Exploration of the relationship among roadway characteristics, operating speed, and crashes for city streets using path analysis.

Estimating the speed-crash relationship has long been a focus area of interest in roadway safety analysis. Because of many confounding factors that may influence both speeds and crashes, the relationship cannot be appropriately established without considering the corresponding roadway contexts and accounting for their effects on speeds and crashes. This paper investigates the speed-crash relationship for city streets by jointly modeling speed, roadway characteristics, and crashes using a path analysis approach that has been recently introduced into safety analysis while incorporating a wide range of roadway and traffic related variables and additional speed measures. The results from the coherent path analysis identified multiple speed measures of interest that have a statistically significant association with crashes as well as having intuitive and useful interpretation. The results also supported a positive relationship between speed variability and crash occurrence (i.e., larger spread/variability in operational speed is associated with more crashes).

Investigation on the wrong way driving crash patterns using multiple correspondence analysis.

Wrong way driving (WWD) has been a constant traffic safety problem in certain types of roads. Although these crashes are not large in numbers, the outcomes are usually fatalities or severe injuries. Past studies on WWD crashes used either descriptive statistics or logistic regression to determine the impact of key contributing factors. In conventional statistics, failure to control the impact of all contributing variables on the probability of WWD crashes generates bias due to the rareness of these types of crashes. Distribution free methods, such as multiple correspondence analysis (MCA), overcome this issue, as there is no need of prior assumptions. This study used five years (2010-2014) of WWD crashes in Louisiana to determine the key associations between the contribution factors by using MCA. The findings showed that MCA helps in presenting a proximity map of the variable categories in a low dimensional plane. The outcomes of this study are sixteen significant clusters that include variable categories like determined several key factors like different locality types, roadways at dark with no lighting at night, roadways with no physical separations, roadways with higher posted speed, roadways with inadequate signage and markings, and older drivers. This study contains safety recommendations on targeted countermeasures to avoid different associated scenarios in WWD crashes. The findings will be helpful to the authorities to implement appropriate countermeasures.