Off-campus South Dakota State University users: To download campus access theses, please use the following link to log into our proxy server with your South Dakota State University ID and password.

Non-South Dakota State University users: Please talk to your librarian about requesting this thesis through interlibrary loan.

Document Type

Thesis - University Access Only

Award Date


Degree Name

Master of Science (MS)

Department / School

Civil and Environmental Engineering

First Advisor

Xiao Qin


Truck safety has been of great interest to transportation officials, engineers, and researchers for many years because of the amount of freight transported by truck, the safety impact of trucks in traffic, and trucks’ invaluable contribution to the country’s economic growth. According to the National Highway Traffic Safety Administration (NHTSA), over 400,000 truck-related crashes occurred in 2009 with approximately 7,800 of those are fatal. Compared to the extensive studies conducted on freeway truck safety, the research on arterial streets is considerably disproportionate. Connecting between traffic generators, arterial streets are key links for door-to-door deliveries. It is urgent to study and evaluate truck safety impact on arterial streets in response to the continued strong growth of truck traffic. This study closes the gap by providing a comprehensive analysis of truck-related crashes occurred on arterial roads. Truck related crashes are expected to be reduced through the careful planning of the location, design and operations of driveways, median openings, intersections and street sections. By collecting extensive roadway geometric characteristics, pavement conditions, traffic data on selected arterial corridors heavily traveled by truck, truck crash frequency and severity contributing factors have been identified using the negative binomial model and multinomial logit (MNL) model, respectively. In particular,tremendous effort has been made to collect the access related information manually through Google earth and Google map. Statistical models have been tested with different combinations of datasets, e.g. with and without access parameters. Without the access related variables, truck miles traveled, annual average daily traffic (AADT), signal density, shoulder width, pavement service index (PSI) and its standard deviation are statistically significant factors for predicting the crash frequency. After incorporating newly collected access data, commercial driveway design related variables exhibited statistical significance while the previously significant variables such as AADT, PSI and its standard deviation were no longer statistically significant. This noticeable change of the statistical models composed of different variables warns us that the spurious relationship may be formed if a causal relationship does not exist. The engineering judgment and reasoning through improved data collection can help to distinguish statistical artifacts from meaningful statistical correlations. For the crash severity prediction MNL identified twelve contributing factors such as posted speed limit, lane width, number of lanes, pavement condition index, undivided roadway portion and the variables that also affect crash frequency. Subsequently, the arterial corridors safety performance measured via a truck crash severity index (CSI) as a function of crash frequency and injury severity has been established. It is anticipated that the findings in the study will not only benefit state and local agencies in planning, designing, and managing a safe arterial corridor for trucks and other motorists, but also help motor carriers to optimize their routes from the safety perspective.

Library of Congress Subject Headings

Truck accidents
Trucks---Safety measures


Includes bibliographical references (pages 88-95)



Number of Pages



South Dakota State University


In Copyright - Educational Use Permitted