A crash severity analysis at highway-rail grade crossings: The random survival forest method. (September 2020)

• Record Type: Journal Article
• Authors: Keramati, Amin; Lu, Pan; Iranitalab, Amirfarrokh; Pan, Danguang; Huang, Ying
• Is Part Of: Accident analysis and prevention. Volume 144(2020)
• Publication Date: 2020-09
• Subjects: Accident prediction -- Railroad grade crossing -- Machine learning -- Random survival forests; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2020.105683
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Random survival forest method in highway rail grade crossing safety analysis is introduced. Long-term time effects on cumulative probability of crash severity and occurrence over 29 years is evaluated. Contributors' long-term and instantaneous effects on crash severity and occurrence behave very different. Adding stop sign to active controlled crossings will reduce crash risk up to 7 years. Audible device to active crossings will reduce crash/PDO/injury/fatal likelihoods by 49 %, 52 %, 46 %, and 50 % respectively. Abstract: 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.