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Despite the zero tolerance and minimum legal drinking age laws since 1999, crashes caused by underage drinking drivers have occurred every year in the United States, which is a huge cause for concern for roadway safety. This study utilized multiple correspondence analysis (MCA) on 9 years (2010 to 2018) of at-fault, underage (aged 15 to 20 years), alcohol-intoxicated driver crashes in Louisiana to investigate hidden crash attribute patterns. This exploratory multivariate analysis technique identifies systematic associations among categories of qualitative variables rather than recognizing the effect of a single factor on the response variable. The results exhibited the capability of MCA in discovering the meaningful clouds of crash contributory factors from a complex multidimensional dataset. Fatal crashes happened to underage alcohol-intoxicated drivers who were not using seatbelts on high-speed curve segments under dark without streetlights, whereas single-vehicle crashes resulted in moderate injuries among drinking novice teenagers operating light trucks. The findings also revealed the driving behavior patterns of rookie drivers after alcohol intake that resulted in collisions. For example, male teenagers were engaged in impaired driving during weekends under adverse weather conditions, and underage drinking drivers tended to use cellphones during late-night driving. Targeting critical attributes identified from associations could be helpful in reducing the number of related crashes and fatalities. Furthermore, knowledge gained about the attribute groups identified in this study could be included in educational training programs targeting risky driving maneuvers. Integration of multiple interventions could be more strategic in minimizing underage drinking collisions.

Animal-vehicle crashes (AVCs) are a significant issue in Louisiana that requires attention. Data on AVCs that occurred from 2015 to 2020 were obtained from the Louisiana Department of Transportation and Development (DOTD), including 14,349 crashes with major injury (KA), minor injury (BC), and no injury (O) severity groups. Aiming to find the collective association of attributes from AVC data, which are categorical in nature, this study utilized two data mining methods: multiple correspondence analysis (MCA) and association rule mining (ARM). Five hierarchical clusters that were generated from the BC and O AVC datasets were particularly significant. Among several other findings, MCA revealed that BC and O AVCs are more concentrated on parish roads during the spring season, while O AVCs in the fall and winter tend to occur on highways with speed limits of 50 mph or higher. ARM revealed that moderate-speed parish roads are frequently associated with KA and BC AVCs, particularly in residential areas and during the spring season, and they often involve young drivers. The findings of this study can be particularly beneficial by considering the spatiotemporal factors associated with animal concentration and movement to develop targeted interventions and mitigation strategies.

Estimation of the capacity of work zones is vital to manage the possibility of traffic flows exceeding capacity and resulting in unbearable queues during work zone lane closures. A plethora of research papers have studied several ways to estimate work zone capacity, with the Highway Capacity Manual (HCM) having its own methodology to estimate capacity based on various site characteristics. However, HCM always recommends validating its model with local data to reflect the actual driving behavior of the region. This study considered work zone capacity as a function of queue discharge rate (QDR), defined as the 15-min average flow rate immediately after breakdown, also known as postbreakdown flow rate. By collecting data from 10 different work zones within the state of Louisiana, the study estimated QDR and its corresponding duration at breakdowns. An average QDR of 1,664 pcphpl and an associated queue of 120 min average duration was found. Analysis of variance showed that average QDRs across all sites were not significantly different. The QDR prediction model revealed that a closed right lane and a work zone on linear roadways significantly increased the discharge rate. However, the presence of nearby exit ramps, daytime scenarios, and an increase in the speed ratio and truck percentages were found to decrease the discharge rate. A separate model for the duration of queue or breakdown found the time of day, change in the speed ratio, presence of entry ramp, location of work zones, and annual average daily traffic of the roadway to be significant variables.