Intelligent traffic time period dividing method based on sliding average algorithm

A technology of time division and sliding average, which is applied in the direction of road vehicle traffic control system, traffic control system, traffic flow detection, etc. It can solve the problem of difficulty in accurately describing the changing situation of traffic flow, and achieve the effect of improving effectiveness.

Active Publication Date: 2019-04-30
航天科工广信智能技术有限公司
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Problems solved by technology

At present, the most common method of setting thresholds for actual traffic flow data to distinguish between peak and...

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  • Intelligent traffic time period dividing method based on sliding average algorithm
  • Intelligent traffic time period dividing method based on sliding average algorithm
  • Intelligent traffic time period dividing method based on sliding average algorithm

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Embodiment Construction

[0024] The present invention will be further described below in conjunction with the accompanying drawings.

[0025] refer to Figure 1 ~ Figure 3 , a method for dividing intelligent traffic time slots based on the moving average algorithm, including the following steps:

[0026] 1) Enter the specific date, intersection, and lane to obtain the traffic flow information of the corresponding intersection road; take Δt=5 minutes as the time interval, divide the day into 288 time periods, and obtain the passing data from the database, and calculate the 288-dimensional The traffic flow of the intersection is denoted as V, where V(t) represents the traffic flow of the current intersection at the time period t;

[0027] 2) Using the moving average algorithm to smooth V, the calculation method is shown in the following formula (1):

[0028]

[0029] In the formula: τ is the half period length of sliding; w i is the weight of each period V, where ∑w i = 1; is the traffic flow d...

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Abstract

The invention discloses an intelligent traffic time period dividing method based on a sliding average algorithm. The method comprises the following steps: 1) the road traffic flow information at an intersection is acquired; 2) the sliding average algorithm is used for smoothing the acquired data; 3) difference calculation is carried out through the smoothed data, and a corresponding derivative, that is, the change trend of the traffic flow, is obtained; and 4) according to the different change characteristics of the traffic flow derivative, further data discrimination is carried out, the traffic state of a day is divided to multiple time periods, and a basis is provided for the design of a timing scheme in the next step. The data are smoothed by the sliding average algorithm, the data derivative is approximated by the difference result, the time periods are divided, the traffic flow data change trend can be reflected more sensitively, the traffic condition of the road network can be recognized more accurately, and the effectiveness of the timing scheme is improved.

Description

technical field [0001] The invention relates to the application fields of traffic control engineering and big data analysis, and in particular to a traffic peak-valley state identification and a time division method of a timing scheme. Background technique [0002] With the improvement of the degree of urban modernization, the scale of urban traffic has also maintained a trend of rapid growth, and traditional traffic technology has begun to find it difficult to adapt to the requirements of social development. Due to the complexity and uncertainty of traffic flow changes, the most prominent one is that due to the impact of morning and evening peak hours, urban traffic conditions change frequently and complexly. In order to alleviate urban traffic conditions, based on the characteristics of different time periods, it is necessary to configure different Therefore, the accuracy and effectiveness of the division of peak and valley periods is particularly important. At present, t...

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Application Information

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IPC IPC(8): G08G1/01G06Q10/06G06Q50/30
CPCG06Q10/06393G06Q50/30G08G1/0145
Inventor 陈明王勐彭吉友
Owner 航天科工广信智能技术有限公司
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