Gap, techniques and evaluation: traffic flow prediction using machine learning and deep learning

AbstractThe development of the Internet of Things (IoT) has produced new innovative solutions, such as smart cities, which enable humans to have a more efficient, convenient and smarter way of life. The Intelligent Transportation System (ITS) is part of several smart city applications where it enhances the processes of transportation and commutation. ITS aims to solve traffic problems, mainly traffic congestion. In recent years, new models and frameworks for predicting traffic flow have been rapidly developed to enhance the performance of traffic flow prediction, alongside the implementation of Artificial Intelligence (AI) methods such as machine learning (ML). To better understand how ML implementations can enhance traffic flow prediction, it is important to inclusively know the current research that has been conducted. The objective of this paper is to present a comprehensive and systematic review of the literature involving 39 articles published from 2016 onwards and extracted from four main databases: Scopus, ScienceDirect, SpringerLink and Taylor & Francis. The extracted information includes the gaps, approaches, evaluation methods, variables, datasets and results of each reviewed study based on the methodology and algorithms used for the purpose of predicting traffic flow. Based on our findings, the common and frequent machine learning techniques that have been applied for traffic flow prediction are Convolutional Neural Network and Long-Short Term Memory. The performance of their proposed techniques was compared with existing baseline models to determine their effectiveness. This paper is limited to certain literature pertaining to common databases. Through this limitation, the discussion is more focused on (and limited to) the techniques found on the list of reviewed articles. The aim of this paper is to provide a comprehensive understanding of the application of ML and DL techniques for improving traffic flow prediction, contributing to the betterment of ITS in smart cities. For future endeavours, experimental studies that apply the most used techniques in the articles reviewed in this study (such as CNN, LSTM or a combination of both techniques) can be accomplished to enhance traffic flow prediction. The results can be compared with baseline studies to determine the accuracy of these techniques.

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An AutoEncoder and LSTM-Based Traffic Flow Prediction Method

Sensors ◽

10.3390/s19132946 ◽

2019 ◽

Vol 19 (13) ◽

pp. 2946 ◽

Cited By ~ 19

Author(s):

Wangyang Wei ◽

Honghai Wu ◽

Huadong Ma

Keyword(s):

Traffic Flow ◽

Prediction Accuracy ◽

Short Term Memory ◽

Smart Cities ◽

Prediction Method ◽

Short Term ◽

Term Memory ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Long Short Term Memory

Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days.

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Traffic Flow Prediction Using Long-Short Term Memory Technique for Connected Vehicles in Smart Cities

10.1007/978-3-030-90235-3_36 ◽

2021 ◽

pp. 411-422

Author(s):

Nuraini Shamsaimon ◽

Noor Afiza Mat Razali ◽

Khairani Abd Majid ◽

Suzaimah Ramli ◽

Mohd Fahmi Mohamad Amran ◽

...

Keyword(s):

Traffic Flow ◽

Short Term Memory ◽

Smart Cities ◽

Connected Vehicles ◽

Short Term ◽

Term Memory ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Long Short Term Memory

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A Traffic Flow Forecasting Method Regarding Traffic Network as an Digraph

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001421590436 ◽

2021 ◽

pp. 2159043

Author(s):

Zefei Chen ◽

Jianmin Xu ◽

Yongjie Lin ◽

Bin Feng ◽

Zihao Huang

Keyword(s):

Traffic Flow ◽

Traffic Congestion ◽

Intelligent Transportation System ◽

Undirected Graph ◽

The State ◽

Good Prediction ◽

Traffic Network ◽

The Road ◽

Traffic Flow Prediction ◽

Flow Prediction

Traffic congestion has become a major problem restricting the development of major cities. ITS (Intelligent Transportation System) can record the state of traffic and predict the future traffic state, then reasonably optimize the travel scheme, so as to achieve the purpose of alleviating traffic congestion. Meanwhile, traffic flow prediction can provide data support for ITS, so many researchers have done a lot of research on traffic flow prediction. Many researchers take the traffic network as an undirected graph, and use the GCN (Graph Convolution Network) model to study the traffic flow prediction, and have achieved good prediction results. However, the traffic network is directed, and the traffic network is regarded as an undirected graph, which loses the direction information of the road network. Therefore, this inspires us to propose a graph convolution operator DGCN (Directed GCN), which can make full use of the in degree and out degree information of each station in the traffic network. The experimental results show that the graph convolution neural network based on this operator has better prediction accuracy than the state-of-the-art models.

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Deep Learning with Bidirectional Long Short-Term Memory for traffic flow Prediction

Journal of Physics Conference Series ◽

10.1088/1742-6596/1972/1/012098 ◽

2021 ◽

Vol 1972 (1) ◽

pp. 012098

Author(s):

Song Xue ◽

Chunfu Shao ◽

Shengyou Wang ◽

Yan Zhuang

Keyword(s):

Deep Learning ◽

Traffic Flow ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Long Short Term Memory

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A Machine Learning Approach to Short-Term Traffic Flow Prediction: A Case Study of Interstate 64 in Missouri

2018 IEEE International Smart Cities Conference (ISC2) ◽

10.1109/isc2.2018.8656924 ◽

2018 ◽

Cited By ~ 3

Author(s):

Osama Mohammed ◽

Jalil Kianfar

Keyword(s):

Machine Learning ◽

Traffic Flow ◽

Learning Approach ◽

Short Term ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Machine Learning Approach

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Short-Term Traffic Flow Prediction Based on Ensemble Machine Learning Strategies

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) ◽

10.1109/ddcls52934.2021.9455594 ◽

2021 ◽

Author(s):

Ximu Zeng ◽

Yixiong Wang ◽

Xin Deng ◽

Jin Wang

Keyword(s):

Machine Learning ◽

Traffic Flow ◽

Learning Strategies ◽

Short Term ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Ensemble Machine Learning

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Dynamic Traffic Flow Prediction Based on Long-Short Term Memory Framework With Feature Organization

IEEE Intelligent Transportation Systems Magazine ◽

10.1109/mits.2021.3116156 ◽

2021 ◽

pp. 2-17

Author(s):

Jing Liu ◽

Fangfang Zheng ◽

Xiaobo Liu ◽

Ge Guo

Keyword(s):

Traffic Flow ◽

Short Term Memory ◽

Dynamic Traffic ◽

Short Term ◽

Term Memory ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Long Short Term Memory

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Spatiotemporal Traffic Flow Prediction with KNN and LSTM

Journal of Advanced Transportation ◽

10.1155/2019/4145353 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10 ◽

Cited By ~ 37

Author(s):

Xianglong Luo ◽

Danyang Li ◽

Yu Yang ◽

Shengrui Zhang

Keyword(s):

Support Vector Regression ◽

Traffic Flow ◽

Prediction Models ◽

Short Term Memory ◽

Transportation Systems ◽

Support Vector ◽

K Nearest Neighbor ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Proposed Model

The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.

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