## 2 Literature Review
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Method** & **Task** & **Data Source** \\ \hline
[44] & Fusion of support vector regression and neural network & Predicting abnormal passenger flow & Yangji station in Guangzhou, China \\
[45] & Combining fuzzy pet net (FPN) and fault tree analysis (FTA) & Railway safety risk assessment and control optimization. & The Shijiahuang high-speed railway station \\
[46] & Algorithm utilizing the cost theory and automatic frequency control (CT-AFC) & Railway passenger flow recognition in the terminal station & A questionnaire survey of the passenger travel characteristics for the passengers of the Shanghai railway, APC data and dynamic video data \\
[47] & Deep learning method & Analysis of railway accidents & The accident reports from the Federal Railroad Administration (FRA) in the U.S. \\
[48] & Multiscale radial basis function network & Forecasting the irregular fluctuation of subway passenger flows & Transit smart card data in Beijing’s subway \\
[49] & Machine learning for safety at railway stations & Analysis of accidents at railway stations & The history of the accidents at railway stations in the UK \\
[50] & Test mining and natural language process (NLP) techniques & Extraction and analysis of risk factors & Chinese railway accident reports \\
[51] & Hybrid particle swarm optimization and support vector machine PSO-SVM model & A hybrid risk-prediction framework for design safety of metro station construction & Using an expert interview method \\
[52] & Big data and natural language processing & Identify heightened risk on the railway & Reports of railway accidents in the UK from 2008 to 2010 \\
[53] & An ordinal regression model & Assessing safety and security in a railway station as perceived by travellers & Data collected from a survey carried out in the station of Frosinone (Italy) \\
[54] & A safety-related risk assessment model & Design practices regarding passenger hazard & National Thai station design practices as a baseline \\
[55] & Analytic hierarchy process (AHP) & Safety risk assessment of railway station & A Wuhan railway station in China \\
[56] & Dispatching fault log management and analysis database system (DELMIS) & Risk early-warning and promoting the safety management level of metro & Shanghai rail transit operation data in China \\
[57] & Cause Congsonence Analysis (CCA) and Fuzzy Causal Model (PCM) & Risk analysis initiated by human factors (passengers and operators) & Historical incident data from metro Beijing operational risks in China \\
[58] & Nonlinear combined weighting method based on AHP and entropy-weighted method & Risk assessment of railway freight station & A station in North China \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._The literature review suggests that artificial intelligence (AI) methods for flow prediction have become a new trend and have gained a lot of academic and industrial interest. According to such literature, we have entered the era of big data [59; 60; 61; 62]. The new technology will play an important role in the future of flow prediction and in dealing with the flow forecast issues of intelligent transportation systems. Studies have been utilising a wide range of data; however, the acquisition of data is a problem for researchers. More datasets are necessary for training to improve prediction performance. Recently, traffic prediction (both numerical and image based) has greatly developed to cover all transportation types, including vehicles, buses, taxis, and trains, with more studies using road data. Moreover, it is linked to many aspects, such as delays, activity patterns, weather, fuel consumption, pollution, city congestion, driving speed, traffic and public flow, and pedestrian flow characteristics. Therefore, it is a challenge to focus on one specific prediction such as the congestion in railway stations with risk and safety management perspectives.