A crash severity analysis at highway-rail grade crossings: The random survival forest method
Autor: | Amin Keramati, Amirfarrokh Iranitalab, Pan Lu, Ying Huang, Danguang Pan |
---|---|
Rok vydání: | 2020 |
Předmět: |
Computer science
Crash severity Human Factors and Ergonomics Crash Risk Assessment Machine Learning Risk Factors 0502 economics and business Statistics Humans 0501 psychology and cognitive sciences Safety Risk Reliability and Quality Railroads 050107 human factors Survival analysis Event (probability theory) 050210 logistics & transportation 05 social sciences Accidents Traffic Public Health Environmental and Occupational Health Cumulative effects Cumulative incidence function Level crossing Variable (computer science) Environment Design Female human activities |
Zdroj: | Accident Analysis & Prevention. 144:105683 |
ISSN: | 0001-4575 |
DOI: | 10.1016/j.aap.2020.105683 |
Popis: | This paper proposes a machine learning approach, the random survival forest (RSF) for competing risks, to investigate highway-rail grade crossing (HRGC) crash severity during a 29-year analysis period. The benefits of the RSF approach are that it (1) is a special type of survival analysis able to accommodate the competing nature of multiple-event outcomes to the same event of interest (here the competing multiple events are crash severities), (2) is able to conduct an event-specific selection of risk factors, (3) has the capability to determine long-term cumulative effects of contributors with the cumulative incidence function (CIF), (4) provides high prediction performance, and (5) is effective in high-dimensional settings. The RSF approach is able to consider complexities in HRGC safety analysis, e.g., non-linear relationships between HRGCs crash severities and the contributing factors and heterogeneity in data. Variable importance (VIMP) technique is adopted in this research for selecting the most predictive contributors for each crash-severity level. Moreover, marginal effect analysis results real several HRGC countermeasures' effectiveness. Several insightful findings are discovered. For examples, adding stop signs to HRGCs that already have a combination of gate, standard flashing lights, and audible devices will reduce the likelihood of property damage only (PDO) crashes for up to seven years; but after the seventh year, the crossings are more likely to have PDO crashes. Adding audible devices to crossing with gates and standard flashing lights will reduce crash likelihood, PDO, injury, and fatal crashes by 49 %, 52 %, 46 %, and 50 %, respectively. |
Databáze: | OpenAIRE |
Externí odkaz: |