |
| CN103366560A (en) | Vehicle-following detection method, system and application for road traffic state | |
| CN102867406B (en) | Traffic network generation method applying vehicle detection data | |
| CN114912689B (en) | Overrun vehicle destination prediction method and system based on map grid index and XGBOOST | |
| CN101750082A (en) | Road section recognizing and matching method based on decision-making circle | |
| CN109584549A (en) | A kind of road traffic operation index detection method based on large scale floating vehicle data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20180619 |