Traffic Flow Combination Forecasting Method Based on Improved LSTM and ARIMA

06/25/2019
by   Boyi Liu, et al.
4

Traffic flow forecasting is hot spot research of intelligent traffic system construction. The existing traffic flow prediction methods have problems such as poor stability, high data requirements, or poor adaptability. In this paper, we define the traffic data time singularity ratio in the dropout module and propose a combination prediction method based on the improved long short-term memory neural network and time series autoregressive integrated moving average model (SDLSTM-ARIMA), which is derived from the Recurrent Neural Networks (RNN) model. It compares the traffic data time singularity with the probability value in the dropout module and combines them at unequal time intervals to achieve an accurate prediction of traffic flow data. Then, we design an adaptive traffic flow embedded system that can adapt to Java, Python and other languages and other interfaces. The experimental results demonstrate that the method based on the SDLSTM - ARIMA model has higher accuracy than the similar method using only autoregressive integrated moving average or autoregressive. Our embedded traffic prediction system integrating computer vision, machine learning and cloud has the advantages such as high accuracy, high reliability and low cost. Therefore, it has a wide application prospect.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/20/2021

Forecasting The JSE Top 40 Using Long Short-Term Memory Networks

As a result of the greater availability of big data, as well as the decr...
research
03/05/2020

Cellular Traffic Prediction with Recurrent Neural Network

Autonomous prediction of traffic demand will be a key function in future...
research
05/02/2018

A Dynamic Model for Traffic Flow Prediction Using Improved DRN

Real-time traffic flow prediction can not only provide travelers with re...
research
12/06/2017

Short-Term Prediction of Signal Cycle in Actuated-Controlled Corridor Using Sparse Time Series Models

Traffic signals as part of intelligent transportation systems can play a...
research
12/25/2017

Network-Scale Traffic Modeling and Forecasting with Graphical Lasso and Neural Networks

Traffic flow forecasting, especially the short-term case, is an importan...
research
08/25/2020

Prediction of Hilbertian autoregressive processes : a Recurrent Neural Network approach

The autoregressive Hilbertian model (ARH) was introduced in the early 90...
research
09/21/2021

Short-term traffic prediction using physics-aware neural networks

In this work, we propose an algorithm performing short-term predictions ...

Please sign up or login with your details

Forgot password? Click here to reset