CN111192465A - Method for realizing signal timing scheme group division processing based on flow data - Google Patents
Method for realizing signal timing scheme group division processing based on flow data Download PDFInfo
- Publication number
- CN111192465A CN111192465A CN202010014089.2A CN202010014089A CN111192465A CN 111192465 A CN111192465 A CN 111192465A CN 202010014089 A CN202010014089 A CN 202010014089A CN 111192465 A CN111192465 A CN 111192465A
- Authority
- CN
- China
- Prior art keywords
- flow
- scheme group
- time period
- timing scheme
- signal timing
- 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.)
- Withdrawn
Links
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/07—Controlling traffic signals
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention relates to a method for realizing signal timing scheme group division processing based on flow data, which comprises the following steps: taking an average value of each flow data with the same attribute; calculating the flow variation amplitude; counting the flow variation amplitude of each time interval in a time period, and counting the total number of the time intervals with different flow variation amplitudes; judging whether the total number of time intervals with different flow variation amplitudes in a time period is smaller than a preset value or not, if so, judging that the flow conditions in the time period have similarity; otherwise, the flow rate conditions in the time period have obvious difference; and determining the attribute day of the scheme group divided by the same time interval by taking the week as a unit. By adopting the method for realizing the division processing of the signal timing scheme group based on the flow data, the traffic laws of the intersection on different attribute days are more effectively researched and judged through two-dimensional calculation analysis based on the original vehicle passing data of the vehicles at the intersection; a new angle is provided for optimizing intersection signal control, and the problem of urban traffic jam is relieved more scientifically.
Description
Technical Field
The invention relates to the field of intelligent traffic, in particular to the field of intelligent signal control, and specifically relates to a method for realizing signal timing scheme group division processing based on flow data.
Background
With the rapid development of social economy, urban traffic problems become more prominent, which brings great pressure to traffic management functional departments. At present, a method for further optimizing time interval division of an intersection signal control scheme by analyzing the change rule of intersection flow in one week is lacked.
At present, the signal control method is simple, and timing calculation is carried out mainly according to a traffic management department or traffic optimization practitioner through on-site simple data investigation and a traffic engineering method. However, due to the complexity and uncertainty of traffic flow change, the urban traffic state changes frequently and complexly, and in order to relieve the urban traffic condition, different scheme groups need to be configured to dredge the road based on the characteristics of different attribute days. Therefore, the accuracy and the effectiveness of day and time interval division with different attributes and large traffic rule difference are particularly important in week units. However, at present, when there is no timing optimization requirement, the timing scheme of the previous day is usually used only continuously in the next day, and certain limitation exists, so that the method is difficult to accurately adapt to the change situation of the traffic flow.
The invention can further scientifically and finely optimize the existing signal control by processing the original flow data, is a strong demand of a public security traffic management department, and is a booster for realizing an intelligent intersection.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method for realizing the division processing of the signal timing scheme group based on the flow data, which is accurate, efficient and wide in application range.
In order to achieve the above object, the method for implementing signal timing scheme group division processing based on traffic data of the present invention is as follows:
the method for realizing the division processing of the signal timing scheme group based on the flow data is mainly characterized by comprising the following steps:
(1) taking an average value of each flow data with the same attribute;
(2) calculating the flow variation amplitude;
(3) counting the flow variation amplitude of each time interval in a time period, and counting the total number of the time intervals with different flow variation amplitudes;
(4) judging whether the total number of time intervals with different flow variation amplitudes in a time period is smaller than a preset value or not, if so, judging that the flow conditions in the time period have similarity; otherwise, the flow rate conditions in the time period have obvious difference;
(5) and determining the attribute day of the scheme group divided by the same time interval by taking the week as a unit.
Preferably, the step (1) specifically comprises the following steps:
(1.1) acquiring flow data one month, three months, half a year or one year prior to the selected date;
(1.2) screening out dates with the same attribute as the selected dates and all corresponding flow rates of the dates;
and (1.3) taking an exponential weighted average according to the screened dates.
Preferably, the step (2) specifically comprises the following steps:
(2.1) determining a base line date;
(2.2) finding out the maximum flow of a low peak-flattening time period;
and (2.3) finding out the median flow in the peak time period to obtain a summary table of the flow change amplitude.
Preferably, the step (3) is specifically:
and counting the flow change amplitude of each time interval in comparison with the previous day, and counting the total number of the time intervals with the flow change amplitudes respectively larger than 10%, 15% and 20%.
Preferably, the step (5) specifically comprises the following steps:
(5.1) judging whether any n is smaller than a limit value, if so, the flow curves are similar; otherwise, the difference of the flow curves is large;
and (5.2) determining a timing scheme group adopted by each day in the week according to the flow change trend, combining attribute days adopting the same scheme group, and counting the signal timing scheme group in the week.
By adopting the method for realizing the division processing of the signal timing scheme group based on the flow data, the traffic laws of the intersection on different attribute days are more effectively researched and judged through two-dimensional calculation analysis based on the original vehicle passing data of the vehicles at the intersection; accurately analyzing intersection rules and configuring a more detailed signal scheme for the intersection rules; a new angle is provided for optimizing intersection signal control, and the problem of urban traffic jam is relieved more scientifically.
Drawings
Fig. 1 is a flowchart of a method for implementing a signal timing scheme group division process based on traffic data according to the present invention.
Detailed Description
In order to more clearly describe the technical contents of the present invention, the following further description is given in conjunction with specific embodiments.
As shown in fig. 1, the method for implementing signal timing scheme group division processing based on traffic data of the present invention includes the following steps:
(1) taking an average value of each flow data with the same attribute;
(1.1) acquiring flow data one month, three months, half a year or one year prior to the selected date;
(1.2) screening out dates with the same attribute as the selected dates and all corresponding flow rates of the dates;
(1.3) taking an exponential weighted average according to the screened dates;
(2) calculating the flow variation amplitude;
(2.1) determining a base line date;
(2.2) finding out the maximum flow of a low peak-flattening time period;
(2.3) finding out the median flow in the peak time period to obtain a summary table of flow change amplitude;
(3) counting the flow variation amplitude of each time interval in a time period, and counting the total number of the time intervals with different flow variation amplitudes;
(4) judging whether the total number of time intervals with different flow variation amplitudes in a time period is smaller than a preset value or not, if so, judging that the flow conditions in the time period have similarity; otherwise, the flow rate conditions in the time period have obvious difference;
(5) determining attribute days of the scheme groups divided by the same time period by taking a week as a unit;
(5.1) judging whether any n is smaller than a limit value, if so, the flow curves are similar; otherwise, the difference of the flow curves is large;
and (5.2) determining a timing scheme group adopted by each day in the week according to the flow change trend, combining attribute days adopting the same scheme group, and counting the signal timing scheme group in the week.
As a preferred embodiment of the present invention, the step (3) specifically comprises:
and counting the flow change amplitude of each time interval in comparison with the previous day, and counting the total number of the time intervals with the flow change amplitudes respectively larger than 10%, 15% and 20%.
In the specific implementation mode of the invention, the original data is primarily processed according to the field of the time period by extracting the total intersection flow, the flow of each inlet channel and the flow of each flow direction every 15min for nearly one week, one month, three months, half year and 1 year, so that the problems of data abnormality, data loss and the like are mainly solved; taking an average value of each flow data with the same attribute; calculating the flow variation amplitude; counting the flow variation amplitude (coarse time interval); the time periods of different attribute days are finely divided in units of weeks. The technical scheme is as follows:
firstly, taking the average value of each flow data with the same attribute
And (3) taking the flow data of the previous month, three months, half year and one year of the backward deduction of the selected date, screening the date with the same attribute (day of week) as the selected date and all the corresponding flow, and taking an index weighted average.
Data table after weighted average of index according to attribute
Secondly, calculating the flow change amplitude
0：00-6：00，
6：15-10：00，
10：15-15：00，
15：15-19：00，
19：15-21：00，
21：15-23：45，
Finally, the following summary table of the flow rate variation amplitude is obtained.
Third, flow variation amplitude statistics (coarse time interval)
Last stepThe steps are roughly divided into 6 time periods. When counting, each time interval corresponds to 3 tables which are bounded by +/-10%, +/-15% and +/-20%. For example, ± 10%, the brief introduction period 0: 00-6: 00 n1And (4) calculating.
n1In period 0: 00-6: 00, the magnitude of the change in flow rate per tuesday interval relative to tuesday (i.e., the magnitude of the change in flow rate per tuesday interval)
) Greater than or equal to 10% of the total.
Fourth, similarity analysis (time interval level)
0: 00-6: 00(25) if niIf > 9(i ═ 1, 2, …, 10), then the time periods are significantly different;
6: 15-10: 00(16) if niIf > 3(i ═ 1, 2, …, 10), then the time periods are significantly different;
10: 15-15: 00(20) if niIf > 5(i ═ 1, 2, …, 10), then the time periods are significantly different;
15: 15-19: 00(16) if niIf > 3(i ═ 1, 2, …, 10), then the time periods are significantly different;
19: 15-21: 00(8) if niGreater than 2(i ═ 1, 2, …, 10), then there was significant variability in this time period;
21: 15-23: 45(11) if n isi> 2(i ═ 1, 2, …, 10), the time periods are clearly different.
Note: the above-defined limits are calibrated after observing multiple different types of intersections in the city of Changzhou. In order to ensure higher studying and judging accuracy, the flow conditions of more intersections need to be observed.
Fifthly, determining attribute days which can adopt the same time interval to divide scheme groups by taking weeks as units
When n of any period is satisfiediBoth are smaller than the upper limit given by the corresponding time period, then two-by-two similarity can be inferred. And then the similarity of the three and above is deduced.
Analyzing 24-hour flow change of working days and weekends, determining a timing scheme group adopted by each day of the week according to the flow change trend, combining attribute days adopting the same scheme group, and counting the signal timing scheme group of the week.
By adopting the method for realizing the division processing of the signal timing scheme group based on the flow data, the traffic laws of the intersection on different attribute days are more effectively researched and judged through two-dimensional calculation analysis based on the original vehicle passing data of the vehicles at the intersection; accurately analyzing intersection rules and configuring a more detailed signal scheme for the intersection rules; a new angle is provided for optimizing intersection signal control, and the problem of urban traffic jam is relieved more scientifically.
In this specification, the invention has been described with reference to specific embodiments thereof. It will, however, be evident that various modifications and changes may be made thereto without departing from the broader spirit and scope of the invention. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.
Claims (5)
1. A method for realizing signal timing scheme group division processing based on flow data is characterized by comprising the following steps:
(1) taking an average value of each flow data with the same attribute;
(2) calculating the flow variation amplitude;
(3) counting the flow variation amplitude of each time interval in a time period, and counting the total number of the time intervals with different flow variation amplitudes;
(4) judging whether the total number of time intervals with different flow variation amplitudes in a time period is smaller than a preset value or not, if so, judging that the flow conditions in the time period have similarity; otherwise, the flow rate conditions in the time period have obvious difference;
(5) and determining the attribute day of the scheme group divided by the same time interval by taking the week as a unit.
2. The method for implementing signal timing scheme group partition processing based on traffic data according to claim 1, wherein the step (1) specifically comprises the following steps:
(1.1) acquiring flow data one month, three months, half a year or one year prior to the selected date;
(1.2) screening out dates with the same attribute as the selected dates and all corresponding flow rates of the dates;
and (1.3) taking an exponential weighted average according to the screened dates.
3. The method for implementing signal timing scheme group partition processing based on traffic data according to claim 1, wherein the step (2) specifically comprises the following steps:
(2.1) determining a base line date;
(2.2) finding out the maximum flow of a low peak-flattening time period;
and (2.3) finding out the median flow in the peak time period to obtain a summary table of the flow change amplitude.
4. The method for implementing signal timing scheme group partition processing based on traffic data according to claim 1, wherein the step (3) is specifically as follows:
and counting the flow change amplitude of each time interval in comparison with the previous day, and counting the total number of the time intervals with the flow change amplitudes respectively larger than 10%, 15% and 20%.
5. The method for implementing signal timing scheme group partition processing based on traffic data according to claim 1, wherein the step (5) specifically comprises the following steps:
(5.1) judging whether any n is smaller than a limit value, if so, the flow curves are similar; otherwise, the difference of the flow curves is large;
and (5.2) determining a timing scheme group adopted by each day in the week according to the flow change trend, combining attribute days adopting the same scheme group, and counting the signal timing scheme group in the week.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010014089.2A CN111192465A (en) | 2020-01-07 | 2020-01-07 | Method for realizing signal timing scheme group division processing based on flow data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010014089.2A CN111192465A (en) | 2020-01-07 | 2020-01-07 | Method for realizing signal timing scheme group division processing based on flow data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111192465A true CN111192465A (en) | 2020-05-22 |
Family
ID=70709879
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010014089.2A Withdrawn CN111192465A (en) | 2020-01-07 | 2020-01-07 | Method for realizing signal timing scheme group division processing based on flow data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111192465A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111951572A (en) * | 2020-07-07 | 2020-11-17 | 永嘉县公安局交通警察大队 | Time interval division optimization method for multi-time interval signal control scheme of urban road intersection |
| CN113851007A (en) * | 2021-09-27 | 2021-12-28 | 阿波罗智联(北京)科技有限公司 | Time interval dividing method and device, electronic equipment and storage medium |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR844917A (en) * | 1937-10-19 | 1939-08-04 | Automatic Telephone & Elect | Improvements to automatic traffic control systems |
| CN106408960A (en) * | 2016-10-29 | 2017-02-15 | 浙江大学 | Signal control time period dividing method based on ordered clustering |
| CN108171998A (en) * | 2018-02-11 | 2018-06-15 | 深圳市智能交通技术有限公司 | A kind of crossing self-adapting traffic signal control system and its method of work based on the alert data of electricity |
| CN108389406A (en) * | 2018-04-26 | 2018-08-10 | 苏州城方信息技术有限公司 | Signal control time Automated Partition Method |
| CN108615375A (en) * | 2018-05-28 | 2018-10-02 | 安徽畅通行交通信息服务有限公司 | A kind of intersection signal timing Time segments division method |
| CN109697849A (en) * | 2018-12-26 | 2019-04-30 | 航天科工广信智能技术有限公司 | Intelligent transportation Time segments division method based on sliding average algorithm |
| CN109712393A (en) * | 2019-01-10 | 2019-05-03 | 浙江工业大学 | Intelligent transportation Time segments division method based on Gaussian process regression algorithm |
| CN110008415A (en) * | 2019-03-21 | 2019-07-12 | 北京仝睿科技有限公司 | A kind of data object variation tendency determines method, apparatus and server |
| CN110162547A (en) * | 2018-01-19 | 2019-08-23 | 曲逸文 | A kind of class periods rules method for digging based on temporal data system |
| CN110610607A (en) * | 2019-07-26 | 2019-12-24 | 浙江工业大学 | Traffic-based SCATS control scheme operation cycle automatic diagnosis method and system |
-
2020
- 2020-01-07 CN CN202010014089.2A patent/CN111192465A/en not_active Withdrawn
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR844917A (en) * | 1937-10-19 | 1939-08-04 | Automatic Telephone & Elect | Improvements to automatic traffic control systems |
| CN106408960A (en) * | 2016-10-29 | 2017-02-15 | 浙江大学 | Signal control time period dividing method based on ordered clustering |
| CN110162547A (en) * | 2018-01-19 | 2019-08-23 | 曲逸文 | A kind of class periods rules method for digging based on temporal data system |
| CN108171998A (en) * | 2018-02-11 | 2018-06-15 | 深圳市智能交通技术有限公司 | A kind of crossing self-adapting traffic signal control system and its method of work based on the alert data of electricity |
| CN108389406A (en) * | 2018-04-26 | 2018-08-10 | 苏州城方信息技术有限公司 | Signal control time Automated Partition Method |
| CN108615375A (en) * | 2018-05-28 | 2018-10-02 | 安徽畅通行交通信息服务有限公司 | A kind of intersection signal timing Time segments division method |
| CN109697849A (en) * | 2018-12-26 | 2019-04-30 | 航天科工广信智能技术有限公司 | Intelligent transportation Time segments division method based on sliding average algorithm |
| CN109712393A (en) * | 2019-01-10 | 2019-05-03 | 浙江工业大学 | Intelligent transportation Time segments division method based on Gaussian process regression algorithm |
| CN110008415A (en) * | 2019-03-21 | 2019-07-12 | 北京仝睿科技有限公司 | A kind of data object variation tendency determines method, apparatus and server |
| CN110610607A (en) * | 2019-07-26 | 2019-12-24 | 浙江工业大学 | Traffic-based SCATS control scheme operation cycle automatic diagnosis method and system |
Non-Patent Citations (1)
| Title |
|---|
| 赵伟明: "面向交通控制的时段划分与子区划分", 《中国优秀硕士学位论文全文数据库（工程科技Ⅱ辑）》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111951572A (en) * | 2020-07-07 | 2020-11-17 | 永嘉县公安局交通警察大队 | Time interval division optimization method for multi-time interval signal control scheme of urban road intersection |
| CN113851007A (en) * | 2021-09-27 | 2021-12-28 | 阿波罗智联(北京)科技有限公司 | Time interval dividing method and device, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109410574B (en) | Phase-phase signal control scheme-oriented timing parameter optimization method | |
| CN111192465A (en) | Method for realizing signal timing scheme group division processing based on flow data | |
| CN106920402A (en) | A kind of time series division methods and system based on the magnitude of traffic flow | |
| CN107368915B (en) | Subway passenger travel time selection behavior analysis method | |
| CN108615375B (en) | Intersection signal timing time interval dividing method | |
| CN114020975A (en) | Method for automatically screening flood field | |
| CN110956826A (en) | Method and device for generating traffic signal timing scheme and storage medium | |
| Growns et al. | Characterisation of flow in regulated and unregulated streams in eastern Australia | |
| CN110555448B (en) | Method and system for subdividing dispatch area | |
| CN111815973B (en) | Signalized intersection analysis method and related equipment | |
| CN111626860B (en) | System and method for judging high-frequency transaction by combining historical and real-time financial data | |
| Bond et al. | Package ‘hydrostats’ | |
| van Toorenburg | Performance of motorways and trunk routes at high traffic volumes | |
| Stevanovic et al. | Long-term benefits of adaptive traffic control under varying traffic flows during weekday peak hours | |
| CN109523788B (en) | Road section operation effect evaluation method | |
| CN112652164A (en) | Traffic time interval dividing method, device and equipment | |
| Anggraini et al. | Comparison of Indonesian Banking Performance Before and During The Covid-19 Pandemic | |
| CN113066286A (en) | Method and device for judging vehicle running risk road sections of vehicle-road cooperative road network | |
| CN116051173B (en) | Passenger flow prediction method, passenger flow prediction system and bus dispatching method | |
| CN114155037A (en) | Work result visualization method and system | |
| CN102096887A (en) | Step-by-step progressive screening method for tax payment assessment model | |
| CN109801047A (en) | The prompting message generation method and device of finance and money management product | |
| Hirsch et al. | Introduction to the EGRET package | |
| CN115759423B (en) | Project audit management system and method based on big data | |
| Wobbrock | Statistical Analysis and Reporting in R |
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 | ||
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20200522 |