A Hybrid Short-Term Traffic Flow Multistep Prediction Method Based on Variational Mode Decomposition and Long Short-Term Memory Model

Qichun Bing,Weijian Zhang,Yanran Hu,Dayi Qu,Xiufeng Chen,Fuxin Shen,Ya Jia

doi:10.1155/2021/4097149

Abstract

Timely and accurate traffic prediction information is essential for advanced traffic management system (ATMS) and advanced traveler information system (ATIS). Because of the characteristics of nonlinearity, nonstationarity, and randomness, short-term traffic flow prediction could be still a challenging task. In this study, a hybrid short-term traffic flow multistep prediction method is proposed by combining the variational mode decomposition (VMD) algorithm and long short-term memory (LSTM) model. Firstly, the VMD algorithm is employed to decompose the original traffic flow data into a series of intrinsic mode function (IMF) components. Secondly, different LSTM models are established to predict different IMF components. For each prediction model, one-step to three-step predictions are carried out. Finally, the component prediction results are aggregated to obtain the final traffic flow multistep prediction values. The prediction performance of the proposed hybrid model is investigated using inductive loop data measured from the north-south viaduct expressway in Shanghai. The experiment results show that (1) VMD algorithm could effectively avoid the problems of endpoint effects and modal aliasing, and the decomposition effect is better than empirical mode decomposition algorithm and wavelet decomposition algorithm; (2) among all the involved methods, the proposed hybrid model is more effective and robust in extracting the trend information, which has the best multistep prediction performance.

Highlights

High-precision traffic flow prediction information is definitely considered as one of the most important parts in intelligent transportation systems. e accurate and timely traffic flow prediction information can provide strong support for travel decisions and transform traffic management mode
To achieve accuracy short-term traffic flow forecasting, a hybrid short-term traffic flow multistep prediction method based on variational mode decomposition (VMD) algorithm and long short-term memory (LSTM) model is proposed. e main idea consists of two parts: the data processing technique and the performance of the prediction model
1-step to 3-step iterative predictions are carried out. e traffic flow data measured from the north-south viaduct expressway in Shanghai are used to carry out the experiment

Summary

Introduction

High-precision traffic flow prediction information is definitely considered as one of the most important parts in intelligent transportation systems. e accurate and timely traffic flow prediction information can provide strong support for travel decisions and transform traffic management mode. High-precision traffic flow prediction information is definitely considered as one of the most important parts in intelligent transportation systems. E accurate and timely traffic flow prediction information can provide strong support for travel decisions and transform traffic management mode. It would enable them to plan their trips ahead of time and timely adjust their trip mode and trip route with the dynamic short-term traffic prediction information. The short-term traffic flow prediction information would enable them to take traffic control measures early enough to avoid traffic congestion rather than to deal with the traffic problems after the traffic congestion has already occurred [1]. Owing to the randomness and volatility characteristics of the traffic flow data, it is difficult to develop the satisfactory short-term traffic flow prediction models

Methods

Results

Conclusion