## 2 Literature Review
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Method** & **Task** & **Data Source** \\ \hline
[27] & Deep learning & Safety prediction of rail transit system & Datasets based on the Beijing metro in China \\
[28] & K-means and the entropy method & The risk of passenger flow congestion & 3 rd ring road corridor in Beijing (the bus passenger) \\
[29] & Deep residual neural network framework & Forecast metro station crowd flows & Using the records collected by automated fare collection (AFC) data in Sunzhou, China \\
[30] & Interpretative Structural Modeling (ISM) & Urban rail transit operation safety evaluation & Beijing metro \\
[31] & Failure mode and effects criticality analysis (FMECA) & Risk evaluation of railway rolling stock failures & Data collected from the literature, the company’s maintenance management software system called EQUINOX and the UK’s railway performance management software DATASYS BUCIE \\
[32] & Analysed the whole process of safety risk assessment & The concept of the safety risk assessment of large-scale railway stations & Beijing South railway station \\
[33] & Multilevel fuzzy comprehensive evaluation model & Operation safety of the large high-speed railway station. & Guangzhou South railway station in China \\
[34] & Data envelopment analysis (DEA) & Design the risk evaluation index system of crowd agglomeration and stampedes & In 13 subway stations in Beijing \\
[35] & Bayesian network & Safety risk interactions in rail stations & Risk events were collected from 62 stations worldwide and 241 from China \\
[36] & Quantitative statistics for safety risk analysis & Metro incident management & Shanghai metro operation incident database (MOID) \\
[37] & Fuzzy analytical hierarchy & Analysis of operation risk factors of urban rail transit & Experts’ judgement information \\
[17] & Risk evaluation model & For the safety of passengers and improving the operation efficiency in metro stations & A practical application in Beijing’s GuoMao metro station. \\
[38] & Multi-Criteria Decision-Making (MCDM) model & Train realment risk response strategy & Yangyangcheng station in Shenmu city in China \\
[39] & Nonparametric regression model & Predicting the transfer passenger flow to & Transfer passenger flow or one month in the XIDAN transfer forecast pedestrian congressions & station \\
[40] & Grey clustering and dynamic identification method & Passenger flow congestion risk in rail transit & Real-time data of AFC (Automatic Fare Collection) system from Duonghileum rail transit hub station in China \\  & Combines failure mode and effect analysis (FMEA) and pessimistic-optimistic fuzzy information axiom (POFIA) & Evaluate the risk of railway dangerous goods transportation system & The historical data of Chinese railway dangerous goods transportation system (BDNTS) accidents from 1986 to 2017 \\
[41] & Nonparametric regression model & Forecasting traffic flow incidents & Collected incident-free data in the freeway in San Francisco Bay \\
[43] & Online spatio-temporal & Crowd flow prediction & Dataset of Sydney trains network and the weather data from the Australian Bureau of Meteorology \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Review of flow prediction methods from previous studies.