Version 1
: Received: 5 October 2021 / Approved: 6 October 2021 / Online: 6 October 2021 (10:35:39 CEST)
How to cite:
Schindler, R.; Jänsch, M.; Bálint, A.; Johannsen, H. Exploring European Heavy Goods Vehicle Crashes Using a Three-Level Analysis of Crash Data. Preprints2021, 2021100102. https://doi.org/10.20944/preprints202110.0102.v1
Schindler, R.; Jänsch, M.; Bálint, A.; Johannsen, H. Exploring European Heavy Goods Vehicle Crashes Using a Three-Level Analysis of Crash Data. Preprints 2021, 2021100102. https://doi.org/10.20944/preprints202110.0102.v1
Schindler, R.; Jänsch, M.; Bálint, A.; Johannsen, H. Exploring European Heavy Goods Vehicle Crashes Using a Three-Level Analysis of Crash Data. Preprints2021, 2021100102. https://doi.org/10.20944/preprints202110.0102.v1
APA Style
Schindler, R., Jänsch, M., Bálint, A., & Johannsen, H. (2021). Exploring European Heavy Goods Vehicle Crashes Using a Three-Level Analysis of Crash Data. Preprints. https://doi.org/10.20944/preprints202110.0102.v1
Chicago/Turabian Style
Schindler, R., András Bálint and Heiko Johannsen. 2021 "Exploring European Heavy Goods Vehicle Crashes Using a Three-Level Analysis of Crash Data" Preprints. https://doi.org/10.20944/preprints202110.0102.v1
Abstract
This paper addresses crashes involving heavy goods vehicles (HGV) in Europe focusing on long-haul trucks weighing 16 tons or more (16t+). The identification of the most critical scenarios and their characteristics is based on a three-level analysis: general crash statistics from CARE addressing all HGVs, results about 16t+ trucks from national crash databases and a detailed study of in-depth crash data from GIDAS, including a crash causation analysis. Most European HGV crashes occur in clear weather, during daylight, on dry roads, outside city limits, and on non-highway roads. Three main scenarios for 16t+ trucks are characterized in-depth: (1) rear-end crashes in which the truck is the striking partner, (2) conflicts during right turn maneuvers of the truck and a cyclist riding alongside and (3) pedestrians crossing the road in front of the truck. Among truck-related crash causes, information admission failures (e.g. distraction) were the main causing factors in 72% of cases in scenario (1) while information access problems (e.g. blind spots) were present for 72% of cases in scenario (2) and 75% of cases in scenario (3). The results provide both a global overview and sufficient depth of analysis in the most relevant cases and thereby aid safety system development.
Keywords
long-haul truck; crash scenarios; GIDAS; CARE; crash causation; European national crash data
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
