Accident and hazard prediction models for highway–rail grade crossings: a state-of-the-practice review for the USA
Olumide F. Abioye, Maxim A. Dulebenets, Junayed Pasha, Masoud Kavoosi, Ren Moses, John Sobanjo, Eren Erman Özgüven
Abstract
Abstract Highway–rail grade crossings (HRGCs) are one of the most dangerous segments of the transportation network. Every year numerous accidents are recorded at HRGCs between highway users and trains, between highway users and traffic control devices, and solely between highway users. These accidents cause fatalities, severe injuries, property damage, and release of hazardous materials. Researchers and state Departments of Transportation (DOTs) have addressed safety concerns at HRGCs in the USA by investigating the factors that may cause accidents at HRGCs and developed certain accident and hazard prediction models to forecast the occurrence of accidents and crossing vulnerability. The accident and hazard prediction models are used to identify the most hazardous HRGCs that require safety improvements. This study provides an extensive review of the state-of-the-practice to identify the existing accident and hazard prediction formulae that have been used over the years by different state DOTs. Furthermore, this study analyzes the common factors that have been considered in the existing accident and hazard prediction formulae. The reported performance and implementation challenges of the identified accident and hazard prediction formulae are discussed in this study as well. Based on the review results, the US DOT Accident Prediction Formula was found to be the most commonly used formula due to its accuracy in predicting the number of accidents at HRGCs. However, certain states still prefer customized models due to some practical considerations. Data availability and data accuracy were identified as some of the key model implementation challenges in many states across the country.