No abstract

No abstract

No abstract

Despite many other methods that identify only the contributing factors, this method can identify possible associations between various contributing factors. This is an inherent advantage of the MCA method, which can provide a major opportunity for state departments of transportation (DOTs) to select safety countermeasures that are associated with multiple safety benefits.

No abstract

During the five-year period between 2010 and 2014, there were 24,178 pedestrian crashes in Illinois. Approximately 4.39% of these pedestrian crashes occurred in rural areas; and approximately 40% of the crashes resulted in a severe injury or a fatality. Thus, pedestrian safety problems exist in rural locales, and the factors contributing to these problems need to be investigated. The goal of this study is to answer the question: “Which variable categories, when acting together, contribute more to the occurrence of pedestrian crashes in rural areas?” Crashes are random events stemming from the convergence of various factors. However, traditional statistical tools can only make pairwise comparisons of dependent and independent variables. Therefore, it is necessary to apply an analytical tool that can identify complex underlying structures in crash data and spot associations among variable categories that contribute to crash occurrence. The multiple correspondence analysis (MCA) method, which is used in this study, can do just that. According to the obtained results, categories of variables such as roadway functional class, the number of lanes, lighting conditions, weather conditions, traffic control devices, driver condition, and pedestrian condition were proved to contribute to pedestrian crashes in rural Illinois.

No abstract

No abstract