CN113739814A - Passenger getting-off point extraction optimization method based on taxi track sequence - Google Patents

Passenger getting-off point extraction optimization method based on taxi track sequence Download PDF

Info

Publication number
CN113739814A
CN113739814A CN202110992319.7A CN202110992319A CN113739814A CN 113739814 A CN113739814 A CN 113739814A CN 202110992319 A CN202110992319 A CN 202110992319A CN 113739814 A CN113739814 A CN 113739814A
Authority
CN
China
Prior art keywords
point
sequence
track
passenger
points
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
Application number
CN202110992319.7A
Other languages
Chinese (zh)
Other versions
CN113739814B (en
Inventor
马培龙
吴义豪
王子龙
武奇
周侗
陶菲
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nantong University
Original Assignee
Nantong University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Nantong University filed Critical Nantong University
Priority to CN202110992319.7A priority Critical patent/CN113739814B/en
Publication of CN113739814A publication Critical patent/CN113739814A/en
Application granted granted Critical
Publication of CN113739814B publication Critical patent/CN113739814B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3407—Route searching; Route guidance specially adapted for specific applications
    • G01C21/343—Calculating itineraries, i.e. routes leading from a starting point to a series of categorical destinations using a global route restraint, round trips, touristic trips
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention discloses a passenger getting-off point extraction optimization method based on a taxi track sequence, which specifically comprises the following steps; s1, acquiring taxi track data and extracting departure points; s2, selecting an entrance with a more determined lower passenger point position in the city, and arranging buffer areas in front of the entrance and on adjacent roads leading to the entrance to be respectively used as areas with deviation between the accurate lower passenger point position area and the lower passenger point position area; s3, screening the passenger points in the buffer area; s4, extracting the trace points of the passenger points reserved in the S3, and respectively generating an unbiased sequence TST and a biased sequence TSW after standardization processing; s5, respectively calculating the similarity between the track sequence in the TST and the track sequence in the TSW by using a DTW sequence similarity measurement method; and S6, taking the point, closest to the running speed of the 5 th track point in the TSM, of the running speed in the TSW as the corrected passenger getting-off point. The method provides an extraction optimization measure of the taxi passenger leaving point, so that the extraction precision of the taxi passenger leaving point is improved.

Description

Passenger getting-off point extraction optimization method based on taxi track sequence
Technical Field
The invention relates to a passenger getting-off point extraction optimization method based on a taxi track sequence, and belongs to the technical field of position deviation correction methods.
Background
With the continuous development of economic society and the continuous improvement of living standard of people, people begin to tend to select taxi travel service so as to meet the requirements of convenience and comfort. Taxi is one of the main components of urban traffic in China at present, taxi track data can well reflect the travel behaviors of residents and the operation conditions of urban traffic, so that many scholars develop research by utilizing the taxi track data, such as road congestion condition research, urban functional area recognition research, population flow pattern research and urban entrance and exit discovery research based on the taxi track data. In the above researches, most taxi service points are required to extract results, and in real life, taxi drivers often habitually finish orders in advance when coming to the passenger destinations, so that on one hand, the taxi service points give good feelings to customers and help to improve the favorable rating of the taxi service points, and on the other hand, the taxi service points also help the system to arrange new orders for the taxi service points as soon as possible, thereby increasing the number of orders. The behavior has many functions for drivers, but causes inconvenience for scientific research personnel, so that the passenger boarding point extracted by the drivers by utilizing taxi track information has larger position deviation with the real passenger boarding point, and the experimental result is influenced to a great extent. Therefore, it is necessary to design an optimization method for extracting taxi-taking points, so as to improve the extraction accuracy of taxi-taking points.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a passenger boarding point extraction optimization method based on a taxi track sequence, so that the extraction precision of a taxi boarding point is improved.
In order to achieve the purpose, the invention adopts the technical scheme that: a passenger boarding point extraction optimization method based on a taxi track sequence specifically comprises the following steps;
s1, obtaining taxi track data and extracting the passenger getting-off points according to the change conditions of the passenger carrying state field and the time field;
s2, selecting an entrance with a more determined lower passenger point position in the city, and arranging buffer areas in front of the entrance and on adjacent roads leading to the entrance to be respectively used as areas with deviation between the accurate lower passenger point position area and the lower passenger point position area;
s3, screening the getting-off points in the buffer area, and cleaning the getting-off points according to the driving direction and the driving speed of the vehicle;
s4, respectively extracting 4 track points before and after the leaving point reserved in S3 to generate a track sequence with the length of 9, and respectively generating an unbiased sequence TST with accurate leaving point position and a biased sequence TSW with deviation of leaving point position after standardization processing;
s5, respectively calculating the similarity between the track sequence in the TST and the track sequence in the TSW by using a DTW sequence similarity measurement method;
and S6, selecting the track sequence TSM with the highest similarity to the TSW sequence in the TST as a matching sequence, and taking the point, with the running speed being closest to that of the 5 th track point in the TSM, in the TSW as the corrected passenger getting-off point.
Preferably, the step S1 specifically includes:
s11, pre-extracting a boarding point and a disembarking point according to the change condition of a passenger carrying state field of taxi track information, wherein the time when the 'heavy-duty' is changed into 'empty' is the disembarking point, and the time when the 'empty-duty' is changed into 'heavy-duty' is the boarding point;
and S12, cleaning the drop-off point extraction result according to the time field in the track data, and removing the drop-off points with the order execution time less than 5 minutes.
3. The passenger boarding point extraction optimization method based on taxi track sequences as claimed in claim 1, wherein the step S3 specifically comprises:
s31, judging the spatial position relation between the guest point and the buffer area established in S2 according to an injection line method, namely, sending a ray from the guest point, and judging according to the ray and the number of intersections of all edges of the polygon, wherein if the number of intersections is odd, the guest point is in the buffer area, and if the number of intersections is even, the guest point is outside the buffer area; screening the number of the intersection points which are odd, namely the number of the passenger points falling into the buffer area;
s32: and (4) cleaning the passenger points according to the driving direction of the vehicle falling in the buffer area and the driving speed of the vehicle, namely, rejecting the passenger points with the included angle of more than 45 degrees between the driving direction D1 in the adjacent road buffer area and the direction D2 of the gateway relative to the vehicle and the passenger points with the driving speed of 0 so as to ensure that the destination of the passenger points extracted in S32 is the selected gateway.
Preferably, the step S4 specifically includes:
s41, taking the passenger leaving point extracted in S3 as a track center, respectively extracting the running speeds of 4 track points before and after the point, and generating a taxi track sequence { dp) with the length of 91，dp2，dp3，dp4，dp5，dp6，dp7，dp8，dp9D, wherein dp5Pre-extracting the order of the passenger points;
s42, in order to eliminate the influence of the data variation size factor, a 0-1 standardization method is used for processing data, and therefore the unbiased sequence TST with accurate guest placement position and the biased sequence TSW with deviation of the guest placement position are generated. The normalization formula is as follows:
Figure BDA0003232814500000031
wherein x is*Is a normalized sequence value, x is a sequence value currently being processed, xminIs the minimum value of the current sequence, xmaxIs the maximum value of the current sequence.
Preferably, the step S5 specifically includes:
s51, using a matrix M of 9 x 9 to represent the distance of each point between two track sequences A and B, wherein the distance between the two points is as follows:
M(i，j)＝|A(i)-B(j)|，1≤i，j≤9
wherein M (i, j) is the distance between the ith point of the sequence A and the jth point of the sequence B, A (i) is the running speed of the ith track point of the track sequence A, and B (j) is the running speed of the jth track point of the track sequence B;
s52, initializing the shortest distance between the track sequences A and B, namely:
Lmin(1，1)＝M(1，1)
wherein L ismin(1, 1) is the shortest distance from the 1 st point in the sequence A to the 1 st point in the sequence B, and M (1, 1) is the distance from the 1 st point in the sequence A to the 1 st point in the sequence B;
s53, solving the shortest distance between the track sequence A and the track sequence B according to the following recursion rule:
Lmin(i，j)＝min{Lmin(i，j-1)，Lmin(i-l，j)，Lmin(i-1，j-1)}+M(i，j)
wherein L ismin(i, j) is the shortest distance from the ith point in the sequence A to the jth point in the sequence B, and M (i, j) is the distance from the ith point in the sequence A to the jth point in the sequence B;
the distance obtained according to the recursive algorithm is the shortest distance between the track sequences A and B, and the distance represents the similarity degree of the two track sequences.
The invention has the beneficial effects that: according to the taxi track data processing method, the taxi track data are cleaned, so that the influence of system errors and accidental errors on the passenger point extraction result is eliminated; the method comprises the steps of constructing a taxi running track sequence with a passenger point as the center, extracting an unbiased track sequence with the highest similarity to a biased track sequence by a DTW sequence similarity measurement method, extracting the corrected passenger point position by a vehicle speed matching method, and effectively improving the extraction precision of the passenger point of the taxi.
Drawings
FIG. 1 is a flowchart of an embodiment of a method for optimizing drop-off point location extraction based on taxi track sequences according to the present invention;
FIG. 2 is a buffer-based drop-off extraction diagram of the present invention;
FIG. 3 is a schematic diagram of the highest similarity sequence-based optimization of the present invention;
FIG. 4 is a diagram of the optimization results of drop-off point extraction according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood, however, that the description herein of specific embodiments is only intended to illustrate the invention and not to limit the scope of the invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs, and the terms used herein in the specification of the present invention are for the purpose of describing particular embodiments only and are not intended to limit the present invention.
Referring to fig. 1 to 4, the passenger boarding point extraction optimization method based on taxi track sequence according to the present invention includes the following steps:
s1, obtaining taxi track data and extracting a passenger getting-off point according to the change conditions of a passenger carrying state field and a time field;
s11, pre-extracting a boarding point and a disembarking point according to the change condition of the passenger carrying state field of the taxi track information, wherein the time when the 'heavy-duty' is changed into 'empty' is the disembarking point, and the time when the 'empty-duty' is changed into 'heavy-duty' is the boarding point.
S12, considering that noise points exist in the extraction result due to the fact that system errors and accidental errors exist in the track data, cleaning the passenger getting-off point extraction result according to the time field in the track data, and removing the passenger getting-on and passenger getting-off points with order execution time less than 5 minutes.
S2, selecting an entrance with a more determined passenger position in the city, and arranging buffer zones in front of the entrance and on adjacent roads leading to the entrance to be used as areas with accurate passenger position and areas with deviation from the passenger position.
S3, screening the getting-off points located in the buffer area, and cleaning the getting-off points according to the driving direction and the driving speed of the vehicle;
s31, judging the spatial position relation between the guest point and the buffer area established in the S2 according to an injection line method, namely, emitting a ray from the guest point, and judging according to the ray and the number of intersections of all sides of a polygon, wherein if the number of intersections is odd, the guest point is in the buffer area, and if the number of intersections is even, the guest point is outside the buffer area. And screening the passenger points with odd number of the intersection points, namely the passenger points falling in the buffer area.
S32: and (4) cleaning the passenger points according to the driving direction of the vehicle falling in the buffer area and the driving speed of the vehicle, namely, rejecting the passenger points with the included angle of more than 45 degrees between the driving direction D1 in the adjacent road buffer area and the direction D2 of the gateway relative to the vehicle and the passenger points with the driving speed of 0 so as to ensure that the destination of the passenger points extracted in S32 is the selected gateway.
S4, respectively extracting 4 track points before and after the leaving point reserved in S3 to generate a track sequence with the length of 9, and respectively generating an unbiased sequence TST with accurate leaving point position and a biased sequence TSW with deviation of leaving point position after standardization processing;
s41, taking the passenger leaving point extracted in S3 as a track center, respectively extracting the running speeds of 4 track points before and after the point, and generating a taxi track sequence { dp) with the length of 91，dp2，dp3，dp4，dp5，dp6，dp7，dp8，dp9D, wherein dp5Is the drop off point for the sequence.
And S42, in order to eliminate the influence of the data variation size factor, processing the data by using a 0-1 standardization method, so as to generate a track sequence TSW with the accurate guest point position and the deviation between the guest point position and the track sequence TST. The normalization formula is as follows:
Figure BDA0003232814500000061
wherein x is*Is a normalized sequence value, x is a sequence value currently being processed, xminIs the minimum value of the current sequence, xmaxIs the maximum value of the current sequence.
And S5, respectively calculating the similarity between the track sequence in the TST and the track sequence in the TSW by using a DTW sequence similarity measurement method.
S51, using a matrix M of 9 x 9 to represent the distance of each point between two track sequences A and B, wherein the distance between the two points is as follows:
M(i，j)＝|A(i)-B(j)|，1≤i，j≤9
wherein, M (i, j) is the distance between the ith point of the sequence A and the jth point of the sequence B, A (i) is the running speed of the ith track point of the track sequence A, and B (j) is the running speed of the jth track point of the track sequence B.
S52, initializing the shortest distance between the track sequences A and B, namely:
Lmin(1，1)＝M(1，1)
wherein L isminAnd (1, 1) is the shortest distance from the 1 st point in the sequence A to the 1 st point in the sequence B, and M (1, 1) is the distance from the 1 st point in the sequence A to the 1 st point in the sequence B.
S53, solving the shortest distance between the track sequence A and the track sequence B according to the following recursion rule:
Lmin(i，j)＝min{Lmin(i，j-1)，Lmin(i-1，j)，Lmin(i-1，j-1)}+M(i，j)
wherein L isminAnd (i, j) is the shortest distance from the ith point in the sequence A to the jth point in the sequence B, and M (i, j) is the distance from the ith point in the sequence A to the jth point in the sequence B.
The distance obtained according to the recursive algorithm is the shortest distance between the track sequences A and B, and the distance represents the similarity degree of the two track sequences.
And S6, selecting the track sequence TSM with the highest similarity to the TSW sequence in the TST as a matching sequence, and taking the point, with the running speed being closest to that of the 5 th track point in the TSM, in the TSW as the corrected passenger getting-off point.
According to the taxi passenger-leaving point extraction method, taxi track data are cleaned, and the influence of system errors and accidental errors on passenger-leaving point extraction results is eliminated; the method comprises the steps of constructing a taxi running track sequence with a passenger point as the center, extracting an unbiased track sequence with the highest similarity to a biased track sequence by a DTW sequence similarity measurement method, extracting the corrected passenger point position by a vehicle speed matching method, and effectively improving the extraction precision of the passenger point of the taxi.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents or improvements made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (5)

1. A passenger boarding point extraction optimization method based on a taxi track sequence is characterized by comprising the following steps;
s1, obtaining taxi track data and extracting the passenger getting-off points according to the change conditions of the passenger carrying state field and the time field;
s2, selecting an entrance with a more determined lower passenger point position in the city, and arranging buffer areas in front of the entrance and on adjacent roads leading to the entrance to be respectively used as areas with deviation between the accurate lower passenger point position area and the lower passenger point position area;
s3, screening the getting-off points in the buffer area, and cleaning the getting-off points according to the driving direction and the driving speed of the vehicle;
s4, respectively extracting 4 track points before and after the leaving point reserved in S3 to generate a track sequence with the length of 9, and respectively generating an unbiased sequence TST with accurate leaving point position and a biased sequence TSW with deviation of leaving point position after standardization processing;
s5, respectively calculating the similarity between the track sequence in the TST and the track sequence in the TSW by using a DTW sequence similarity measurement method;
and S6, selecting the track sequence TSM with the highest similarity to the TSW sequence in the TST as a matching sequence, and taking the point, with the running speed being closest to that of the 5 th track point in the TSM, in the TSW as the corrected passenger getting-off point.
2. The passenger boarding point extraction optimization method based on taxi track sequences as claimed in claim 1, wherein the step S1 specifically comprises:
s11, pre-extracting a boarding point and a disembarking point according to the change condition of a passenger carrying state field of taxi track information, wherein the time when the 'heavy-duty' is changed into 'empty' is the disembarking point, and the time when the 'empty-duty' is changed into 'heavy-duty' is the boarding point;
and S12, cleaning the drop-off point extraction result according to the time field in the track data, and removing the drop-off points with the order execution time less than 5 minutes.
3. The passenger boarding point extraction optimization method based on taxi track sequences as claimed in claim 1, wherein the step S3 specifically comprises:
s31, judging the spatial position relation between the guest point and the buffer area established in S2 according to an injection line method, namely, sending a ray from the guest point, and judging according to the ray and the number of intersections of all edges of the polygon, wherein if the number of intersections is odd, the guest point is in the buffer area, and if the number of intersections is even, the guest point is outside the buffer area; screening the number of the intersection points which are odd, namely the number of the passenger points falling into the buffer area;
s32: and (4) cleaning the passenger points according to the driving direction of the vehicle falling in the buffer area and the driving speed of the vehicle, namely, rejecting the passenger points with the included angle of more than 45 degrees between the driving direction D1 in the adjacent road buffer area and the direction D2 of the gateway relative to the vehicle and the passenger points with the driving speed of 0 so as to ensure that the destination of the passenger points extracted in S32 is the selected gateway.
4. The passenger boarding point extraction optimization method based on taxi track sequences as claimed in claim 1, wherein the step S4 specifically comprises:
s41, taking the passenger leaving point extracted in S3 as a track center, respectively extracting the running speeds of 4 track points before and after the point, and generating a taxi track sequence { dp) with the length of 91，dp2，dp3，dp4，dp5，dp6，dp7，dp8，dp9D, wherein dp5Pre-extracting the order of the passenger points;
s42, in order to eliminate the influence of the data variation size factor, a 0-1 standardization method is used for processing data, and therefore the unbiased sequence TST with accurate guest placement position and the biased sequence TSW with deviation of the guest placement position are generated. The normalization formula is as follows:
Figure FDA0003232814490000021
wherein x is*Is a normalized sequence value, x is a sequence value currently being processed, xminIs the minimum value of the current sequence, xmaxIs the maximum value of the current sequence.
5. The passenger boarding point extraction optimization method based on taxi track sequences as claimed in claim 1, wherein the step S5 specifically comprises:
s51, using a matrix M of 9 x 9 to represent the distance of each point between two track sequences A and B, wherein the distance between the two points is as follows:
M(i，j)＝|A(i)-B(j)|，1≤i，j≤9
wherein M (i, j) is the distance between the ith point of the sequence A and the jth point of the sequence B, A (i) is the running speed of the ith track point of the track sequence A, and B (j) is the running speed of the jth track point of the track sequence B;
s52, initializing the shortest distance between the track sequences A and B, namely:
Lmin(1，1)＝M(1，1)
wherein L ismin(1, 1) is the shortest distance from the 1 st point in the sequence A to the 1 st point in the sequence B, and M (1, 1) is the distance from the 1 st point in the sequence A to the 1 st point in the sequence B;
s53, solving the shortest distance between the track sequence A and the track sequence B according to the following recursion rule:
Lmin(i，j)＝min{Lmin(i，j-1)，Lmin(i-1，j)，Lmin(i-1，j-1)}+M(i，j)
wherein L ismin(i, j) is the shortest distance from the ith point in the sequence A to the jth point in the sequence B, and M (i, j) is the distance from the ith point in the sequence A to the jth point in the sequence B;
the distance obtained according to the recursive algorithm is the shortest distance between the track sequences A and B, and the distance represents the similarity degree of the two track sequences.
CN202110992319.7A 2021-08-27 2021-08-27 Passenger getting-off point extraction optimization method based on taxi track sequence Active CN113739814B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110992319.7A CN113739814B (en) 2021-08-27 2021-08-27 Passenger getting-off point extraction optimization method based on taxi track sequence

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110992319.7A CN113739814B (en) 2021-08-27 2021-08-27 Passenger getting-off point extraction optimization method based on taxi track sequence

Publications (2)

Publication Number Publication Date
CN113739814A true CN113739814A (en) 2021-12-03
CN113739814B CN113739814B (en) 2023-09-26

Family

ID=78733212

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110992319.7A Active CN113739814B (en) 2021-08-27 2021-08-27 Passenger getting-off point extraction optimization method based on taxi track sequence

Country Status (1)

Country Link
CN (1) CN113739814B (en)

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108537391A (en) * 2018-04-25 2018-09-14 哈尔滨工业大学 A kind of taxi bus stop setting optimization method based on taxi track data
CN108959466A (en) * 2018-06-20 2018-12-07 淮阴工学院 Taxi hot spot method for visualizing and system based on BCS-DBSCAN
CN109254861A (en) * 2018-09-17 2019-01-22 江苏智通交通科技有限公司 OD requirement extract and its analysis method for reliability based on track data
CN110413855A (en) * 2019-07-11 2019-11-05 南通大学 A kind of region entrance Dynamic Extraction method based on taxi drop-off point
CN110458589A (en) * 2019-02-01 2019-11-15 吉林大学 Trackside formula taxi bus stop addressing preferred method based on track big 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
CN110990661A (en) * 2019-10-23 2020-04-10 南通大学 Interest area entrance and exit extraction method based on road network constraint and density clustering
CN111814596A (en) * 2020-06-20 2020-10-23 南通大学 Automatic city function partitioning method for fusing remote sensing image and taxi track
CN113163330A (en) * 2021-03-24 2021-07-23 广州宸祺出行科技有限公司 Method and device for correcting boarding and alighting recommended points based on door closing sound

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108537391A (en) * 2018-04-25 2018-09-14 哈尔滨工业大学 A kind of taxi bus stop setting optimization method based on taxi track data
CN108959466A (en) * 2018-06-20 2018-12-07 淮阴工学院 Taxi hot spot method for visualizing and system based on BCS-DBSCAN
CN109254861A (en) * 2018-09-17 2019-01-22 江苏智通交通科技有限公司 OD requirement extract and its analysis method for reliability based on track data
CN110458589A (en) * 2019-02-01 2019-11-15 吉林大学 Trackside formula taxi bus stop addressing preferred method based on track big data
CN110413855A (en) * 2019-07-11 2019-11-05 南通大学 A kind of region entrance Dynamic Extraction method based on taxi drop-off point
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
CN110990661A (en) * 2019-10-23 2020-04-10 南通大学 Interest area entrance and exit extraction method based on road network constraint and density clustering
CN111814596A (en) * 2020-06-20 2020-10-23 南通大学 Automatic city function partitioning method for fusing remote sensing image and taxi track
CN113163330A (en) * 2021-03-24 2021-07-23 广州宸祺出行科技有限公司 Method and device for correcting boarding and alighting recommended points based on door closing sound

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
HAO-XUAN CHEN: "Applicability Evaluation of Several Spatial Clustering Methods in Spatiotemporal Data Mining of Floating Car Trajectory", 《INTERNATIONAL JOURNAL OF GEO-INFORMATION》, pages 1 - 15 *

Also Published As

Publication number Publication date
CN113739814B (en) 2023-09-26

Similar Documents

Publication Publication Date Title
CN111653096B (en) Urban trip mode identification method based on mobile phone signaling data
JP6247754B2 (en) How to process vehicle measurement data to identify the start of parking hunting
US8554473B2 (en) Energy efficient routing using an impedance factor
CN111611955B (en) Method, device, equipment and storage medium for identifying passable construction road
CN109544914B (en) Shared bicycle reverse behavior identification method based on historical GPS track
CN109903553B (en) Multi-source data mining bus station identification and inspection method
CN101964941A (en) Intelligent navigation and position service system and method based on dynamic information
CN110909106B (en) Trajectory prediction method and system
CN110634299B (en) Urban traffic state fine division and identification method based on multi-source track data
US11798408B2 (en) Green wave speed determination method, electronic device and storage medium
AU2017261601B2 (en) Intelligent automatic license plate recognition for electronic tolling environments
CN110472999B (en) Passenger flow mode analysis method and device based on subway and shared bicycle data
WO2023040539A1 (en) Vehicle stream relocating condition display method and apparatus, device, medium, and product
CN106227859A (en) The method identifying the vehicles from gps data
CN114170797B (en) Method, device, equipment, medium and product for identifying traffic restriction intersection
CN110766940A (en) Method for evaluating running condition of road signalized intersection
CN112507624A (en) Intercity highway trip mode identification model construction and identification method and device
CN109489679A (en) A kind of arrival time calculation method in guidance path
CN113739814A (en) Passenger getting-off point extraction optimization method based on taxi track sequence
CN112699773A (en) Traffic light identification method and device and electronic equipment
CN111613052B (en) Traffic condition determining method and device, electronic equipment and storage medium
Oliveira-Neto et al. Tracking large trucks in real time with license plate recognition and text-mining techniques
CN114582125B (en) Method, device, equipment and storage medium for identifying road traffic direction
CN113297878A (en) Road intersection identification method and device, computer equipment and storage medium
CN115995151A (en) Network vehicle-closing abnormal behavior detection method applied to city management

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant