Local abnormity factor-based urban heavy-traffic road detection method

A local anomaly factor and urban traffic technology, which is applied in traffic flow detection, road vehicle traffic control system, traffic control system, etc., can solve the problems of lack of bus GPS data traffic congestion detection method, unfavorable traffic information service, etc.

Inactive Publication Date: 2016-05-25
DALIAN UNIV OF TECH
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  • Abstract
  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

However, in the existing technology, there is no method of using local abnormal factors to detect abnormally congested traffic sections, and there is also a lack of methods for detecting abnormally congested traffic sections using massive bus GPS data, which is not conducive to providing traffic information services

Method used

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  • Local abnormity factor-based urban heavy-traffic road detection method
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  • Local abnormity factor-based urban heavy-traffic road detection method

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Embodiment Construction

[0052] In order to make the purpose, technical solution and advantages of the present invention clearer, the specific implementation manners of the present invention will be further described in detail below.

[0053] Embodiments of the present invention provide a method for detecting urban traffic congestion road sections based on local abnormal factors, such as figure 1 As shown, the method includes:

[0054] Step 1: Divide the 332 bus GPS data in Hangzhou in October 2014 into m*n space-time segments by using the Hangzhou bus line station table. Among them, m=16077, divide the bus running time from 6:00 to 21:00 into a time period every hour, that is, n=16.

[0055] The structure of the bus line station table is shown in Table 1 below.

[0056] serial number

name

note

1

serial number

The value is an integer within 1-26574, which uniquely identifies a record

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line number

The value is a positive integer, which uniquely identi...

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Abstract

The invention discloses a local abnormity factor-based urban heavy-traffic road detection method. The method is characterized by comprising the steps of: firstly dividing urban bus GPS data into space-time segments from the space and time perspectives by using bus route and station data, extracting, from the space-time segments, characteristic values which reflect the road traffic condition, calculating a local abnormity factor of each space-time segment by using the characteristic value, calculating an abnormity index of each road section, ranking the abnormity indexes, and finally obtaining the heavy-traffic road of the urban traffic. The local abnormity factor-based urban heavy-traffic road detection method of the invention has the characteristics of strong feasibility, wide application range and low manpower consumption, makes it possible to automatically detect the heavy-traffic road of urban traffic by using data and provides effective information for urban traffic planning.

Description

technical field [0001] The invention relates to the field of urban intelligent transportation, in particular to a method for detecting urban traffic congestion road sections based on local abnormal factors. Background technique [0002] With the rapid development of urban traffic, how to effectively adjust traffic congestion, optimize road usage, and improve people's travel efficiency has become the focus of research in the field of urban intelligent transportation. One of the key technologies is to detect abnormally congested roads in urban traffic, that is, to detect abnormally congested road sections in urban traffic through certain technical methods. Bus routes can generally cover the main routes of the entire urban road network, and the route of the bus will not change due to road conditions, so the GPS data of the bus operation can reflect the real situation of urban traffic. The invention calculates local abnormal factors of road sections by extracting appropriate ch...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G08G1/01
CPCG08G1/0104
Inventor 孔祥杰宋茜萌杨卓夏锋
Owner DALIAN UNIV OF TECH
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