The Severity of Large Truck Accidents PDF Download
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Author: James H. Hedlund
Publisher:
ISBN:
Category :
Languages : en
Pages : 44
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Author: James H. Hedlund
Publisher:
ISBN:
Category :
Languages : en
Pages : 44
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Author: Marc Starnes
Publisher:
ISBN:
Category :
Languages : en
Pages : 60
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Author:
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ISBN:
Category :
Languages : en
Pages :
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Author: Siddhartha Kotikalapudi
Publisher:
ISBN:
Category :
Languages : en
Pages :
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In order to improve safety of the overall surface transportation system, each of the critical areas needs to be addressed separately with more focused attention. Statistics clearly show that large-truck crashes contribute significantly to an increased percentage of high-severity crashes. It is therefore important for the highway safety community to identify characteristics and contributory causes related to large-truck crashes. During the first phase of this study, fatal crash data from the Fatality Analysis Reporting System (FARS) database were studied to achieve that objective. In this second phase, truck-crashes of all severity levels were analyzed with the intention of understanding characteristics and contributory causes, and identifying factors contributing to increased severity of truck-crashes, which could not be achieved by analyzing fatal crashes alone. Various statistical methodologies such as cross-classification analysis and severity models were developed using Kansas crash data. Various driver-, road-, environment- and vehicle- related characteristics were identified and contributory causes were analyzed. From the cross-classification analysis, severity of truck-crashes was found to be related with variables such as road surface (type, character and condition), accident class, collision type, driver- and environment-related contributory causes, traffic-control type, truck-maneuver, crash location, speed limit, light and weather conditions, time of day, functional class, lane class, and Average Annual Daily Traffic (AADT). Other variables such as age of truck driver, day of the week, gender of truck-driver, pedestrian- and truck-related contributory causes were found to have no relationship with crash severity of large trucks. Furthermore, driver-related contributory causes were found to be more common than any other type of contributory cause for the occurrence of truck-crashes. Failing to give time and attention, being too fast for existing conditions, and failing to yield right of way were the most dominant truck-driver-related contributory causes, among many others. Through the severity modeling, factors such as truck-driver-related contributory cause, accident class, manner of collision, truck-driver under the influence of alcohol, truck maneuver, traffic control device, surface condition, truck-driver being too fast for existing conditions, truck-driver being trapped, damage to the truck, light conditions, etc. were found to be significantly related with increased severity of truck-crashes. Truck-driver being trapped had the highest odds of contributing to a more severe crash with a value of 82.81 followed by the collision resulting in damage to the truck, which had 3.05 times higher odds of increasing the severity of truck-crashes. Truck-driver under the influence of alcohol had 2.66 times higher odds of contributing to a more severe crash. Besides traditional practices like providing adequate traffic signs, ensuring proper lane markings, provision of rumble strips and elevated medians, use of technology to develop and implement intelligent countermeasures were recommended. These include Automated Truck Rollover Warning System to mitigate truck-crashes involving rollovers, Lane Drift Warning Systems (LDWS) to prevent run-off-road collisions, Speed Limiters (SLs) to control the speed of the truck, connecting vehicle technologies like Vehicle-to-Vehicle (V2V) integration system to prevent head-on collisions etc., among many others. Proper development and implementation of these countermeasures in a cost effective manner will help mitigate the number and severity of truck-crashes, thereby improving the overall safety of the transportation system.
Author: S. M. Mamun-Ar-Rashid
Publisher:
ISBN:
Category : Automobile driving
Languages : en
Pages :
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Author: National Academies of Sciences, Engineering, and Medicine
Publisher: National Academies Press
ISBN: 0309392527
Category : Transportation
Languages : en
Pages : 273
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Book Description
There are approximately 4,000 fatalities in crashes involving trucks and buses in the United States each year. Though estimates are wide-ranging, possibly 10 to 20 percent of these crashes might have involved fatigued drivers. The stresses associated with their particular jobs (irregular schedules, etc.) and the lifestyle that many truck and bus drivers lead, puts them at substantial risk for insufficient sleep and for developing short- and long-term health problems. Commercial Motor Vehicle Driver Fatigue, Long-Term Health and Highway Safety assesses the state of knowledge about the relationship of such factors as hours of driving, hours on duty, and periods of rest to the fatigue experienced by truck and bus drivers while driving and the implications for the safe operation of their vehicles. This report evaluates the relationship of these factors to drivers' health over the longer term, and identifies improvements in data and research methods that can lead to better understanding in both areas.
Author:
Publisher:
ISBN:
Category : Freight and freightage
Languages : en
Pages : 58
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Author: National Highway Traffic Safety Administration
Publisher: CreateSpace
ISBN: 9781492398738
Category : Transportation
Languages : en
Pages : 56
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The Large-Truck Crash Causation Study (LTCCS) is a data collection project conducted by the National Highway Traffic Safety Administration (NHTSA) and the Federal Motor Carrier Safety Administration (FMCSA) of the United States Department of Transportation (USDOT). NHTSA's National Center for Statistics and Analysis (NCSA) worked together with FMCSA to develop the LTCCS, which was conducted within the National Automotive Sampling System (NASS) that NCSA operates. The tables in this report were created through the use of the data collected in the LTCCS. While the LTCCS collected data on approximately 1,000 variables, the tables presented in this report comprise only a sample of these variables. The complete LTCCS variable database can be used jointly to examine a large number of issues surrounding large-truck crashes. One section in the report focuses on “crash-level” variables, which provide counts of crashes that occurred under certain characteristics (i.e., crash counts stratified according to how many vehicles were in the crash). The next section includes tables that are presented at the “vehicle level.” These tables thus provide counts of the number of vehicles involved in certain types of crashes (i.e., vehicle counts that have been stratified by the injury severity of the person most severely injured in each vehicle). The tables in the following section are presented at the “driver level.” These tables display counts of drivers that were involved in certain crash scenarios (i.e., the number of drivers involved in the crashes, stratified by the age of the driver). The appendix includes tables and computer programs for calculating standard errors and confidence intervals using LTCCS data.
Author:
Publisher:
ISBN:
Category : Traffic accidents
Languages : en
Pages : 4
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Author:
Publisher:
ISBN:
Category : Traffic accidents
Languages : en
Pages : 70
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