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A Method for Extracting Traffic Flow Trend Based on Floating Car Data

A floating vehicle data and changing trend technology, applied in traffic flow detection, electrical digital data processing, special data processing applications, etc., can solve the problem that the changing trend of traffic flow cannot be completely distinguished and extracted, and the characteristics of traffic information expression are not considered, etc. question

Active Publication Date: 2015-10-28
北京千方城市信息科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, most of these methods cluster from a purely mathematical perspective, without considering the characteristics of the expression of traffic information in practical applications, and the trend of traffic flow changes cannot be completely distinguished and extracted

Method used

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  • A Method for Extracting Traffic Flow Trend Based on Floating Car Data
  • A Method for Extracting Traffic Flow Trend Based on Floating Car Data
  • A Method for Extracting Traffic Flow Trend Based on Floating Car Data

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0023] The flow of the traffic flow trend extraction method based on floating car data is as follows: figure 2 As shown, it specifically includes the following steps:

[0024] Step 201. Obtain the historical floating vehicle data of the road link for several months, and classify the data according to the characteristic date. A road segment is the basic spatial unit of traffic information expression. Taking the road segment as the basic processing object, the historical floating vehicle data of a certain road segment for several months (at least three months) is obtained. Divide the floating car data into eight characteristic days: Monday (Mon), Tuesday (Tue), Wednesday (Wed), Thursday (Thu), Friday (Fri), Saturday (Sat), Sunday (Sun), Holidays (Hol), use C to represent the set of characteristic days, then C={Mon,Tue,Wed,Thu,Fri,Sat,Sun,Hol}.

[0025] Step 202, performing denoising and smoothing processing on the historical floating car data. On a daily basis, denoise and s...

Embodiment 2

[0062] Take an auxiliary road of the North Fourth Ring Middle Road in Beijing as an example. This auxiliary road belongs to the secondary road of the city, and the road grade is 4. Carry out clustering on the data of floating cars on 12 Fridays from October to December 2011 (15:00~20:00 (period 180~240)) for 12 days in total, such as Figure 4 As shown, each pattern contains 61 cycles, that is, C=Fri, P=4, n=61. First use the K-means method to cluster the 12-day data into 6 categories, such as Figure 5 As shown, it can be seen that some patterns can be further combined. Then the distance type method and the binary type method are used for further combination. Related parameter values: α=5km / h, β=15kn / h.

[0063] According to the value of the given parameter, calculate when The weighting factor when : The conditions under which patterns can be combined are: d ^ = Σ i ...

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Abstract

The invention discloses a traffic flow change trend extraction method based on floating car data and belongs to the technical field of intelligent transportation. The traffic flow change trend extraction method based on the floating car data comprises obtaining historical floating car data of at least three mouths of a road chain and classifying the data according to feature days; performing denoising and smoothing on the historical floating car data; dividing the historical floating car data of every feature days into at least one time frame according to the change trend of a morning peak and an evening peak; performing preliminary clustering on the classified historical floating car data through a K-means clustering method; further clustering the historical floating car data through a controlled interval method or a two-value method according to a coarse granularity expression of traffic information. The traffic flow change trend extraction method based on the floating car data combines the coarse granularity expression of the traffic information, further merges the traffic flow trends on the basis of a K-means clustering method, enables the traffic flow change trend to be more salient, thereby providing more crystal clear references for traffic flow prediction, route planning and induction, route planning and the like.

Description

technical field [0001] The invention relates to the technical field of intelligent transportation, in particular to a method for extracting traffic flow change trends based on floating car data. Background technique [0002] With the rapid development of intelligent transportation system technology, more and more cities have established advanced traffic information service systems. Real-time traffic data are obtained through data acquisition systems, and after comprehensive processing, traffic flow is predicted, and with the help of the Internet, radio, mobile phones, Variable information boards or car navigation devices release real-time road condition information, and combine traffic information to plan optimal routes for travelers. In addition, traffic information can provide the basis for traffic control and management for traffic control departments, and provide reference for road planning departments to plan road facilities reasonably, so as to achieve the purpose of a...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G08G1/01G06F19/00
Inventor 杨珍珍郭胜敏李平马法进张高峰孙亚夫于晓
Owner 北京千方城市信息科技有限公司
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