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Author: Xiaoyu Zhu (Writer on transportation)
Publisher:
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Category :
Languages : en
Pages :
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The results indicate that the variables which are not significant in ordered probit model may have impact on the injury severity. For different roles (truck occupant, car driver and car passenger), the significant driver behavior variables are also different. In summary, the advanced and flexible methodologies for occupant level injury severity study are developed and compared in this dissertation. The results and implications are useful from the standpoints of traveler, transportation engineer and policy maker.
Author: Xiaoyu Zhu (Writer on transportation)
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
The results indicate that the variables which are not significant in ordered probit model may have impact on the injury severity. For different roles (truck occupant, car driver and car passenger), the significant driver behavior variables are also different. In summary, the advanced and flexible methodologies for occupant level injury severity study are developed and compared in this dissertation. The results and implications are useful from the standpoints of traveler, transportation engineer and policy maker.
Author: Marc Starnes
Publisher:
ISBN:
Category :
Languages : en
Pages : 60
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Author:
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Category : Traffic accidents
Languages : en
Pages : 4
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Author: Jasmine Pahukula
Publisher:
ISBN:
Category : Crash injuries
Languages : en
Pages : 84
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Book Description
Large truck-involved crashes have a significant impact on both the economy and society. They are associated with high injury severities, high crash costs and contribute to congestion in urban areas. Past studies have investigated the contributing factors of large truck-involved crashes, however a study isolating the spatial and temporal effects is lacking. This thesis aims to bridge that gap as well as provide practical applications to improve safety from a large truck perspective through two new frameworks. This thesis contains two standalone documents, each detailing the spatial and temporal transferability framework, separately. These frameworks provide additional information that can be utilized in the development of planning tools to ultimately improve safety. Random parameters logit models (i.e. mixed logit models) were utilized to help identify the contributing factors of large truck-involved crashes. One advantage of the mixed logit model is that it can account for the unobserved heterogeneity in the model which relaxes the independence of irrelevant alternatives (IIA) property. A series of log likelihood ratio tests were utilized to determine if transferability, spatial or temporal, was warranted. The first document details the spatial transferability framework which is demonstrated through a case study on large truck-involved crashes in urban areas in Oregon and Texas. Strict regulations imposed on the trucking industry limits the variability of heavy-vehicle configurations and enhance the standards for truck drivers (as opposed to passenger vehicle drivers). Encouraging consistency between large trucks is one way to improve safety and has also lead to the investigation of commonalities between large truck-involved crashes in two spatially distributed regions. The results of the log-likelihood ratio tests indicate that spatial transferability is not warranted between Oregon and Texas. Key differences were non-driver or 'uncontrollable' characteristics (e.g. weather, light conditions and time of day) while driver related characteristics (e.g. gender, age and restraint use) had similar impacts. Since the major differences include non-driver characteristics, perhaps a regional model with similar 'uncontrollable' characteristics is warranted. The second document illustrates the temporal transferability framework which is applied to large truck-involved crashes in urban areas in Texas. Traffic patterns, light conditions and driver behavior vary throughout the day and consequently can have a varied impact on large truck-involved crashes. The results of the log likelihood ratio tests indicate that temporal transferability is warranted and the database was divided into five time periods to be analyzed separately. Traffic flow, light conditions, surface conditions, month and percentage of trucks on the road were among the significant differences between the crash factors of each time period. The two proposed transferability frameworks, spatial and temporal, provide new information that can be integrated into safety planning tools and more sharply guide decision-makers. For example, the results of this thesis can help to pinpoint temporal or spatial-related countermeasures. In addition the results of this thesis can help in the allocation of limited resources (i.e. help prioritize projects), minimize economic loss and help decision makers improve safety from a large truck perspective (e.g. modify trucking regulations). Finally, this thesis provides a foundation for future research. As indicated in Chapter 2, a future study to evaluate the feasibility of a regional large truck-involved crash model between neighboring regions and the development of a national crash data reporting standard are potential ideas for future research. Chapter 3 stressed the importance of time of day on large truck-involved crashes which can serve as the basis to study the safety and economic impacts of time of day shifts of truck freight movements to off-peak periods. In summary, this thesis involves original research that expands the literature and provides a new foundation to analyze large truck-involved crashes.
Author: S. M. Mamun-Ar-Rashid
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ISBN:
Category : Automobile driving
Languages : en
Pages :
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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:
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Category : Freight and freightage
Languages : en
Pages : 58
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Author: A. J. Khattak
Publisher:
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Category : Truck accidents
Languages : en
Pages : 16
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Author: J. P. Eicher
Publisher:
ISBN:
Category :
Languages : en
Pages : 226
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Author: James H. Hedlund
Publisher:
ISBN:
Category :
Languages : en
Pages : 44
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