In-depth approach for identifying crash causation patterns and its implications for pedestrian crash prevention. (June 2020)

• Record Type: Journal Article
• Authors: Yue, Lishengsa; Abdel-Aty, Mohamed; Wu, Yina; Zheng, Ou; Yuan, Jinghui
• Is Part Of: Journal of safety research. Volume 73(2020)
• Page Start: 119
• Page End: 132
• Publication Date: 2020-06
• Subjects: Pedestrian crashes -- Crash causation pattern -- Crash contributing factors -- Driving Reliability and Error Analysis Method; Industrial safety -- Periodicals; Accidents -- Prevention -- Periodicals; Safety -- Periodicals; Accidents, Occupational -- Periodicals; Sécurité du travail -- Périodiques; Accidents -- Prévention -- Périodiques; Accidents -- Prevention; Industrial safety; Periodicals; 363.1
• DOI: 10.1016/j.jsr.2020.02.020
• Languages: English
• ISSNs: 0022-4375

Abstract

Highlights: Pedestrian crashes occur due to different crash causation patterns. Driving Reliability and Error Analysis Method was used to identify causation patterns. The pattern related to distracted driving contributed to 27% of total crashes. The pattern of unexpected change of pedestrian trajectory contributed 24%. Suggestions for roadway facility design and other issues were proposed. Abstract: Introduction: A pedestrian crash occurs due to a series of contributing factors taking effect in an antecedent-consequent order. One specific type of antecedent-consequent order is called a crash causation pattern. Understanding crash causation patterns is important for clarifying the complicated growth of a pedestrian crash, which ultimately helps recommend corresponding countermeasures. However, previous studies lack an in-depth investigation of pedestrian crash cases, and are insufficient to propose a representative picture of causation patterns. Method: In this study, pedestrian crash causation patterns were discerned by using the Driving Reliability and Error Analysis Method (DREAM). One hundred and forty-two pedestrian crashes were investigated, and five pedestrian pre-crash scenarios were extracted. Then, the crash causation patterns in each pre-crash scenario were analyzed; and finally, six distinct patterns were identified. Accordingly, 17 typical situations corresponding to these causation patterns were specified as well. Results: Among these patterns, the pattern related to distracted driving and the pattern related to an unexpected change of pedestrian trajectory contributed to a large portion of the total crashes (i.e., 27% and 24%, respectively). Other patterns also played an important role in inducing a pedestrian crash; these patterns include the pattern related to an obstructed line of sight caused by outside objects (9%), the pattern that involves reduced visibility (13%), and the pattern related to an improper estimation of the gap distance between the vehicle and the pedestrian (10%). The results further demonstrated the inter-heterogeneity of a crash causation pattern, as well as the intra-heterogeneity of pattern features between different pedestrian pre-crash scenarios. Conclusions and practical applications: Essentially, a crash causation pattern might involve different contributing factors by nature or dependent on specific scenarios. Finally, this study proposed suggestions for roadway facility design, roadway safety education and pedestrian crash prevention system development.