## 1 Introduction
Given the impact of deep learning on traffic prediction, the significance of RNNs and their variants, such as long short-term memory (LSTM) networks and Gated Recurrent Unit (GRU) networks, stands out prominently. The design philosophy of RNNs enables them to naturally handle time series data, which is crucial for traffic prediction as traffic features, like flow and speed, vary over time. By capturing long-term dependencies in time series, RNNs can accurately forecast future changes in traffic conditions. Mainly, variants of RNNs, like LSTMs and GRUs, address the issue of vanishing or exploding gradients faced by traditional RNNs when dealing with long sequences through specialized gating mechanisms. This further enhances prediction accuracy and stability. As neural network models for traffic prediction become increasingly popular, the diversification of deep learning models complicates the assessment of the current state and future directions of this research field. Despite the emergence of advanced models, like transformers, which have demonstrated superior performance in traffic prediction, reviewing the applications of RNNs in this field remains essential. Recent developments, such as the introduction of the Test-Time Training (TTT) [1] and the Extend LSTM (xLSTM) model by the original author of LSTM in 2024 [2], have reinvigorated interest in RNNs, showcasing their exceptional predictive capabilities and highlighting a resurgence in their relevance. RNNs offer a critical baseline for evaluating newer architectures, and understanding RNNs' capabilities and limitations provides valuable insights into model design and optimization, which can still be advantageous in specific contexts. The scarcity of specialized studies on RNN models further complicates understanding their application in traffic prediction. This review addresses these challenges by providing a comprehensive overview, targeting professionals interested in applying RNNs for traffic prediction. Starting with the definition of the problem and a brief history of traffic prediction, it then delves into RNNs within traffic prediction research. It discusses the comparison between RNNs and other state-of-the-art methods and the challenges RNNs face in traffic prediction. The insights that can be extracted from this paper are as follows:  
1. The unique characteristics of traffic data and common input data representations in traffic prediction are summarized. 2. The statement of traffic prediction problems is generalized. 3. A comprehensive overview of seven sub-categories of applications of current deep learning models based on the structures of RNNs in traffic prediction is conducted. 4. A detailed comparison between RNNs and other state-of-the-art deep learning models is conducted. In addition to the comparison of RNNs with other state-of-the-art models on public datasets, such as the Performance Measurement System (PeMS, [[http://pems.dot.ca.gov/](http://pems.dot.ca.gov/)]([http://pems.dot.ca.gov/](http://pems.dot.ca.gov/)) (accessed on 2 March 2024)), we design a comparative study focused on short-term passenger flow prediction using a real-world metro smart card dataset. This study allows us to directly compare the predictive performance of RNNs with other models in a practical, real-world context. 5. Transformers excel with long sequences and complex patterns, but RNNs can outperform with shorter sequences and smaller datasets. The metro data used in our comparative study favored LSTM, showing that simpler models can sometimes provide more accurate and efficient predictions. Choosing the right model based on the dataset and resources is crucial. 6. The future challenges facing RNNs in traffic prediction and how to deal with these challenges are discussed. The paper is structured as follows. In Section 2, we review the development history of the traffic prediction field over the past six decades and describe various data-driven prediction methods.