Traffic abnormal road section probability identification method

An identification method and abnormal technology, applied in the field of intelligent transportation, can solve the problems of poor reliability and low adaptability

Active Publication Date: 2015-06-24
ENJOYOR COMPANY LIMITED
View PDF7 Cites 17 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the disadvantages of poor reliability and low adaptability of existing methods for identifying abnormal road sections, the present invention provides a probabilistic identification method for traffic abnormal sections with good reliability and high adaptability

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Traffic abnormal road section probability identification method
  • Traffic abnormal road section probability identification method
  • Traffic abnormal road section probability identification method

Examples

Experimental program
Comparison scheme
Effect test

example

[0084] Example: the calculation method of road anomaly index, including the following steps:

[0085] Step 1: Divide the 24 hours of the day into several 5-minute time slots. Assume that the latest 5 minutes in the database of the day are 12:20-12:25. After data cleaning, the real-time data of microwave point 890 on the day is Figure 4 .

[0086] WAVE_ID indicates the microwave point number, which is related to the road section, DEV_WAY_ID marks different lanes, SPEED is the average speed of the current lane, TOTAL_FLOW is the total flow of the current lane, COLLECT_DATE is the date of data collection, and COLLECT_TIME is the time of data collection. The recent historical data of No. 890 microwave point after data cleaning is as follows: Figure 5 shown.

[0087] Step 2: Take the vehicle speed data of microwave point 890 as an example to test the normality of the data source, and directly calculate the kurtosis coefficient and skewness coefficient of the vehicle speed in th...

Embodiment 2

[0093] Embodiment 2: abnormal traffic case.

[0094] Taking Hangzhou road network data as an example, using the above method to identify abnormal road sections, Figure 10 Calculated results for the anomaly index at 14:45 on December 5, 2014: Figure 10 The middle and large box is the road section ranked fifth in the anomaly index. The first number 133 represents the number of the microwave point, the second number 0.8798103 represents the anomaly index of the current road section, followed by the name and direction of the road section where the current microwave point is located; the small box It indicates that the real-time average vehicle speed during the time period from 14:45 to 14:50 of the day is 8km / h, and the total flow is 70 vehicles. It can be found that the current vehicle speed and flow are obviously abnormal.

[0095] The road section of No. 133 Microwave Point is from east to west on Shixiang Road in Hangzhou (near the Guashan Overpass). Through a video search...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

A traffic abnormal road section probability identification method comprises steps that 1, after vehicle speed and data of flow quantity are cleaned, a sample space is formed by fusing sampling data; 2, a normality test is conducted on the vehicle speed and a data source of the flow quantity; 3, vehicle speed of each microwave point and a mean value and variance of the flow quantity are calculated; 5, vehicle speed abnormal indexes and flow quantity abnormal indexes are calculated; 6, descending order is conducted on the abnormal indexes to output an warning, road abnormal indexes D of current time slots of all the microwave points of all road networks are calculated in an ergodic mode, the calculated abnormal index results are arranged from large to small, and the top K most abnormal road section warnings are output. The invention provides the traffic abnormal road section probability identification method which is good in reliability and higher in adaptivity.

Description

technical field [0001] The invention belongs to the field of intelligent transportation, and in particular relates to a method for evaluating real-time abnormal road traffic conditions based on microwave data, which is used to guide traffic managers to actively check the real traffic conditions of abnormal road sections through video monitoring and other means. Background technique [0002] With the rapid growth of my country's economy and the continuous advancement of urbanization, the number of motor vehicles has increased rapidly, and the demand for travel has continued to increase. As a result, infrastructure construction cannot keep up with the increase in traffic demand, causing great troubles to traffic managers. The traffic control department will consider deploying police forces to guide traffic as appropriate for frequent traffic bottlenecks. For occasional road congestion caused by uncontrollable reasons such as road construction, bad weather, and traffic accidents...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G08G1/01
CPCG08G1/0133G08G1/052
Inventor 王浩李建元赵贝贝张麒李芳顾超
Owner ENJOYOR COMPANY LIMITED
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap