1. NETWORK-LEVEL VEHICLE CRASH PREDICTIONS INCOPORATING TIME-
DEPENDENT EFFECTS INTO CONSIDERATIONS
Hoang H., Dao, Ph.D.
Illinois Institute of Technology, July 2016
Advisers: Dr. Zongzhi Li & Dr. Jamshid Mohammadi
Maintaining highway safety is viewed as the over-arching goal of mananging
transportation systems at all levels. According to the National Highway Traffic Safety
Administration (NHTSA), over 37,000 people got killed and 2.35 million are injured in
road crashes annually. The equivalent economic and societal losses are on the order of
over $231 billion, or an average of $820 per person. Thus, developing vehicle crash
models that can accurately predict crash occurrences becomes essential. The study begins
with literature review of models for predicting vehicle crash frequencies and crash
severity levels on highway segments and at highway intersections. The findings of
literature review indicate that some models lack prediction accuracy owing to exclusion
of many crashing contributing factors. Consequently, a new methodology for improved
vehicle crash predictability is proposed to include as many crash contributing factors as
possible. In addition, the proposed methodology aims to conduct crash predictions
targeting a highway network. Two computational experiments are performed for
methodology application, including one on highway segment-related crash predictions
using data on Highway Safety Information System for Illinois from 2001 to 2010, and

2. another one on intersection-related crash predictions using crash data on more than one
thousand intersections for period 2004 to 2010 provided by city of Chicago. Cross
comparisons are made on the results obtained by applying the proposed methodology,
method documented in Highway Safety Manual (AASHTO, 2010), and Empirical
Bayesian (EB) before-after method for validation. The proposed methodology is found to
have out-performed the other two methods. Future research directions are provided for
continuing refinements of the proposed methodology.