Evaluating Factors Influencing the Severity of Three-Plus Multiple-Vehicle Crashes using Real-Time Traffic Data

Abstract

Multiple-vehicle crashes involving at least two vehicles constitute over 70% of fatal and injury crashes in the U.S. Moreover, multiple-vehicle crashes involving three or more vehicles (3+) are usually more severe compared with the crashes involving only two vehicles. This study focuses on developing 3+ multiple-vehicle crash severity models for a freeway section using real-time traffic data and crash data for the years 2014–2016. The study corridor is a 111-mile section on I-4 in Orlando, Florida. Crash injury severity was classified as a binary outcome (fatal/severe injury and minor/no injury crashes). For the purpose of identifying the reliable relationship between the 3+ severe multiple-vehicle crashes and the identified explanatory variables, a binary probit model with Dirichlet random effect parameter was used. More specifically, Dirichlet random effect model was introduced to account for unobserved heterogeneity in the crash data. The probit model was implemented using a Bayesian framework and the ratios of the Monte Carlo errors were monitored to achieve parameter estimation convergence. The following variables were found significant at the 95% Bayesian credible interval: logarithm of average vehicle speed, logarithm of average equivalent 10-minute hourly volume, alcohol involvement, lighting condition, and number of vehicles involved (3, or >3) in multiple-vehicle crashes. Further analysis involved analyzing the posterior probability distributions of these significant variables. The study findings can be used to associate certain traffic conditions with severe injury crashes involving 3+ multiple vehicles, and can help develop effective crash injury reduction strategies based on real-time traffic data.

Use of Real-Time Traffic and Signal Timing Data in Modeling Occupant Injury Severity at Signalized Intersections

“…On the other hand, the direction of effect (mean = 0.5, SD = 0.4) for three vehicles in a crash was estimated with 66% certainty only. This attribute's findings are consistent with the literature, suggesting that multiple vehicle crashes (2+) are associated with severe injury (4).…”

“…However, traffic incidents are atypical events; thus, aggregated data may not yield reliable results. Intersection safety analysis requires spatial and temporal high-resolution input data to accurately detect and predict crash risk circumstances (3)(4)(5).…”

Clustering Approach toward Large Truck Crash Analysis

“…Data constraining is a common approach that focuses on a very specific segment of the crash dataset. For examples, Kitali et al focused on multiple-vehicle crashes ( 23 ); Hadi et al and Ghasemzadeh et al analyzed incidents in work zones ( 24 , 25 ); other specific subjects like crashes in rural or urban arterial ( 26 , 27 ) and crashes involving volatile or older adult drivers ( 28 , 29 ) have also been investigated.…”

Calibrated confidence learning for large-scale real-time crash and severity prediction

“…Some efforts to assess the viability of spatial and temporal transferability of real-time crash likelihood prediction models 30 or propose a workflow for real-time crash likelihood prediction, quantification, and classification 39 were also found. Real-time crash severity-related studies have evolved in the literature since 2010, and these studies are conducted in the USA 11,[40][41][42][43][44][45][46][47][48][49] , China 50,51 , France 52 , Greece 53 , and Iran 54 . Most of these studies are conducted on Freeways/Expressways/Toll Roads.…”