TY - JOUR
T1 - Elderly Pedestrian Fatal Crash-Related Contributing Factors
T2 - Applying Empirical Bayes Geometric Mean Method
AU - Das, Subasish
AU - Bibeka, Apoorba
AU - Sun, Xiaoduan
AU - Zhou, Hongmin “Tracy”
AU - Jalayer, Mohammad
N1 - Publisher Copyright:
© National Academy of Sciences: Transportation Research Board 2019.
PY - 2019/8/1
Y1 - 2019/8/1
N2 - Recent statistics show that around 20% of all pedestrian fatalities (1,002 out of 5,376) in 2015 were pedestrians over the age of 65. There is a need to identify issues associated with elderly pedestrian crashes to develop effective countermeasures. This study aimed to determine the key associations between contributing factors of elderly pedestrian crashes. The authors analyzed three years (2014 to 2016) of elderly pedestrian fatal crashes from the Fatality Analysis Reporting System in the United States by using empirical Bayes (EB) data mining. The findings of this study revealed several association patterns with high crash potential for elderly pedestrians that include backing vehicle-related crashes for female pedestrians (especially those aged 79 and above), segment-related crashes at night for 65 to 69 year-old male pedestrians, crossing an expressway at night for male pedestrians, especially the 65 to 69 year group, failure to yield while crossing at intersections, and crashes occurring in the dark with poor street lighting. The findings of this study could help authorities determine effective countermeasures for this group of vulnerable road users.
AB - Recent statistics show that around 20% of all pedestrian fatalities (1,002 out of 5,376) in 2015 were pedestrians over the age of 65. There is a need to identify issues associated with elderly pedestrian crashes to develop effective countermeasures. This study aimed to determine the key associations between contributing factors of elderly pedestrian crashes. The authors analyzed three years (2014 to 2016) of elderly pedestrian fatal crashes from the Fatality Analysis Reporting System in the United States by using empirical Bayes (EB) data mining. The findings of this study revealed several association patterns with high crash potential for elderly pedestrians that include backing vehicle-related crashes for female pedestrians (especially those aged 79 and above), segment-related crashes at night for 65 to 69 year-old male pedestrians, crossing an expressway at night for male pedestrians, especially the 65 to 69 year group, failure to yield while crossing at intersections, and crashes occurring in the dark with poor street lighting. The findings of this study could help authorities determine effective countermeasures for this group of vulnerable road users.
UR - http://www.scopus.com/inward/record.url?scp=85064655142&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064655142&partnerID=8YFLogxK
U2 - 10.1177/0361198119841570
DO - 10.1177/0361198119841570
M3 - Article
AN - SCOPUS:85064655142
SN - 0361-1981
VL - 2673
SP - 254
EP - 263
JO - Transportation Research Record
JF - Transportation Research Record
IS - 8
ER -