Highway OD prediction method based on depth set empirical mode decomposition

A technology that integrates empirical modes and highways, applied in the field of traffic information, can solve problems such as inability to obtain results, achieve the effects of improving calculation efficiency, reducing model complexity, and increasing prediction accuracy

Active Publication Date: 2021-07-09
BEIHANG UNIV +1
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AI Technical Summary

Problems solved by technology

Due to the complexity and randomness of OD data, it is impossible to obtain better results by directly inputting it into the GRU network.

Method used

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  • Highway OD prediction method based on depth set empirical mode decomposition
  • Highway OD prediction method based on depth set empirical mode decomposition
  • Highway OD prediction method based on depth set empirical mode decomposition

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Embodiment Construction

[0011] The present invention will be described in further detail below in conjunction with example.

[0012] The present invention proposes an expressway OD prediction method based on deep ensemble empirical mode decomposition, which mainly includes three steps, and its flow is as follows figure 1 As shown, specifically:

[0013] Step 1: Expressway OD sequence extraction

[0014] Select all toll data in the expressway road network from the database, and eliminate duplicate data, that is, two data with exactly the same information such as license plate number, entrance station location, entrance passing time, exit station location, and exit passing time. Secondly, it is judged whether there is abnormal data, that is, the time from entering the expressway to leaving the expressway is too short.

[0015] (1) According to the distance between the shortest stations on the expressway and the maximum speed limit value, define the minimum time threshold set T = (t 1 , t 2 , t 3 ,...

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Abstract

The invention discloses a highway OD prediction method based on deep set empirical mode decomposition. The method comprises the steps of 1, extracting a highway OD sequence; 2, carrying out time feature extraction based on ensemble empirical mode decomposition; and 3, carrying out OD sequence prediction based on the GRU network. According to the prediction method provided by the invention, the ensemble empirical mode decomposition model and the deep learning network are combined, on one hand, the model can convert non-stationary nonlinear data into stationary linear data, a great auxiliary effect is achieved on the prediction model to mine a sequential relationship, and the prediction precision of the model is improved; and on the other hand, the model complexity can be reduced, and the model calculation efficiency is improved.

Description

technical field [0001] The invention relates to the field of traffic information, in particular to an expressway OD prediction method based on deep ensemble empirical mode decomposition. Background technique [0002] The mileage of my country's expressways is increasing year by year. By the end of 2020, the mileage of my country's expressways will reach 155,000 kilometers, effectively supporting national construction and development. Subsequently, a large amount of highway entrance and exit toll data is generated, which includes information such as license plate number, entrance site location, entrance passing time, exit station location, and exit passing time. Through the analysis and calculation of toll data, the traffic volume between the departure point and the destination (OD) within a certain period of time can be obtained, which can be used to reflect the time-space state and travel demand of the expressway road network to a certain extent. Predicting OD volume can h...

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Application Information

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IPC IPC(8): G08G1/01G06N3/04G06N3/08
CPCG08G1/0104G06N3/08G06N3/048
Inventor 于海洋刘帅任毅龙于海生
Owner BEIHANG UNIV
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