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| Subjects | Publications | Main contents | Methods | Weights | Data |
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| Multifactor analysis and evaluation | [7] | Identify the most significant human performance shaping factors | Factors identification framework | — | Accident statistics |
| [8] | Evaluate operational safety and the availability of signalling systems | Improved Markov model | — | State monitoring of signalling systems |
| [10] | Evaluate environmental safety through establishing the impact index system of weather | Attribute recognition model | Natural attribute weight | Environmental statistics |
|
| Analysis and mining of accident characteristics | [11] | Analyze the accident and its spreading processes | System-theoretic accident models and process | — | Accident reports (4·28 China-Jiaoji) |
| [12, 30] | Accident causation analysis | Factors identification, analysis, and classification model | — | Accident/incident reports |
| [13] | Discover and reveal relationships and patterns among accidents | Association rules mining techniques | Subjective weight | Accident records |
| [29] | Present a more comprehensive analysis of the accident | Fault tree and quantitative analysis | Expert scoring weight | Accident reports (7·23 China-Yongwen) |
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| Safety state prediction | [14, 17] | State prediction based on the representative index system | Neural network | — | Accident statistics |
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| Risk management | [26] | Describe the dynamic changing process of system safety | Cusp catastrophe model | — | Accident records |
| [27] | Raise awareness of potential safety risks | Risk mechanism analysis | — | Related literature |
| [28] | Local risk estimation | Bayesian network | — | Incidents records |
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| Safety situation evaluation | This paper | Evaluate operational safety situation from a regional perspective | SPA-TOPSIS-CSM | Combined weight (subjective, natural, and entropy) | Accident statistics |
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