Short-time traffic flow prediction method based on long-time and short-time memory recurrent neural network

A recursive neural network and long-short-term memory technology, applied in the field of intelligent transportation systems, can solve problems such as inability to memorize historical traffic flow data, unsatisfactory prediction accuracy, and inability to dynamically determine the optimal history length, etc., to achieve good scalability and high prediction The effect of precision

Active Publication Date: 2016-03-09
SHANGHAI JIAO TONG UNIV
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AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem that the existing method cannot memorize the historical traffic flow data, and thus cannot dynamically determine the optimal historical

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  • Short-time traffic flow prediction method based on long-time and short-time memory recurrent neural network
  • Short-time traffic flow prediction method based on long-time and short-time memory recurrent neural network
  • Short-time traffic flow prediction method based on long-time and short-time memory recurrent neural network

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[0040] Example

[0041] This embodiment provides a short-term traffic flow prediction method based on a long and short-term memory recurrent neural network. The method includes the following steps:

[0042] Step S1: Aggregate historical traffic flow data according to the predicted time interval;

[0043] The historical traffic flow data comes from a traffic data collection system, and can be obtained through methods such as coil detection, microwave sensors, and video monitoring.

[0044] The historical traffic flow data obtained is the number of vehicles passing by a specific observation point or road section within a certain time interval. The specified time interval may be specified according to predicted demand (for example, 15 minutes).

[0045] A number of traffic flow data within a specified time interval of each observation point or road section are respectively accumulated to obtain traffic flow data at a specified time interval of each observation point. The following traffi...

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Abstract

The invention discloses a short-time traffic flow prediction method based on a long-time and short-time memory recurrent neural network. The method comprises the following steps: aggregating inputted historical traffic flow data according to a prediction time interval of a short-time traffic flow; carrying out pretreatment on the aggregated historical traffic flow; setting a reasonable parameter for a long-time and short-time memory recurrent neural network; training a neural network prediction model by using data after pretreatment; and invoking a traffic flow, predicted by the prediction model, of a designated time interval and evaluating a prediction error. According to the invention, because the long-time and short-time memory recurrent neural network has the advantage of being capable of memorizing inputted historical data for long time, the high prediction precision is realized; and the expansibility for different prediction intervals is good.

Description

technical field [0001] The invention belongs to the field of intelligent traffic systems, and in particular relates to a short-term traffic flow prediction method based on a long-short-term memory recursive neural network. Background technique [0002] As one of the core contents of Intelligent Transportation System (Intelligent Transportation System, ITS), intelligent traffic control and guidance system has always been a hot topic in the research of intelligent transportation system. The realization of intelligent control and guidance can effectively alleviate traffic congestion, reduce environmental pollution, and provide efficient and safe road traffic conditions. The premise and key of these realizations is to be able to accurately predict short-term traffic flow, and the accuracy of prediction directly determines the effectiveness of control and induction. [0003] According to the literature search of the prior art, it is found that the existing prediction models can ...

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Application Information

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IPC IPC(8): G08G1/01G08G1/065
CPCG08G1/0129G08G1/065
Inventor 潘理田永学
Owner SHANGHAI JIAO TONG UNIV
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