Association knowledge for fatal run-off-road crashes by Multiple Correspondence Analysis

Abstract

a b s t r a c t a r t i c l e i n f o Available online xxxxIn 2013, 346 out of 616 fatal crashes in Louisiana were single vehicle crashes with Run-Off-Road (ROR) crashes being the most common type of single vehicle crash. In order to create effective countermeasures for reducing the number of fatal single vehicle ROR crashes, it is important to identify any associated key factors that can quantitatively assess the performance of roads, vehicles and humans. This research uses Multiple Correspondence Analysis (MCA), a multidimensional descriptive data analysis method that associates a combination of factors based on their relative distance in a two dimensional plane, to analyze eight years (2004-2011) of fatal ROR crashes in Louisiana. This method measures important contributing factors and their degree of association. The results revealed that drivers of lightweight trucks, drivers on undivided state highways, male drivers in passenger-vehicles at dawn, older female (65-74) drivers in non-passenger vehicles, older drivers facing hardship to yield in partial access control zones, and drivers with poor reaction time due to impaired driving were closely associated with fatal ROR crashes. Results of the MCA method can help researchers select the most effective crash countermeasures. Further work on the degree of association between the identified crash contributing factors can help safety management systems develop the most efficient crash reduction strategies.

Traditional Ecological knowledge of predicting rain for climate adapting in North Benin

“…Qualitative data from interview transcripts and field notes were analyzed by way of soft R, which facilitates data storage and particularly the coding and construction of categories, leading to the identification of the aspects that are relevant to the informants. Through such analysis, it is possible to systematize and associated categories in Multiple Correspondence Analysis (MCA) are placed close together in a Euclidean space, leading indicators, or a combinations of points that have similar distributions (Das and Sun, 2015;Das and Sun, 2016). Notably, MCA produces two point indicators (i.e., individuals and categories), which are usually defined by two-dimensional graphs (Das and Sun, 2015).…”

“…Multiple Correspondence Analysis (MCA) is a powerful technique for analysis and graphical presentation of categorical data in large and complex datasets (Das and Sun, 2015;Greenacre, Blasius, 2006;Das and Sun, 2016). MCA graphical overviews, which are more conventional rather than log-liner models, simplify the expression of the relationships between variables without the necessity of any preconditions, thereby making interpretation easier (Das and Sun, 2016). Additionally, very small and very large sample sizes…”

Geometric and Operational Features of Horizontal Curves with Specific Regard to Skidding Proneness

“…The aims of the study were: (a) the identification of recurrent specific features for ROR FI crashes at curves of the road type analyzed, (b) the identification of recurrent specific geometric and operational features that may be associated with the occurrence of ROR FI crashes at curves, and (c) link empirical findings from the study to road safety practice. These research questions were addressed through a micro-analysis approach involving accident reconstructions [16][17][18]39], which allowed for additional insights than traditional macro-level approaches (e.g., [2,54]).…”

A multiple correspondence analysis of at‐fault motorcycle‐involved crashes in Alabama

“…In order to determine the overall squared distance between two individual records, all individual squared distances must be added together, as shown in Equation (2) in Table . Note that the cloud of categories and the cloud of individuals have the same dimension, and each category in this cloud is defined by a point and a weight . Equation (3) in Table shows the squared distance between categories j and j ´ .…”