01622546

Component

In 2011, 4,432 pedestrians were killed (14% of total traffic crash fatalities) and 69,000 pedestrians were injured in vehicle-pedestrian crashes in the United States. Particularly in Louisiana, Vehicle-pedestrian crashes have become a key concern because of the high percentage of fatalities in recent years. In 2012, pedestrians were accounted for 17% of all fatalities due to traffic crashes in Louisiana. Alcohol was involved in nearly 44% of these fatalities. Therefore, an extensive research on the pedestrian safety in Louisiana is called for. This research utilized the a priori algorithm of association mining technique to discover knowledge from the vehicle-pedestrian crash database. This paper establishes how to apply association rules mining to discover vehicle pedestrian crash patterns using eight years of Louisiana crash data (2004-2011). The results indicated that roadway lighting at night helped in alleviating pedestrian crash severity. In addition, a few groups of interest were identified from this study: male pedestrians’ greater propensity towards severe and fatal crashes, younger female drivers (15-24) being more crash-prone than other age groups, vulnerable impaired pedestrians even on roadways with lighting at night, middle-aged male pedestrians (35-54) being inclined towards crash occurrence, and dominance of single vehicle crashes. It is clear that data mining approaches help revealing pedestrian crash patterns that may not always follow the intuitive explanations. The findings of this study can be used by traffic safety professionals to develop better pedestrian crash countermeasures, as well as to design campaings to raise awareness and potentially address the groups of interests identified in the analysis.

This paper was sponsored by TRB committee ANB20 Standing Committee on Safety Data, Analysis and Evaluation.

01618707

17-01166

English

0000-0002-8431-9304

19p

2017

Transportation Research Board 96th Annual Meeting

Digital/other

Figures; References; Tables

Countermeasures; Data analysis; Data mining; Pattern recognition systems; Pedestrian safety; Pedestrian vehicle crashes

Louisiana

Data and Information Technology; Pedestrians and Bicyclists; Safety and Human Factors

Transportation Research Board Annual Meeting 2017 Paper #17-01166

TRIS, TRB, ATRI

Dec 8 2016 10:20AM