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Road section travel time forecasting method based on LSTM

A technology of travel time and prediction method, applied in the field of intelligent transportation system, to achieve the effect of high prediction accuracy and good robustness

Inactive Publication Date: 2016-11-09
INST OF AUTOMATION CHINESE ACAD OF SCI
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Problems solved by technology

There is currently no LSTM recurrent neural network based road segment travel time prediction method

Method used

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  • Road section travel time forecasting method based on LSTM
  • Road section travel time forecasting method based on LSTM
  • Road section travel time forecasting method based on LSTM

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Embodiment Construction

[0044] The present invention will be described in detail below in conjunction with the accompanying drawings. It should be noted that the described embodiments are only intended to facilitate the understanding of the present invention, rather than limiting it in any way.

[0045] The present invention provides an LSTM-based road section travel time prediction method. Such as figure 1 As shown, the method includes two parts: prediction model generation and travel time prediction in the future period;

[0046] Predictive model generation:

[0047] Step A1: Perform data normalization processing on the historical travel time data of the specified road section;

[0048]The historical travel time data comes from the traffic data collection system, and the time when the vehicle enters and exits the road section can be obtained through the automatic vehicle recognition equipment at the exit and entrance of the road section, and then the travel time of the vehicle on the designated r...

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Abstract

The invention provides a road section travel time forecasting method based on LSTM. The method comprises forecasting model generation and forecasting of travel time in a future period of time. The step of forecasting model generation comprises the steps that A1 data normalization processing is carried out on the history travel time data of a designated road section; and A2 the normalized history travel time data are used to train an LSTM recursive neural network to acquire a forecasting model. The step of forecasting of travel time in the future period of time comprises the steps that B1 data normalization processing is carried out on current travel time data through the same method used in step A1; the forecasting model is input to forecast the travel time in the future period of time; and inverse normalization processing is carried out on a forecasting result to acquire the travel time in the future period of time. A short-time correlation mode and a long-time association mode of travel time data can be excavated. The method automatically adjusts the contribution of historical information to current forecasting according to the current state, and has the advantages of high prediction accuracy and good robustness.

Description

technical field [0001] The invention belongs to the field of intelligent transportation systems, in particular to a method for predicting travel time of road sections. Background technique [0002] Road segment travel time is an important indicator to reflect traffic conditions, and it is also one of the traffic information that people pay most attention to when traveling. The road section travel time has the characteristics of great difficulty in collection and many influencing factors. With the acceleration of the pace of life in modern society, planned travel has increasingly become a priority for people to travel, and obtaining the predicted travel time in advance has become an urgent need for people to travel. The prediction of road travel time can not only meet the actual needs of people traveling, but also help to achieve effective traffic guidance. [0003] Existing travel time prediction methods are mainly based on linear models, time series models, spectral analy...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06N3/04
CPCG06Q10/047G06N3/04
Inventor 王飞跃段艳杰吕宜生陈圆圆林懿伦刘裕良
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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