CN109712401A - A kind of compound road network bottleneck point recognition methods based on Floating Car track data - Google Patents
A kind of compound road network bottleneck point recognition methods based on Floating Car track data Download PDFInfo
- Publication number
- CN109712401A CN109712401A CN201910074831.6A CN201910074831A CN109712401A CN 109712401 A CN109712401 A CN 109712401A CN 201910074831 A CN201910074831 A CN 201910074831A CN 109712401 A CN109712401 A CN 109712401A
- Authority
- CN
- China
- Prior art keywords
- bottleneck
- point
- track data
- floating car
- intersection
- 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
Landscapes
- Traffic Control Systems (AREA)
Abstract
The compound road network bottleneck point recognition methods based on Floating Car track data that the present invention relates to a kind of, comprising: step S1: data cleansing is carried out to Floating Car track data；Step S2: merging for track data and geographic information data is carried out to the Floating Car track data after over cleaning；Step S3: it carries out information excavating and obtains bottleneck point；Step S4: bottleneck point is divided by moving bottleneck or fixed bottleneck according to the change in location of bottleneck point, and bottleneck point is divided by often hair property bottleneck or sporadic bottleneck according to the time change of bottleneck point；Step S5: the output bottleneck point origin cause of formation.Compared with prior art, track data of the present invention need not additionally install Vehicle Detection sensor, and procurement cost is cheap.
Description
Technical field
The present invention relates to traffic state analysis fields, more particularly, to a kind of compound road network based on Floating Car track data
Bottleneck point recognition methods.
Background technique
Bottleneck point in transportation network refers to the certain point throughput in section, and there are a significant declines.Bottleneck
Formation will result directly in the formation of traffic congestion and the sprawling of queuing vehicle.Timely detection and the dissipation of bottleneck point are that road is handed over
One of the top priority of logical manager.For bottleneck, the position occurred, the queue length of formation and bottleneck it is lasting when
Between, occurrence frequency, rule are the most concerned contents of traffic administration person.
Bottleneck is usually made of two parts parameter, first is that bottleneck starting point, refering in particular to downstream, section traffic throughput occur aobvious
The case where work increases and traffic shows free flow feature.Second is that being lined up caused by bottleneck, it refers to bottleneck starting point up
The slow vehicle of the travel speed of roam all around the would to appearance is formed by queue.
Whether moved in its duration according to the starting point of bottleneck, fixed bottleneck and moving bottleneck can be divided into.It presses
According to the time and space idea that bottleneck occurs, often hair property bottleneck and sporadic bottleneck can be divided into.Often hair property bottleneck point refers to that one kind often exists
Same or like place, in the bottleneck that the different time period (such as not interior on the same day) repeats.Sporadic bottleneck then shows
Weaker regularity.
The largely method and research about bottleneck detection existing at present, wherein most common way is under detection speed is significant
The section of drop.But based on detection data source be essentially section fixed detector data, such as loop coil.Its common practice
It is to lay loop coil at certain intervals on a plurality of adjacent section and detect velocity information.But this method can not be extended to
Road network level is analyzed, and this method precision is limited by detector density.With the appearance of track data, also occur
The more method for carrying out bottleneck judgement based on such data.The advantages of this method is that procurement cost is low, without installing on road
Entity detection device, and wide coverage.But since floating wheel paths permeability is low, the sampling interval is big and inconsistent, GPS positioning
The problems such as precision is different brings certain difficulty to the direct application of data.And the existing bottleneck point spy based on track data
Sign calculates identification and description method is abundant not enough.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind to be based on floating track
The compound road network bottleneck point recognition methods of mark data.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of compound road network bottleneck point recognition methods based on Floating Car track data, comprising:
Step S1: data cleansing is carried out to Floating Car track data；
Step S2: merging for track data and geographic information data is carried out to the Floating Car track data after over cleaning；
Step S3: it carries out information excavating and obtains bottleneck point；
Step S4: bottleneck point is divided by moving bottleneck or fixed bottleneck according to the change in location of bottleneck point, and according to bottle
Bottleneck point is divided into often hair property bottleneck or sporadic bottleneck by the time change of neck point；
Step S5: the output bottleneck point origin cause of formation.
The step S1 is specifically included:
Step S11: judging whether the vehicle ID of current trace points in Floating Car track data, timestamp lack or abnormal,
If it is, S12 is thened follow the steps, conversely, thening follow the steps S13；
Step S12: current trace points are abandoned, next tracing point is read and judges whether track data terminates, if it has not, then
Execute step S11；
Step S13: judging whether the instantaneous velocity of current trace points lacks or abnormal, if it is, S14 is thened follow the steps,
Conversely, thening follow the steps S15；
Step S14: being attempted smoothly to be repaired using the instantaneous velocity of adjacent track point, and judge whether success, if it has, then
Step S15 is executed, conversely, thening follow the steps S12；
Step S15: judge whether the longitude and latitude of current trace points lacks or abnormal, if it is, thening follow the steps S16, instead
It, thens follow the steps S17；
Step S16: it attempts smoothly to repair using the longitude and latitude of adjacent track point, and judges whether success, if it has, then holding
Row step S17, conversely, thening follow the steps S12；
Step S17: reading next tracing point and judges whether track data terminates, if it has not, thening follow the steps S11.
Judge whether successful process in the step S14 specifically: judging the difference of the instantaneous velocity of adjacent track point is
It is no to be less than threshold velocity.
Judge whether successful process in the step S16 specifically:
The distance between two tracing points of calculation of longitude & latitude based on adjacent track point；
First Speed is calculated according to the distance between two tracing points and time difference, and judges the First Speed and wink
Whether the difference of Shi Sudu is less than setting ratio, if, then it is assumed that success, it is on the contrary then think failure.
The step S2 is specifically included:
Step S21: by delimiting a rectangular area, tracing point all in target intersection group is obtained；
Step S22: it assigns tracing point intersection number interid, road number roadid, go out row number tripid；
Step S23: direction of travel when whether passing through intersection according to track and passing through intersection assigns tracing point and turns
To number moveid；
Step S24: for the section between two intersections, tracing point is being repaired just according to the intersection that vehicle is driven towards
Intersection number interid；
Step S25: the distance of vehicle distances stop line is obtained.
The step S22 is specifically included:
Step S221: the second is converted by road time format；
Step S222: for the road between intersection, centre adheres to two intersections separately to half-open, assigns and handing over for each tracing point
Prong number interid；
Step S223: delimiting the region of longitude and latitude for different sections of highway, and correspondence invests road number roadid value；
Step S224: row number tripid is assigned out according to the corresponding row order out of each tracing point.
It is specific to wrap for the tracing point that each tripid of each each car of each car is covered in the step S25
It includes:
If only including 1 roadid value, the distance for arriving intersection is directly calculated；
If containing 3 roadid values, an intersection only have passed through, equally directly calculate distance；
If characterization have passed through multiple intersections comprising being greater than 3 roadid values, a pair of cross mouth number is successively screened
The corresponding tracing point of interid, calculates separately the distance apart from stop line.
The step S5 includes:
If moving bottleneck, there is the vehicle (may be truck or car of going slowly) of retardance traffic in output；
If fixed bottleneck, the traffic capacity for exporting the existing means of transportation of the bottleneck point of the bottleneck point be can no longer meet
Status transport need.
Compared with prior art, the invention has the following advantages:
1) acquisition of track data need not additionally install Vehicle Detection sensor, and procurement cost is cheap.
2) the various topological structure and traffic flow condition for combining compound road network, classify to bottleneck point.The bottleneck of foundation
Feature judgement and the opposite more easily execution of classifying method.
Detailed description of the invention
Fig. 1 is the key step flow diagram of the method for the present invention；
Fig. 2 is the space-time trajectory schematic diagram of track data；
Fig. 3 is track data fusion process schematic diagram.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.The present embodiment is with technical solution of the present invention
Premised on implemented, the detailed implementation method and specific operation process are given, but protection scope of the present invention is not limited to
Following embodiments.
The purpose of the application is in order to overcome the shortcomings of existing bottleneck point identifying and analyzing method, based on the traffic to become increasingly abundant
Track big data constructs compound road network bottleneck point recognizer, to promote traffic condition detection accuracy, expand bottleneck point knowledge
Other application range.The it is proposed of this method peomotes urban traffic control system construction and upgrading, accurately identifies and deposits city
Traffic congestion critical issue, to reach the purpose for more targetedly alleviating urban traffic blocking.
It proposes compound road network bottleneck point recognition methods, is based on Floating Car track data, commented from the quantization of time-domain space angle
Estimate the parameter attribute and rule of bottleneck point, thus for network traffic supersaturation control evaluation provide with optimization it is objective, effective
Foundation.
A kind of compound road network bottleneck point recognition methods based on Floating Car track data, as shown in Figure 1, comprising:
Step S1: carrying out data cleansing to Floating Car track data, as shown in Fig. 2, specifically including:
Step S11: judging whether the vehicle ID of current trace points in Floating Car track data, timestamp lack or abnormal,
If it is, S12 is thened follow the steps, conversely, thening follow the steps S13；
Step S12: current trace points are abandoned, next tracing point is read and judges whether track data terminates, if it has not, then
Execute step S11；
Step S13: judging whether the instantaneous velocity of current trace points lacks or abnormal, if it is, S14 is thened follow the steps,
Conversely, thening follow the steps S15；
Step S14: being attempted smoothly to be repaired using the instantaneous velocity of adjacent track point, and judge whether success, if it has, then
Step S15 is executed, conversely, thening follow the steps S12；
Specifically, should judge whether that successful process was specifically as follows in the process: judging the instantaneous velocity of adjacent track point
Difference whether be less than threshold velocity.
Step S15: judge whether the longitude and latitude of current trace points lacks or abnormal, if it is, thening follow the steps S16, instead
It, thens follow the steps S17；
Step S16: it attempts smoothly to repair using the longitude and latitude of adjacent track point, and judges whether success, if it has, then holding
Row step S17, conversely, thening follow the steps S12；
Specifically, wherein judging whether successful process can include:
The distance between two tracing points of calculation of longitude & latitude based on adjacent track point；Then according between two tracing points
Distance and the time difference First Speed is calculated, and judge whether the difference of the First Speed and instantaneous velocity is less than setting ratio
Example, if, then it is assumed that success, it is on the contrary then think failure.
Step S17: reading next tracing point and judges whether track data terminates, if it has not, thening follow the steps S11.
Step S2: carrying out merging for track data and geographic information data to the Floating Car track data after over cleaning,
It specifically includes:
Step S21:: there is a large amount of unrelated tracing point in screening target area in file, by delimiting a rectangular area,
Obtain tracing point all in target intersection group；
Step S22: tracing point intersection number interid, road number roadid are assigned, goes out row number tripid, tool
Body includes:
Step S221: the second is converted by road time format, such as be converted into number of seconds in 35 seconds 35 minutes at 17 points；
Step S222: for the road between intersection, centre adheres to two intersections separately to half-open, assigns and handing over for each tracing point
Prong number interid；
Step S223: delimiting the region of longitude and latitude for different sections of highway, and correspondence invests road number roadid value, specifically,
The region of longitude and latitude delimited to different sections of highway, is corresponded and is assigned roadid value (intersection region can random assignment), then
Roadid is tracing point of the empty data line i.e. not on section, is deleted；
Step S224: row number tripid is assigned out according to the corresponding row order out of each tracing point, specifically, for one
Vehicle, it may have multiple trip in one day, and not occur continuously in time, and therefore, different trips is drawn
It separates.If time interval is greater than 15 seconds (official's explanation, the tracing point sample frequencys of type=11 between upper and lower two tracing points
It is 15 seconds/time, remaining is 1 second/time), then judge that the vehicle has started another new trip, tripid is numbered from 1, this is
Place mat has been made in the setting of subsequent turn around parameters.
Step S23: direction of travel when whether passing through intersection according to track and passing through intersection assigns tracing point and turns
To number moveid, specifically, as follows:
A) for having passed through the track of vehicle of intersection, setting: turning left is 1, and keeping straight on is 2, and turning right is 3；
B) for not passed through the track of vehicle of intersection, 4 are set as.
Step S24: for the section between two intersections, tracing point is being repaired just according to the intersection that vehicle is driven towards
Intersection number interid just needs to correct for the section between two intersections: which the intersection that vehicle drives towards is
As soon as, its interid is assigned a value of to the number of which intersection；
Step S25: the distance of vehicle distances stop line is obtained, wherein for each of each each car of each car
The tracing point that tripid is covered, specifically includes:
If only including 1 roadid value, the distance for arriving intersection is directly calculated；
If containing 3 roadid values, an intersection only have passed through, equally directly calculate distance；
If characterization have passed through multiple intersections comprising being greater than 3 roadid values, a pair of cross mouth number is successively screened
The corresponding tracing point of interid, calculates separately the distance apart from stop line.
Next, draw space-time diagram as shown in figure 3, check virtual stop line at a distance from true stop line, to it is existing away from
It is modified from data.
Step S3: it carries out information excavating and obtains bottleneck point；
Step S4: bottleneck point is divided by moving bottleneck or fixed bottleneck according to the change in location of bottleneck point, and according to bottle
Bottleneck point is divided into often hair property bottleneck or sporadic bottleneck by the time change of neck point；
Bottleneck point analysis specifically includes: bottleneck point Evolution, the analysis of bottleneck range of point influence.
(1) bottleneck point Evolution
Section is drawn and is divided into several sections of separate spaces at certain intervals, it is assumed that each unit road section traffic volume situation is identical, is based on
It corrects track data and carries out state matching.It detects that upstream speed is significantly lower than the section in downstream, is then bottleneck point.Record is different
When the changing rule of bottleneck point inscribed, can use machine learning algorithm and carry out feature identification and predicted with evolvement trend.
(2) bottleneck range of point influence
For the bottleneck point identified, its upstream traffic behavior is observed based on track data.Generally according to 1km, 5km range
The segmentation of carry out state (finds the initial point position of bottleneck upstream congestion), and state segmentation range is flexibly adjusted according to the range in city
It is whole.Since track positioning accuracy is not up to lane grade, need to judge by the deflection attribute of track herein.
Bottleneck type includes:
1) normal hair property bottleneck: more day datas repeat to recognize certain section, and at the close moment, there are bottleneck or bottleneck points
The coverage of mobile trend and bottleneck point is consistent, then can all be classified as the often property sent out bottleneck；
2) sporadic bottleneck: the bottleneck point and its coverage detected, spatial homing phase of getting along well in the historical data
Matching, temporal regularity of also getting along well match；
3) moving bottleneck and fixed bottleneck: being directed to certain bottleneck, if bottleneck point is not moved during being formed with dissipation
Dynamic, then otherwise it is moving bottleneck that explanation, which is fixed bottleneck,.
Step S5: the output bottleneck point origin cause of formation, comprising:
If moving bottleneck, there is the vehicle (may be truck or car of going slowly) of retardance traffic in output；
If fixed bottleneck, the traffic capacity for exporting the existing means of transportation of the bottleneck point of the bottleneck point be can no longer meet
Status transport need.
Claims (8)
1. a kind of compound road network bottleneck point recognition methods based on Floating Car track data characterized by comprising
Step S1: data cleansing is carried out to Floating Car track data；
Step S2: merging for track data and geographic information data is carried out to the Floating Car track data after over cleaning；
Step S3: it carries out information excavating and obtains bottleneck point；
Step S4: bottleneck point is divided by moving bottleneck or fixed bottleneck according to the change in location of bottleneck point, and according to bottleneck point
Time change bottleneck point is divided into often hair property bottleneck or sporadic bottleneck；
Step S5: the output bottleneck point origin cause of formation.
2. a kind of compound road network bottleneck point recognition methods based on Floating Car track data according to claim 1, special
Sign is that the step S1 is specifically included:
Step S11: judging whether the vehicle ID of current trace points in Floating Car track data, timestamp lack or abnormal, if
It is to then follow the steps S12, conversely, thening follow the steps S13；
Step S12: current trace points are abandoned, next tracing point is read and judges whether track data terminates, if it has not, then executing
Step S11；
Step S13: judging whether the instantaneous velocity of current trace points lacks or abnormal, if it is, S14 is thened follow the steps, conversely,
Then follow the steps S15；
Step S14: being attempted smoothly to be repaired using the instantaneous velocity of adjacent track point, and judge whether success, if it has, then executing
Step S15, conversely, thening follow the steps S12；
Step S15: judge whether the longitude and latitude of current trace points lacks or abnormal, if it is, S16 is thened follow the steps, conversely, then
Execute step S17；
Step S16: it attempts smoothly to repair using the longitude and latitude of adjacent track point, and judges whether success, if it has, then executing step
Rapid S17, conversely, thening follow the steps S12；
Step S17: reading next tracing point and judges whether track data terminates, if it has not, thening follow the steps S11.
3. a kind of compound road network bottleneck point recognition methods based on Floating Car track data according to claim 2, special
Sign is, judges whether successful process in the step S14 specifically: judge the instantaneous velocity of adjacent track point difference whether
Less than threshold velocity.
4. a kind of compound road network bottleneck point recognition methods based on Floating Car track data according to claim 2, special
Sign is, judges whether successful process in the step S16 specifically:
The distance between two tracing points of calculation of longitude & latitude based on adjacent track point；
First Speed is calculated according to the distance between two tracing points and time difference, and judges the First Speed and instantaneous speed
Whether the difference of degree is less than setting ratio, if, then it is assumed that success, it is on the contrary then think failure.
5. a kind of compound road network bottleneck point recognition methods based on Floating Car track data according to claim 1, special
Sign is that the step S2 is specifically included:
Step S21: by delimiting a rectangular area, tracing point all in target intersection group is obtained；
Step S22: it assigns tracing point intersection number interid, road number roadid, go out row number tripid；
Step S23: direction of travel when whether passing through intersection according to track and passing through intersection assigns tracing point and turns to volume
Number moveid；
Step S24: for the section between two intersections, the orthogonal of tracing point is repaired according to the intersection that vehicle is driven towards
Mouth number interid；
Step S25: the distance of vehicle distances stop line is obtained.
6. a kind of compound road network bottleneck point recognition methods based on Floating Car track data according to claim 5, special
Sign is that the step S22 is specifically included:
Step S221: the second is converted by road time format；
Step S222: for the road between intersection, centre adheres to two intersections separately to half-open, assigns intersection for each tracing point
Number interid；
Step S223: delimiting the region of longitude and latitude for different sections of highway, and correspondence invests road number roadid value；
Step S224: it according to interval of timestamps between each continuous path point, assigns the corresponding row order out of each tracing point and compiles
Number tripid.
7. a kind of compound road network bottleneck point recognition methods based on Floating Car track data according to claim 5, special
Sign is, specific to wrap for the tracing point that each tripid of each each car of each car is covered in the step S25
It includes:
If only including 1 roadid value, the distance for arriving intersection is directly calculated；
If containing 3 roadid values, an intersection only have passed through, equally directly calculate distance；
If characterization have passed through multiple intersections comprising being greater than 3 roadid values, a pair of cross mouth number is successively screened
The corresponding tracing point of interid, calculates separately the distance apart from stop line.
8. a kind of compound road network bottleneck point recognition methods based on Floating Car track data according to claim 1, special
Sign is that the step S5 includes:
If moving bottleneck, there is the vehicle of retardance traffic in output；
If fixed bottleneck, the traffic capacity for exporting the existing means of transportation of the bottleneck point, which can no longer meet status traffic, to be needed
It asks.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910074831.6A CN109712401B (en) | 2019-01-25 | 2019-01-25 | Composite road network bottleneck point identification method based on floating car track data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910074831.6A CN109712401B (en) | 2019-01-25 | 2019-01-25 | Composite road network bottleneck point identification method based on floating car track data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109712401A true CN109712401A (en) | 2019-05-03 |
| CN109712401B CN109712401B (en) | 2021-05-11 |
Family
ID=66262967
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910074831.6A Active CN109712401B (en) | 2019-01-25 | 2019-01-25 | Composite road network bottleneck point identification method based on floating car track data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109712401B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110781266A (en) * | 2019-09-16 | 2020-02-11 | 北京航空航天大学 | Urban perception data processing method based on time-space causal relationship |
| CN111190891A (en) * | 2019-12-27 | 2020-05-22 | 武汉长江通信产业集团股份有限公司 | Multi-semantic track data segmented storage method |
| CN111739319A (en) * | 2019-10-18 | 2020-10-02 | 腾讯科技（深圳）有限公司 | Information processing method and device |
| CN112492889A (en) * | 2019-11-21 | 2021-03-12 | 北京嘀嘀无限科技发展有限公司 | Traffic control system and method based on vehicle track data |
| CN113127466A (en) * | 2021-04-20 | 2021-07-16 | 同济大学 | Vehicle track data preprocessing method and computer storage medium |
| CN114153204A (en) * | 2021-11-09 | 2022-03-08 | 北京三快在线科技有限公司 | Method and device for repairing driving track |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104112361A (en) * | 2014-04-25 | 2014-10-22 | 浙江大学 | Identification method of common bottleneck and accidental bottleneck in road network |
| US9424748B2 (en) * | 2014-09-09 | 2016-08-23 | Here Global B.V. | Reporting traffic conditions on road segments containing a bottleneck |
| CN106960571A (en) * | 2017-03-30 | 2017-07-18 | 百度在线网络技术（北京）有限公司 | Congestion in road bottleneck point determines method, device, server and storage medium |
| US20170222908A1 (en) * | 2016-01-29 | 2017-08-03 | Hewlett Packard Enterprise Development Lp | Determining candidates for root-cause of bottlenecks in a storage network |
| CN107798871A (en) * | 2017-10-27 | 2018-03-13 | 云南大学 | A kind of freeway toll station traffic flow forecasting method and system |
| CN108364127A (en) * | 2018-02-01 | 2018-08-03 | 北京市地铁运营有限公司 | A kind of road network passenger flow Collaborative Control optimization system |
| CN108877244A (en) * | 2018-06-21 | 2018-11-23 | 东南大学 | A kind of public transit vehicle intersection operation bottleneck method of discrimination based on dynamic data |
| CN109147330A (en) * | 2018-09-19 | 2019-01-04 | 青岛海信网络科技股份有限公司 | A kind of congestion recognition methods and device |
-
2019
- 2019-01-25 CN CN201910074831.6A patent/CN109712401B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104112361A (en) * | 2014-04-25 | 2014-10-22 | 浙江大学 | Identification method of common bottleneck and accidental bottleneck in road network |
| US9424748B2 (en) * | 2014-09-09 | 2016-08-23 | Here Global B.V. | Reporting traffic conditions on road segments containing a bottleneck |
| US20170222908A1 (en) * | 2016-01-29 | 2017-08-03 | Hewlett Packard Enterprise Development Lp | Determining candidates for root-cause of bottlenecks in a storage network |
| CN106960571A (en) * | 2017-03-30 | 2017-07-18 | 百度在线网络技术（北京）有限公司 | Congestion in road bottleneck point determines method, device, server and storage medium |
| CN107798871A (en) * | 2017-10-27 | 2018-03-13 | 云南大学 | A kind of freeway toll station traffic flow forecasting method and system |
| CN108364127A (en) * | 2018-02-01 | 2018-08-03 | 北京市地铁运营有限公司 | A kind of road network passenger flow Collaborative Control optimization system |
| CN108877244A (en) * | 2018-06-21 | 2018-11-23 | 东南大学 | A kind of public transit vehicle intersection operation bottleneck method of discrimination based on dynamic data |
| CN109147330A (en) * | 2018-09-19 | 2019-01-04 | 青岛海信网络科技股份有限公司 | A kind of congestion recognition methods and device |
Non-Patent Citations (3)
| Title |
|---|
| 何巍楠: "基于浮动车数据的城市常发性交通拥堵时空分布特征研究", 《中国优秀硕士学位论文全文数据库（工程科技Ⅱ辑）》 * |
| 张晓燕: "基于FCD的城市路网常发性拥堵路段识别方法研究", 《交通信息与安全》 * |
| 邓瑞: "城市区域路网交通瓶颈识别与预测", 《中国优秀硕士学位论文全文数据库（工程科技Ⅱ辑）》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110781266A (en) * | 2019-09-16 | 2020-02-11 | 北京航空航天大学 | Urban perception data processing method based on time-space causal relationship |
| CN110781266B (en) * | 2019-09-16 | 2020-06-09 | 北京航空航天大学 | Urban perception data processing method based on time-space causal relationship |
| CN111739319A (en) * | 2019-10-18 | 2020-10-02 | 腾讯科技（深圳）有限公司 | Information processing method and device |
| CN112492889A (en) * | 2019-11-21 | 2021-03-12 | 北京嘀嘀无限科技发展有限公司 | Traffic control system and method based on vehicle track data |
| WO2021097759A1 (en) * | 2019-11-21 | 2021-05-27 | Beijing Didi Infinity Technology And Development Co., Ltd. | Systems and methods for traffic control based on vehicle trajectory data |
| CN112492889B (en) * | 2019-11-21 | 2023-02-17 | 北京嘀嘀无限科技发展有限公司 | Traffic control system and method based on vehicle track data |
| CN111190891A (en) * | 2019-12-27 | 2020-05-22 | 武汉长江通信产业集团股份有限公司 | Multi-semantic track data segmented storage method |
| CN113127466A (en) * | 2021-04-20 | 2021-07-16 | 同济大学 | Vehicle track data preprocessing method and computer storage medium |
| CN113127466B (en) * | 2021-04-20 | 2023-04-11 | 同济大学 | Vehicle track data preprocessing method and computer storage medium |
| CN114153204A (en) * | 2021-11-09 | 2022-03-08 | 北京三快在线科技有限公司 | Method and device for repairing driving track |
| CN114153204B (en) * | 2021-11-09 | 2024-03-08 | 北京三快在线科技有限公司 | Repairing method and repairing device for driving track |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109712401B (en) | 2021-05-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109712401A (en) | A kind of compound road network bottleneck point recognition methods based on Floating Car track data | |
| Geroliminis et al. | Identification and analysis of queue spillovers in city street networks | |
| Asakura et al. | Incident detection methods using probe vehicles with on-board GPS equipment | |
| Yang et al. | A microscopic traffic simulator for evaluation of dynamic traffic management systems | |
| Skabardonis et al. | Real-time estimation of travel times on signalized arterials | |
| CN101965601B (en) | Driving support device and driving support method | |
| CN110176153A (en) | A kind of blind area vehicle collision prewarning method based on edge calculations | |
| CN110176139A (en) | A kind of congestion in road identification method for visualizing based on DBSCAN+ | |
| JP3485774B2 (en) | Traffic flow simulation system | |
| KR101943198B1 (en) | Method for estimation of link travel time and signal delay | |
| Nguyen et al. | An automated detection framework for multiple highway bottleneck activations | |
| Vyas et al. | Impact of Construction Work Zone on Urban Traffic Environment | |
| Sharma et al. | Improving safety and mobility at high-speed intersections with innovations in sensor technology | |
| JP4572944B2 (en) | Driving support device, driving support method, and driving support program | |
| CN110827561B (en) | Road condition information forecasting system and method based on vehicles | |
| Li et al. | A feature-based approach to large-scale freeway congestion detection using full cellular activity data | |
| JP4957612B2 (en) | Travel pattern information acquisition device, travel pattern information acquisition method, and travel pattern information acquisition program | |
| Zhu et al. | Outlier mining based automatic incident detection on urban arterial road | |
| Kessler et al. | Detection rate of congestion patterns comparing multiple traffic sensor technologies | |
| JP4126700B2 (en) | Traveling time prediction device and traveling time prediction method | |
| Wang et al. | Red light running prediction for dynamic all-red extension at signalized intersection | |
| Cetin et al. | Using probe vehicle trajectories in stop-and-go waves for inferring unobserved vehicles | |
| Bezuidenhout et al. | Estimating queue length at signalized intersections from single loop detector data | |
| Zhu et al. | An evaluation of travel time on urban road network | |
| Kim et al. | A bus priority signal strategy for regulating headways of buses |
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 |