Artificial Intelligence-Based Traffic Flow Prediction: A Comprehensive Review

Sayed, Sayed A.; Abdel-Hamid, Yasser; Hefny, Hesham Ahmed · 2022 · Crossref

DOI: 10.21203/rs.3.rs-1885747/v1

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Summary

This paper presents a comprehensive systematic review of Artificial Intelligence (AI) techniques applied to traffic flow prediction, a critical component of Intelligent Transportation Systems (ITS). The research is motivated by the increasing urban congestion, rising road traffic fatalities, and the need for accurate forecasting to support route planning, road construction, and traffic management. The authors aim to categorize and evaluate the most popular machine learning (ML) and deep learning (DL) methods used in this domain while identifying inherent obstacles and future directions for these technologies. The study employs a literature review methodology to analyze the evolution of traffic prediction models. It begins by contextualizing historical parametric models, such as time series analysis, Kalman Filtering, and Auto-Regressive Integrated Moving Average (ARIMA), noting their limitations in handling the stochastic and non-linear nature of traffic data. The review then systematically examines non-parametric ML approaches, detailing supervised learning algorithms including Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Logistic and Linear Regression, Decision Trees, Random Forests, and Naïve Bayes. It also covers unsupervised learning techniques like K-Means Clustering, Principal Component Analysis (PCA), and Latent Dirichlet Allocation (LDA), as well as reinforcement learning methods such as Q-Learning and Monte Carlo Tree Search. Furthermore, the paper investigates deep learning architectures, specifically Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) networks, explaining their structural mechanisms and advantages in extracting complex features from raw data. The findings highlight a distinct shift from traditional statistical models to data-driven AI approaches. The review establishes that while shallow neural networks and basic ML algorithms offer simplicity and computational efficiency, they often struggle with non-linear relationships and large datasets. In contrast, deep learning models, particularly LSTM and CNN, demonstrate superior performance in capturing temporal dependencies and spatial patterns in traffic data. The authors detail the specific operational benefits of each algorithm; for instance, Random Forests mitigate overfitting through ensemble voting, while LSTMs address the vanishing gradient problem inherent in standard RNNs, allowing for effective long-term sequence learning. The paper also identifies specific challenges for each method, such as the sensitivity of KNN to high-dimensional data and the computational intensity of training deep networks. The significance of this work lies in its structured comparison of AI methodologies for traffic forecasting, providing a clear roadmap for researchers and practitioners. By outlining the theoretical background, operational mechanics, and limitations of various ML and DL techniques, the paper underscores the necessity of moving toward more sophisticated deep learning frameworks to handle the complexity of modern ITS data. The review concludes by emphasizing that while AI offers robust solutions for traffic prediction, challenges related to data quality, computational costs, and model generalization remain critical areas for future research.

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