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A Data-Driven Framework of Crash Scenario Typology Development for Child Vulnerable Road Users in the U.S.
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 11, 2023 by SAE International in United States
Annotation ability available
Motor vehicle crashes involving child Vulnerable Road Users (VRUs) remain a critical public health concern in the United States. While previous studies successfully utilized the crash scenario typology to examine traffic crashes, these studies focus on all types of motor vehicle crashes thus the method might not apply to VRU crashes. Therefore, to better understand the context and causes of child VRU crashes on the U.S. road, this paper proposes a multi-step framework to define crash scenario typology based on the Fatality Analysis Reporting System (FARS) and the Crash Report Sampling System (CRSS). A comprehensive examination of the data elements in FARS and CRSS was first conducted to determine elements that could facilitate crash scenario identification from a systematic perspective. A follow-up context description depicts the typical behavioral, environmental, and vehicular conditions associated with an identified crash scenario. In addition, hypothesis tests are used to reveal over-represented element conditions that separate a specific crash scenario from others. A case study is given on fatal crashes with a single vehicle and a single-child pedestrian to demonstrate the proposed framework. Insights are obtained on the similarities and more interestingly the differences in the context among crash scenarios. For example, compared to crashes noted with “Non-Motorist Contributing Factors” (actions and/or circumstances that may have contributed to the crash) for child pedestrians, crashes without the type of factors noted were associated with a significantly higher proportion of driver violations charged and/or driving under the influence. When involved in a crash, child pedestrians who failed to yield the right-of-way were significantly more likely to be young teens (13-14 years) while those in the roadway improperly were more likely playing toddlers (1-3 years). We expect the work to serve as a fundamental and practical tool for further examination of crash context and causation, especially those involving children, and to improve their safety traveling on the road.
CitationGuo, H., Wang, Z., Sherony, R., and Bao, S., "A Data-Driven Framework of Crash Scenario Typology Development for Child Vulnerable Road Users in the U.S.," SAE Technical Paper 2023-01-0787, 2023, https://doi.org/10.4271/2023-01-0787.
- Koopmans , J.M. , Friedman , L. , Kwon , S. , and Sheehan , K. Urban Crash-Related Child Pedestrian Injury Incidence and Characteristics Associated with Injury Severity Accident Analysis & Prevention 77 2015 127 136
- Hagel , B.E. , Romanow , N.T. , Enns , N. , Williamson , J. et al. Severe Bicycling Injury Risk Factors in Children and Adolescents: A Case–Control Study Accident Analysis & Prevention 78 2015 165 172
- Barton , B.K. and Schwebel , D.C. The Roles of Age, Gender, Inhibitory Control, and Parental Supervision in children’s Pedestrian Safety Journal of Pediatric Psychology 32 5 2007 517 526
- Sandels , S. Young Children in Traffic British Journal of Educational Psychology 40 1970 111 116
- Schieber , R.A. and Thompson , N. Developmental Risk Factors for Childhood Pedestrian Injuries Injury Prevention 2 3 1996 228
- O’Neal , E.E. , Jiang , Y. , Franzen , L.J. , Rahimian , P. et al. Changes in Perception–Action Tuning over Long Time Scales: How Children and Adults Perceive and Act on Dynamic Affordances when Crossing Roads Journal of Experimental Psychology: Human Perception and Performance 44 1 2018 18
- Charron , C. and Festoc , A. Do Child Pedestrians Deliberately Take Risks when they Are in a Hurry? An Experimental Study on a Simulator Transportation Research Part F: Traffic Psychology and Behaviour 15 6 2012 635 643
- Siman-Tov , M. , Jaffe , D.H. , Peleg , K. , Group , I.T. et al. Bicycle Injuries: A Matter of Mechanism and Age Accident Analysis & Prevention 44 1 2012 135 139
- Boufous , S. , Rome , L.D. , Senserrick , T. , and Ivers , R. Cycling Crashes in Children, Adolescents, and Adults—A Comparative Analysis Traffic Injury Prevention 12 3 2011 244 250
- Liu , J. , Hainen , A. , Li , X. , Nie , Q. et al. Pedestrian Injury Severity in Motor Vehicle Crashes: An Integrated Spatio-Temporal Modeling Approach Accident Analysis & Prevention 132 2019 105272
- Wu , S. , Yuan , Q. , Yan , Z. , and Xu , Q. Analyzing Accident Injury Severity Via an Extreme Gradient Boosting (Xgboost) Model Journal of Advanced Transportation 2021 2021
- Asgarzadeh , M. , Fischer , D. , Verma , S.K. , Courtney , T.K. et al. The Impact of Weather, Road Surface, Time-of-Day, and Light Conditions on Severity of Bicycle-Motor Vehicle Crash Injuries American Journal of Industrial Medicine 61 7 2018 556 565
- Rahman , M.S. , Abdel-Aty , M. , Hasan , S. , and Cai , Q. Applying Machine Learning Approaches to Analyze the Vulnerable Road-Users’ Crashes at Statewide Traffic Analysis Zones Journal of Safety Research 70 2019 275 288
- Liu , S. , Lin , Z. , and Fan , W. Investigating Contributing Factors to Injury Severity Levels in Crashes Involving Pedestrians and Cyclists Using Latent Class Clustering Analysis and Mixed Logit Models Journal of Transportation Safety & Security 14 10 2022 1674 1701
- Rifaat , S.M. and Chin , H.C. Accident Severity Analysis Using Ordered Probit Model Journal of Advanced Transportation 41 1 2007 91 114
- Cerwick , D.M. , Gkritza , K. , Shaheed , M.S. , and Hans , Z. A Comparison of the Mixed Logit and Latent Class Methods for Crash Severity Analysis Analytic Methods in Accident Research 3 2014 11 27
- Eluru N. , Bhat C.R. , and Hensher D.A. Accident Analysis & Prevention
- Ammar , D. , Xu , Y. , Jia , B. , and Bao , S. Examination of Recent Pedestrian Safety Patterns at Intersections through Crash Data Analysis Transportation Research Record 2022 03611981221095513
- Zajac , S.S. and Ivan , J.N. Factors Influencing Injury Severity of Motor Vehicle–Crossing Pedestrian Crashes in Rural Connecticut Accident Analysis & Prevention 35 3 2003 369 379
- Najm , W.G. , Smith , J.D. , Yanagisawa , M. , et al. 2007
- Liu , Q. , Wang , X. , Wu , X. , Glaser , Y. et al. Crash Comparison of Autonomous and Conventional Vehicles Using Pre-Crash Scenario Typology Accident Analysis & Prevention 159 2021 106281
- McDonald , C.C. , Curry , A.E. , Kandadai , V. , Sommers , M.S. et al. Comparison of Teen and Adult Driver Crash Scenarios in a Nationally Representative Sample of Serious Crashes Accident Analysis & Prevention 72 2014 302 308
- Li , Y. , Karim , M.M. , Qin , R. , Sun , Z. et al. Crash Report Data Analysis for Creating Scenario-Wise, Spatio-Temporal Attention Guidance to Support Computer Vision-Based Perception of Fatal Crash Risks Accident Analysis & Prevention 151 2021 105962
- Yue , L. , Abdel-Aty , M. , Wu , Y. , Zheng , O. et al. In-Depth Approach for Identifying Crash Causation Patterns and its Implications for Pedestrian Crash Prevention Journal of Safety Research 73 2020 119 132
- March 2022
- March 2022
- McDonald , J.H. Handbook of Biological Statistics 2 Baltimore, MD Sparky House Publishing 2009
- Ha , H.-H. and Thill , J.-C. Analysis of Traffic Hazard Intensity: A Spatial Epidemiology Case Study of Urban Pedestrians Computers, Environment and Urban Systems 35 3 2011 230 240
- Clifton , K.J. and Kreamer-Fults , K. An Examination of the Environmental Attributes Associated with Pedestrian–Vehicular Crashes near Public Schools Accident Analysis & Prevention 39 4 2007 708 715
- Hwang , J. , Joh , K. , and Woo , A. Social Inequalities in Child Pedestrian Traffic Injuries: Differences in Neighborhood Built Environments near Schools in Austin, Tx, Usa Journal of Transport & Health 6 2017 40 49
- Morrison , C. , Olson , T. , McNickle , A.G. , Fraser , D.R. et al. Higher Risk of Auto Versus Pedestrian Crashes in School-Age Children on School Days Journal of Trauma and Acute Care Surgery 93 1 2022 130 134
- Ammar , D. , Li , M. , Guo , H. , Yu , B. , et al.