TY - JOUR
T1 - Safety effectiveness of autonomous vehicles and connected autonomous vehicles in reducing pedestrian crashes
AU - Susilawati, Susilawati
AU - Wong, Wei Jie
AU - Pang, Zhao Jian
N1 - Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the Ministry of Education Malaysia (MOHE) under the Fundamental Research Grant Scheme (FRGS) (Project code FRGS/1/2019/TK01/MUSM/ 03/1).
Publisher Copyright:
© National Academy of Sciences: Transportation Research Board 2022.
PY - 2023/2
Y1 - 2023/2
N2 - This research aims to study the safety effectiveness of autonomous vehicles (AVs) and connected autonomous vehicles (CAVs) in reducing pedestrian crashes in various scenarios. The proposed methodology involves (1) identifying factors that contribute to pedestrian crashes, (2) developing crash-frequency models to predict the pedestrian crash and identifying the model that performs the best, (3) identifying the AV and CAV technologies that can minimize and remove those identified factors, and (4) assessing the effectiveness of AV and CAV technologies in reducing pedestrian crashes for various road classifications. Using crash data obtained from San Francisco Transportation Injury Mapping System (TIMS) for 2016 to 2020, a two-level Bayesian Poisson lognormal (TLBPL) model is developed to assess the effectiveness of AVs and CAVs in reducing pedestrian crashes. The outcomes of the TLBPL model suggest that weather, lighting, and road classifications tend to influence more vehicle–pedestrian crashes in all road classifications. The results of TLBPL indicate that driver faults related to prediction ability contribute more to pedestrian crashes for all road classifications, while driver fault related to sensing (perception) on urban arterials is the factor contributing most to pedestrian crashes. This paper provides a framework for researchers and engineers to evaluate AVs’ and CAVs’ safety effectiveness by considering crash contributing factors and road classifications.
AB - This research aims to study the safety effectiveness of autonomous vehicles (AVs) and connected autonomous vehicles (CAVs) in reducing pedestrian crashes in various scenarios. The proposed methodology involves (1) identifying factors that contribute to pedestrian crashes, (2) developing crash-frequency models to predict the pedestrian crash and identifying the model that performs the best, (3) identifying the AV and CAV technologies that can minimize and remove those identified factors, and (4) assessing the effectiveness of AV and CAV technologies in reducing pedestrian crashes for various road classifications. Using crash data obtained from San Francisco Transportation Injury Mapping System (TIMS) for 2016 to 2020, a two-level Bayesian Poisson lognormal (TLBPL) model is developed to assess the effectiveness of AVs and CAVs in reducing pedestrian crashes. The outcomes of the TLBPL model suggest that weather, lighting, and road classifications tend to influence more vehicle–pedestrian crashes in all road classifications. The results of TLBPL indicate that driver faults related to prediction ability contribute more to pedestrian crashes for all road classifications, while driver fault related to sensing (perception) on urban arterials is the factor contributing most to pedestrian crashes. This paper provides a framework for researchers and engineers to evaluate AVs’ and CAVs’ safety effectiveness by considering crash contributing factors and road classifications.
KW - Bayesian methods
KW - modeling and forecasting
KW - safety
KW - safety effects of connected/automated vehicles
KW - safety performance and analysis
KW - transportation safety management systems
UR - http://www.scopus.com/inward/record.url?scp=85145230100&partnerID=8YFLogxK
U2 - 10.1177/03611981221108984
DO - 10.1177/03611981221108984
M3 - Article
AN - SCOPUS:85145230100
SN - 0361-1981
VL - 2677
SP - 1605
EP - 1618
JO - Transportation Research Record
JF - Transportation Research Record
IS - 2
ER -
