Pedestrian Throw Distance Prediction from Vehicle Damage Intensity

  • Nenad Saulić Faculty of Technical Sciences, University of Novi Sad
  • Zoran Papić Faculty of Technical Sciences, University of Novi Sad
  • Zoran Ovcin Faculty of Technical Sciences, University of Novi Sad
Keywords: pedestrian traffic accidents, throw distance, vehicle damage, vehicle speed

Abstract

One of the main points to be addressed when analysing vehicle-pedestrian collisions is the vehicle impact speed. If the traffic accident is not recorded on camera, and there are no skid marks nor tachograph in the vehicle, the parameter is determined on the basis of empirical models. All empirical models for ascertaining vehicle speed are based on the pedestrian throw distance, which is not always known because of an unidentified vehicle-pedestrian collision point or the final rest position of the pedestrian after collision. This paper shows a description of a vehicle damage recorded in an ordinal scale and determines the pedestrian throw distance prediction model from the vehicle damage established in such a way. If the accident scene is documented by photographs, the damage can be classified, and by applying a validated model, the pedestrian throw distance envisaged. Then, by applying an empirical model, one can determine the speed of the vehicle at the time of collision with a pedestrian. Two databases were formed during the research. The first is based on real-life traffic accidents (expert witnessing of the professors from the Faculty of Technical Sciences). The second is based on traffic accident simulations as part of PC Crash software package.

References

Gábor M. Gépjárműszakértés. Budapest: Maróti Könyvkereskedés és Könyvkiadó Kft.; 2004.

Limpert R. Brake Design and Safety. United States of America: Society 01’ Autonlohve Engineers; 1999.

Searle JA, Searle A. The Trajectories of Pedestrians, Motorcycles, Motorcyclists, etc. Following a Road Accident. SAE Technical Paper 831622; 1983; p. 277–80. Available from: doi:10.4271/831622

Stcherbatcheff G, Tarriere C, Duclos P, Fayon A. Simulation of Collisions Between Pedestrians and Vehicles Using Adult and Child Dummies. SAE Technical Paper 751167; 1975. p. 33. Available from: doi:10.4271/751167

Simms CK, Wood DP. Confidence limits for impact speed estimation from pedestrian projection distance. International Journal of Crashworthiness. 2004;9(2): 219-28.

Wood DP, Simms CK, Walsh DG. Vehicle – pedestrian collisions : validated models for pedestrian impact and projection. Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering. 2005;219(2): 183-195.

Fugger TF, Randles BC, Wobrock JL, Eubanks JJ. Pedestrian Throw Kinematics in Forward Projection Collisions. SAE 2002 World Congress & Exhibition. Available from: doi:10.4271/2002-01-0019

Han I, Brach RM. Throw Model for Frontal Pedestrian Collisions. SAE Technical Paper 2001-01-0898; 2001; p. 16.

Batista M. A simple throw model for frontal vehicle-pedestrian collisions. Promet – Traffic&Transportation. 2008;20(6): 357-68.

Eubanks JJ, Haight WR. Pedestrian involved traffic collision reconstruction methodology. SAE Technical Paper 921591; 1992; p. 37-49.

Cheng Y, Wong K, Tam C, Tam Y, Wong T, Tao C. Validation of pedestrian throw equations by video footage of real life pedestrian/vehicle collisions. Forensic Science International. 2015;257: 409-12.

Wood DP. Application of a pedestrian impact model to the determination of impact speed. SAE Technical Paper 910814; 1991.

Kostić S. Tehnike bezbednosti i kontrole saobraćaja. Faculty of Technical Sciences, University of Novi Sad; 2009. Serbian.

Burg H, Moser A. Handbuch Verkehrsunfall-rekonstruktion. Wiesbaden, Springer Science+Business Media; 2007.

Soica A, Tarelescu S. Impact phase in frontal vehicle-pedestrian collisions. International Journal of Automotive Technology. 2016;17(3): 387-97. Available from: doi:10.1007/s12239-016-0040-y

Zou T, Yu Z, Cai M, Liu J. Analysis and application of relationship between post-braking-distance and throw distance in vehicle-pedestrian accident reconstruction. Forensic Science International. 2011;207(1–3): 135-44.

Lesko MM, Woodford M, White L, O'Brien SJ, Childs C, Lecky FE. Using Abbreviated Injury Scale (AIS) codes to classify Computed Tomography (CT) features in the Marshall System. Medical Research Methodology. 2010;10: 72. Available from: doi:10.1186/1471-2288-10-72.

Glynn C, Wood DP. Pedestrian Speed from Vehicle Damage. 24 EVU Conference, Edinburgh; 2015.

PC-Crash – A Simulation program for Vehicle Accidents, Operating Manual. Version 12.0. Linz, Austria; 2019.

Ratner B. Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data. Chapman & Hall/CRC; 2003.

Kutner MH, Nachtsheim CJ, Neter J, Li W. Applied Linear Statistical Models. McGraw-Hill/Irwin; 2005. 1415 p.

Barzeley M, Lacy GW. Scientific Automobile Accident Reconstruction. New York, USA: Matthew Bender & Company Incorporated; 1978.

Bhalla K, Montazemi P, Crandall J, Yang J, Liu X, Dokko Y, et al. Vehicle impact velocity prediction from pedestrian throw distance: Trade-offs between throw formulae, crash simulators, and detailed multi-body modeling. Proceedings of the International IRCOBI Conference on the Biomechanics of Impacts, Munich, Germany; 2002.

Portal RJ, Dias JM. Pedestrian Reconstruction Tools Applied to Pedestrian Accidents in Portugal. Proceedings of the 3rd International Symposium on ESAR "Expert Symposium on Accident Research", Hannover, Germany; 2009; p. 304-14.

Hoxha G, Shala A, Likaj R. Pedestrian crash model for vehicle. International Journal of Civil Engineering and Technology. 2017;8(9): 1093-9.

Published
2020-05-10
How to Cite
1.
Saulić N, Papić Z, Ovcin Z. Pedestrian Throw Distance Prediction from Vehicle Damage Intensity. Promet [Internet]. 2020May10 [cited 2024Apr.23];32(3):371-82. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/3312
Section
Articles