The Federal Motor Carrier Safety Administration (FMCSA) defines commercial motor vehicles (CMVs) as trucks with a gross vehicle weight rating (or gross combination weight rating) of over 10,000 lbs, any motor vehicle that seats and transports 9 or more people (including the driver), or any motor vehicle with a hazardous materials placard. In 2020, nearly 8% of all vehicle crashes in the District of Columbia involved trucks and buses. These crashes have a negative impact on individuals, causing economic loss, physical and mental injuries, and fatalities. CMV crashes are a major safety concern across the country, as they can result in severe injuries, fatalities, and traffic disruptions. According to the National Highway Traffic Safety Administration’s (NHTSA) 2020 Traffic Safety Facts, the percentage of large trucks involved in fatal crashes in DC was 6.8%, lower than the US average of 9.4% based on 2018 data. Therefore, it is crucial to identify the factors that contribute to CMV crashes in order to prevent and reduce the number of crashes overall.
The roughness of the pavement on roadways is a crucial factor that can impact various aspects such as vehicle operating costs, ride quality, safety, and travel speeds. The research team will collect pavement condition data for designated truck, CMV, and bus routes in DC from the District Department of Transportation. CMV-related crashes reported between 2016 and 2020 will also be extracted from the Metropolitan Police Department's crash database. The collected data will be analyzed to determine the impact of pavement conditions on CMV crashes in DC, along with other factors. Descriptive statistics and a summary of the data will be presented to provide a quantitative understanding of road segment conditions, vehicular volume, and crash history. Geographic Information System (GIS) mapping software will be utilized for spatial analysis to map and analyze pavement conditions on DC road segments. The study will also explore a multiple regression model to establish a relationship between pavement conditions (based on scores and types), traffic volumes, and CMV crash history.
U.S. Department of Transportation, Office of the Assistant Secretary for Research and Technology - $250,000
The study will examine prediction models built using neural networks to identify potential crash sites based on road conditions, which can help prioritize pavement rehabilitation projects. A summary of the study’s findings and recommendations will be prepared and submitted to FMCSA and DDOT for review and potential implementation.