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Urban traffic bottleneck mining method

A technology for urban transportation and bottlenecks, applied in the field of intelligent transportation, can solve problems such as poor reliability and achieve good reliability.

Active Publication Date: 2015-07-08
ENJOYOR COMPANY LIMITED
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In order to overcome the shortcomings of poor reliability of existing urban traffic bottleneck mining methods, the present invention provides a reliable urban traffic bottleneck mining method

Method used

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  • Urban traffic bottleneck mining method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0098] Example 1: Set the statistical period as 20 days, and the morning peak time period is from 7:30 to 9:00. Due to the impact of major activities nearby in the first 5 days on section A, the morning peak congestion time in the first 5 days is longer, and the morning peak congestion time in the 20 days is as follows:

[0099] [60,65,65,65,70,5,0,5,0,10,5,0,5,5,10,0,0,0,5,5],

[0100] The congestion time of road section B on each day in the statistical period is:

[0101] [20,15,15,20,30,25,15,10,15,20,20,15,15,20,20,15,25,15,20,20].

[0102] Find the bottleneck severity of road section A and road section B respectively.

[0103] Note: Road section A with long congestion due to major events should not be regarded as a traffic bottleneck conceptually, it does not belong to frequent congestion, and is not general. On the contrary, Section B will be congested for 10 minutes to 30 minutes in the morning rush hour almost every day, which can be regarded as regular congestion. ...

Embodiment 2

[0132] Embodiment 2: Traffic state estimation based on microwave data and k-nearest neighbor algorithm. There is a sample library of trunk road section templates as shown in the following table, in which the section speed, flow rate and lane occupancy rate within 5 minutes are measured by microwave sensors, and whether the road section is congested is marked by video playback:

[0133] Sample No.

speed(km / h)

Traffic (vehicle)

Lane occupancy rate (%)

Is it congested

1

19

10

60

yes

2

22

23

52

yes

3

40

87

20

no

4

64

92

12

no

5

15

16

46

yes

[0134] table 3

[0135] If the cross-sectional data measured by microwave radar on a trunk road section to be classified: the speed is 24km / h, the flow rate is 27 vehicles, and the lane occupancy rate is 70%, the k-nearest neighbor method is used to estimate the traffic status of this road sec...

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Abstract

An urban traffic bottleneck mining method includes the following steps that firstly, microwave data and floating car data are cleaned, wherein data interpolation is carried out to solve the data missing problem of the microwave data and the floating car data; secondly, road segment traffic states are estimated by taking set time quanta as time granularity; thirdly, the total occurrence probability of traffic congestion of each road segment in the set time quanta within a statistical cycle is calculated; fourthly, the balance degree of traffic congestion occurring at each day within the statistical cycle is calculated; fifthly, the upper bound of the balance degrees of traffic congestion within the statistical cycle is worked out; sixthly, the normalization balance degree of traffic congestion within the statistical cycle is worked out; seventhly, the severity of traffic bottlenecks is figured out. The urban traffic bottleneck mining method has good reliability.

Description

technical field [0001] The patent of the present invention belongs to the field of intelligent transportation, and specifically relates to a traffic bottleneck mining method based on statistics. Background technique [0002] With the continuous and rapid increase of urban vehicles, urban traffic congestion continues to aggravate. The main reason is that the growth rate of vehicles is much faster than that of road construction, resulting in a situation where supply exceeds demand. Urban traffic congestion leads to increased travel costs for the public, and excessive exhaust emissions due to travel delays further deteriorate the living environment with serious consequences. In order to alleviate urban traffic congestion and optimize the allocation of limited road resources, it is an important means to analyze and utilize the value contained in the data collected by traffic sensors and then carry out effective traffic organization, which is the main basis for traffic management...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G08G1/01
CPCG08G1/0125
Inventor 李建元温晓岳吴越
Owner ENJOYOR COMPANY LIMITED
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