CN109741603A - A method of based on congestion spreading rate between queue length calculating Adjacent Intersections - Google Patents
A method of based on congestion spreading rate between queue length calculating Adjacent Intersections Download PDFInfo
- Publication number
- CN109741603A CN109741603A CN201910070312.2A CN201910070312A CN109741603A CN 109741603 A CN109741603 A CN 109741603A CN 201910070312 A CN201910070312 A CN 201910070312A CN 109741603 A CN109741603 A CN 109741603A
- Authority
- CN
- China
- Prior art keywords
- congestion
- queue length
- lane
- lane group
- group
- 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.)
- Pending
Links
Landscapes
- Traffic Control Systems (AREA)
Abstract
The invention discloses a kind of methods for calculating congestion spreading rate between Adjacent Intersections based on queue length, and in particular to field of road traffic, this method comprises the following steps: being based on crossing detection device, acquires the queue length data of adjacent intersection correlation lane group.When calculating the lane group queue length >=α × benchmark queue length obtained, determine that the lane group is in congestion status.Successively identify the congestion status of all associated lane groups.Lane group, which is divided into, flows into lane group and connection lane group, differs in T when the moment occurs for the congestion for connecting lane group with the congestion generation moment for flowing into lane group, determines that the two is related congestion, T generally takes 0-10min.Otherwise, it is determined that being unrelated congestion.Congestion spreading rate of the ratio of related congestion and all statistics numbers between Adjacent Intersections.
Description
Technical field
The present invention relates to field of road traffic, congestion is propagated between specially a kind of calculating Adjacent Intersections based on queue length
The method of rate.
Background technique
Traffic congestion is the common phenomenon of urban transportation, and has propagation characteristic.According to traffic congestion in road network
In distribution, a congestion, line congestion and face congestion three types can be generally can be divided into.Congestion initially occurs in road network
Key position (intersection), then along road (line) spread in china on road network, in the case of not obtaining effective discongest
Then finally develop into regional congestion.
In order to dredge road, it is avoided as much as urban road and the phenomenon that traffic congestion occurs.More and more technology people
Member puts into the research of urban traffic blocking.However, at present for the research of traffic congestion state be related to mostly it is middle sight and
Macroscopic aspect.Such as research object are as follows: congestion status identification, congestion propagation model or traffic congestion differentiation etc..However, by above-mentioned
Research is merely capable of reflecting on a macro scale the general trend of the congestion status between Adjacent Intersections, cannot obtain traffic congestion be as
Where propagate between Adjacent Intersections.Therefore, in order to obtain the congestion correlation between Adjacent Intersections more accurately, it is necessary to
On a microscopic level urban traffic blocking is continued to study.
Summary of the invention
The present invention provides a kind of method for calculating congestion spreading rate between Adjacent Intersections based on queue length, this method gram
Disadvantages mentioned above is taken.
To achieve the above object, the invention provides the following technical scheme:
A method of based on congestion spreading rate between queue length calculating Adjacent Intersections, this method comprises the following steps:
S01, it is based on crossing detection device, acquires the data of adjacent intersection correlation lane group.
S02, the benchmark queue length and lane group queue length that same lane group is calculated according to the data of above-mentioned acquisition,
When lane group queue length >=α × (benchmark queue length), determine that the lane group is in congestion status, α > 1.
S03, the congestion status for successively identifying all associated lane groups.
S04, lane group, which are divided into, flows into lane group and connection lane group, when the moment occurs for the congestion of connection lane group and flows into
Moment difference occurs for the congestion of lane group in T, is determined as related congestion, and T value is 0-10min.
Otherwise, it is determined that being unrelated congestion；
S05, related congestion frequency to account for the ratio of unrelated congestion and related congestion total degree be between Adjacent Intersections
Congestion spreading rate, be denoted as CRA-B, then:
Wherein, pkThe congestion correlation of Adjacent Intersections when being calculated for kth time.
When related congestion occurs: pk=1, when unrelated congestion occurs: pk=0.
CRA-BFor the congestion spreading rate of intersection A to intersection B.
Preferably, data include device numbering, vehicle in all related lane groups of Adjacent Intersections in step S01
Road number, detection time and queue length.
Preferably, further including step S01.1, the step S01.1 are as follows: obtaining between step S01 and step S02
In the data obtained, the record of unidentified lane number, detection time exception and Data duplication is deleted, obtains the number that cleaning is completed
According to for step S02.
Preferably, the calculation method of the benchmark queue length of lane group is as follows in step S02:
The average value of benchmark queue length=history flat peak period all lane queue lengths.
Preferably, the calculation method of lane group queue length is as follows: being calculated in real time with unit period in step S02
Each added turning lane group queue length, when one of added turning lane group only includes a lane: the group queue length of lane
Value takes the value of the queue length got with crossing detection device.When one of added turning lane group includes two and two or more
Lane when: in unit period, lane group queue length of the value of maximum queue length as the added turning lane group.
Preferably, in step S03: the same period, in all inflow lane groups, at least one flows into lane
When group gets congestion, determining that the period flows into lane group is congestion status.The same period, in all connection lane groups, until
When rare connection lane group gets congestion, determine that period connection lane group is congestion status.
The invention has the benefit that
The lane queue length that this method is obtained in real time based on the crossings such as radar, video detector detection device, passes through meter
It calculates identification and obtains whether related lane group is in congestion status, and differentiate the congestion of all associated lane groups between Adjacent Intersections
The congestion spreading rate of Adjacent Intersections is calculated in correlation.Then technical staff can be pre- according to the congestion spreading rate being calculated
Survey the congestion correlation between Adjacent Intersections.Road traffic congestion regulation can be provided for relevant departments the most reliable, accurate
Foundation.
Detailed description of the invention
Fig. 1 is that flow imports situation map between intersection in the present embodiment；
Fig. 2 is the queue length statistical form in lane；
Fig. 3 is lane group congestion statistic table；
Fig. 4 congestion correlation prediction result statistical form between Adjacent Intersections.
Specific embodiment
Present embodiments provide it is a kind of based on queue length calculate Adjacent Intersections between congestion spreading rate method, and according to
This method calculates the congestion spreading rate of certain Adjacent Intersections, specific as follows:
S01, certain two Adjacent Intersections is chosen as the experimental subjects for calculating congestion spreading rate, data source is radar, view
The queue length data for the intersection correlation lane group that the crossings such as frequency detector detection device is got.As shown in Figure 1, intersection
1,2, the 3 lane group vehicle flowrates of A can flow into 4,5, the 6 lane groups of intersection B (driving direction of arrow expression vehicle).Then vehicle
Road group 1,2,3 is defined as " flow into lane group ", and lane group 4,5,6 is defined as " connection lane group ", and the congestion of the two is consistent degree
For the congestion spreading rate CR of intersection A to intersection BA-B。
S02, choose 2018-9-3 to 2018-9-9 data calculated, data include device numbering (deviceno),
Detection time (timestamp), lane number (laneno) and queue length (queue_len).
S03, the queue length of lane group is counted according to 5 minutes windows, 5 minutes step-lengths.Data sample is as shown in Figure 2.Lane
It is 52 that group queue length, which takes the queue length value of 2018-09-09 17:00:49 lane number 44,.
When S04, lane group queue length >=α × (benchmark queue length), determine lane group be in congestion status (set α=
2).Wherein, benchmark queue length be flat peak period all lane queue lengths in September 3 days to September 9th average value, calculate after
21, lane group congestion status is as shown in Figure 3.By the value for the lane group queue length being calculated in step S03 be 52, and 52 >=
2×21.It can determine that lane group is in congestion status.
S05, according to above-mentioned calculation method, judge the congestion status of all related lanes groups respectively.And by all correlations
Lane group is divided into the position according to vehicle heading and lane group between two neighboring intersection, is divided into connection lane group and stream
Enter lane group, connection lane group and the congestion status for flowing into lane group determine that situation is as shown in Figure 3.When the congestion of connection lane group
The moment occurs and is differed in T with the congestion generation moment for flowing into lane group, in the present embodiment, T=3min is determined as " related
Congestion " is otherwise " unrelated congestion ".Congestion correlation prediction situation is as shown in Figure 4 between Adjacent Intersections.
S06, in the 15:00-17:00 period on the 9th of September in 2018, (since length is limited, part can only be intercepted as shown in Figure 4
Data),Therefore the congestion spreading rate of intersection A to intersection B is CRA-B=0.63.
The present embodiment with the congestion spreading rate between Adjacent Intersections indicate flow into lane group jam situation with connect lane group
The correlation circumstance of jam situation, congestion spreading rate is higher, and the related congestion ratio between Adjacent Intersections is higher.This method supplements
The urban traffic blocking of microcosmic point is studied, and the congestion correlation between Adjacent Intersections is accurately reflected.When congestion spreading rate is greater than
When 0.5, it is larger that correlation is propagated in the congestion between adjacent intersection.In the present embodiment, congestion of the intersection A to intersection B
Spreading rate is 0.63, and therefore, road conditions administrative section can pay close attention to the congestion feelings of intersection A to intersection B in this, as reference
Condition, the case where avoiding corresponding road traffic congestion generation.
Claims (6)
1. a kind of method for calculating congestion spreading rate between Adjacent Intersections based on queue length, which is characterized in that this method includes
Following steps:
S01, it is based on crossing detection device, acquires the data of adjacent intersection correlation lane group；
S02, the benchmark queue length and lane group queue length that same lane group is calculated according to the data of above-mentioned acquisition, work as vehicle
When road group queue length >=α × benchmark queue length, determine that the lane group is in congestion status, α > 1；
S03, the congestion status for successively identifying all associated lane groups；
S04, lane group, which are divided into, flows into lane group and connection lane group, when the moment occurs for the congestion of connection lane group and flows into lane
Moment difference occurs for the congestion of group in T, is determined as related congestion, 0≤T≤10min；
Otherwise, it is determined that being unrelated congestion；
S05, related congestion frequency to account for the ratio of unrelated congestion and related congestion total degree be gathering around between Adjacent Intersections
Stifled spreading rate, is denoted as CRA-B, then:
Wherein, pkThe congestion correlation of Adjacent Intersections when being calculated for kth time；
When related congestion occurs: pk=1, when unrelated congestion occurs: pk=0；
CRA-BFor the congestion spreading rate of intersection A to intersection B.
2. a kind of method that congestion spreading rate between Adjacent Intersections is calculated based on queue length according to claim 1,
It is characterized in that, in step S01, data include device numbering in all related lane groups of Adjacent Intersections, lane number, inspection
Survey time and queue length.
3. a kind of method that congestion spreading rate between Adjacent Intersections is calculated based on queue length according to claim 2,
It is characterized in that, further includes step S01.1, the step S01.1 between step S01 and step S02 are as follows:
In the data of acquisition, the record of unidentified lane number, detection time exception and Data duplication is deleted, is cleaned
The data of completion are used for step S02.
4. a kind of method that congestion spreading rate between Adjacent Intersections is calculated based on queue length according to claim 1,
It is characterized in that, in step S02, the calculation method of the benchmark queue length of lane group is as follows:
The average value of benchmark queue length=history flat peak period all lane queue lengths.
5. a kind of method that congestion spreading rate between Adjacent Intersections is calculated based on queue length according to claim 1,
It is characterized in that, in step S02, the calculation method of lane group queue length is as follows:
Each added turning lane group queue length is calculated in real time with unit period, when one of added turning lane group only includes a vehicle
When road: the value of lane group queue length takes the value of the queue length got with crossing detection device；
When one of added turning lane group includes two and more than two lanes: in unit period, maximum queuing is grown
Lane group queue length of the value of degree as the added turning lane group.
6. a kind of method that congestion spreading rate between Adjacent Intersections is calculated based on queue length according to claim 1,
It is characterized in that, in step S03:
The same period when at least one inflow lane group gets congestion, determines in the period in all inflow lane groups
Inflow lane group be congestion status；
The same period when at least one connection lane group gets congestion, determines in the period in all connection lane groups
Connection lane group be congestion status.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910070312.2A CN109741603A (en) | 2019-01-24 | 2019-01-24 | A method of based on congestion spreading rate between queue length calculating Adjacent Intersections |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910070312.2A CN109741603A (en) | 2019-01-24 | 2019-01-24 | A method of based on congestion spreading rate between queue length calculating Adjacent Intersections |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109741603A true CN109741603A (en) | 2019-05-10 |
Family
ID=66365966
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910070312.2A Pending CN109741603A (en) | 2019-01-24 | 2019-01-24 | A method of based on congestion spreading rate between queue length calculating Adjacent Intersections |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109741603A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113643529A (en) * | 2021-07-02 | 2021-11-12 | 厦门路桥信息股份有限公司 | Parking lot lane congestion prediction method and system based on big data analysis |
| CN116311990A (en) * | 2023-03-27 | 2023-06-23 | 南京莱斯信息技术股份有限公司 | Signal control method based on fusion of Internet data and detection data |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011152022A1 (en) * | 2010-05-31 | 2011-12-08 | 三洋電機株式会社 | Terminal device |
| KR101123967B1 (en) * | 2010-12-31 | 2012-03-23 | 경희대학교 산학협력단 | Traffic congestion prediction system, prediction method and recording medium thereof |
| CN103050005A (en) * | 2012-11-16 | 2013-04-17 | 北京交通大学 | Method and system for space and time analysis of urban road traffic states |
| CN103886748A (en) * | 2014-03-14 | 2014-06-25 | 浙江大学 | Road network traffic peak grouping identification method |
| CN104392607A (en) * | 2014-11-18 | 2015-03-04 | 青岛海信网络科技股份有限公司 | Method and device for identifying traffic conditions |
| CN105303850A (en) * | 2015-10-20 | 2016-02-03 | 北京长峰金鼎科技有限公司 | Control method for traffic signal lamp of crossing |
| CN105825690A (en) * | 2016-06-15 | 2016-08-03 | 北京航空航天大学 | Coordinated control oriented trunk line crossing correlation analysis and division method |
| CN105869401A (en) * | 2016-05-12 | 2016-08-17 | 华南理工大学 | Road network dynamic partitioning method based on different crowdedness degrees |
| US20170206783A1 (en) * | 2016-01-14 | 2017-07-20 | Siemens Industry, Inc. | Systems and methods to detect vehicle queue lengths of vehicles stopped at a traffic light signal |
| CN108062860A (en) * | 2018-01-16 | 2018-05-22 | 毛国强 | Road bottleneck identification method and its system based on propagation of congestion analysis |
| CN108109382A (en) * | 2018-02-05 | 2018-06-01 | 青岛大学 | A kind of congestion points based on composite network, congestion line, the discovery method of congestion regions |
| CN108257382A (en) * | 2018-01-11 | 2018-07-06 | 上海应用技术大学 | Intersection congestion key point finding method and system based on correlation analysis |
| US10134276B1 (en) * | 2017-12-01 | 2018-11-20 | International Business Machines Corporation | Traffic intersection distance anayltics system |
| CN108898858A (en) * | 2018-06-27 | 2018-11-27 | 上海工程技术大学 | The signal coordinating control method of continuous intersection under a kind of supersaturation traffic behavior |
-
2019
- 2019-01-24 CN CN201910070312.2A patent/CN109741603A/en active Pending
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011152022A1 (en) * | 2010-05-31 | 2011-12-08 | 三洋電機株式会社 | Terminal device |
| KR101123967B1 (en) * | 2010-12-31 | 2012-03-23 | 경희대학교 산학협력단 | Traffic congestion prediction system, prediction method and recording medium thereof |
| CN103050005A (en) * | 2012-11-16 | 2013-04-17 | 北京交通大学 | Method and system for space and time analysis of urban road traffic states |
| CN103886748A (en) * | 2014-03-14 | 2014-06-25 | 浙江大学 | Road network traffic peak grouping identification method |
| CN104392607A (en) * | 2014-11-18 | 2015-03-04 | 青岛海信网络科技股份有限公司 | Method and device for identifying traffic conditions |
| CN105303850A (en) * | 2015-10-20 | 2016-02-03 | 北京长峰金鼎科技有限公司 | Control method for traffic signal lamp of crossing |
| US20170206783A1 (en) * | 2016-01-14 | 2017-07-20 | Siemens Industry, Inc. | Systems and methods to detect vehicle queue lengths of vehicles stopped at a traffic light signal |
| CN105869401A (en) * | 2016-05-12 | 2016-08-17 | 华南理工大学 | Road network dynamic partitioning method based on different crowdedness degrees |
| CN105825690A (en) * | 2016-06-15 | 2016-08-03 | 北京航空航天大学 | Coordinated control oriented trunk line crossing correlation analysis and division method |
| US10134276B1 (en) * | 2017-12-01 | 2018-11-20 | International Business Machines Corporation | Traffic intersection distance anayltics system |
| CN108257382A (en) * | 2018-01-11 | 2018-07-06 | 上海应用技术大学 | Intersection congestion key point finding method and system based on correlation analysis |
| CN108062860A (en) * | 2018-01-16 | 2018-05-22 | 毛国强 | Road bottleneck identification method and its system based on propagation of congestion analysis |
| CN108109382A (en) * | 2018-02-05 | 2018-06-01 | 青岛大学 | A kind of congestion points based on composite network, congestion line, the discovery method of congestion regions |
| CN108898858A (en) * | 2018-06-27 | 2018-11-27 | 上海工程技术大学 | The signal coordinating control method of continuous intersection under a kind of supersaturation traffic behavior |
Non-Patent Citations (3)
| Title |
|---|
| 张婧: "城市道路交通拥堵判别、疏导与仿真", 《中国博士学位论文全文数据库工程科技Ⅱ辑》 * |
| 张彪: "交叉口群拥堵扩散机理及其控制与诱导协同模型研究", 《中国博士学位论文全文数据库工程科技Ⅱ辑》 * |
| 李妍羲: "多层网络耦合的城市交通拥堵传播模型研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113643529A (en) * | 2021-07-02 | 2021-11-12 | 厦门路桥信息股份有限公司 | Parking lot lane congestion prediction method and system based on big data analysis |
| CN116311990A (en) * | 2023-03-27 | 2023-06-23 | 南京莱斯信息技术股份有限公司 | Signal control method based on fusion of Internet data and detection data |
| CN116311990B (en) * | 2023-03-27 | 2023-12-22 | 南京莱斯信息技术股份有限公司 | Signal control method based on fusion of Internet data and detection data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hourdakis et al. | Evaluation of ramp control effectiveness in two twin cities freeways | |
| CN103985250B (en) | The holographic road traffic state vision inspection apparatus of lightweight | |
| Pattara-Atikom et al. | Estimating road traffic congestion using vehicle velocity | |
| CN104408925B (en) | Crossing evaluation of running status method based on display radar | |
| KR101385057B1 (en) | Prediction of urban congestion using ITS based data | |
| CN106781499B (en) | Traffic network efficiency evaluation system | |
| CN109544932A (en) | A kind of city road network flow estimation method based on GPS data from taxi Yu bayonet data fusion | |
| CN109686091B (en) | Traffic flow filling algorithm based on multi-source data fusion | |
| Ban et al. | Bottleneck identification and calibration for corridor management planning | |
| CN106845768A (en) | Bus hourage model building method based on survival analysis parameter distribution | |
| CN110164132A (en) | A kind of detection method and system of road traffic exception | |
| CN105513362B (en) | A kind of bus platform adjacent area bus running state evaluation verification method | |
| CN106530749A (en) | Signal control intersection queuing length estimation method based on single section low frequency detection data | |
| CN109741603A (en) | A method of based on congestion spreading rate between queue length calculating Adjacent Intersections | |
| CN111882858A (en) | Method for predicting queuing length of expressway abnormal events based on multi-source data | |
| CN105913658A (en) | Method for estimating OD position and OD matrix by means of traffic flow | |
| Yang et al. | Travel time distribution under interrupted flow and application to travel time reliability | |
| CN110738853B (en) | Key node identification method based on complex network correlation | |
| CN106530710A (en) | Manager-oriented highway traffic index prediction method and system | |
| CN109830101A (en) | A kind of congestion regions recognition methods based on abnormal microwave traffic data reparation | |
| CN107067723B (en) | A kind of estimation method of Urban road hourage | |
| Fu et al. | An adaptive model for real-time estimation of overflow queues on congested arterials | |
| CN112907088B (en) | Parameter adjustment method and system for score-clearing model | |
| Luk et al. | Characterization of incidents on an urban arterial road | |
| Manthirikul et al. | Comparison of pedestrian delay models at signalized midblock crossings under mixed traffic conditions |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20190510 |