ITE Journal - June 2021 - 38

Police officer or human control. Police officer/human
control are a unique traffic control type that are often associated
with specific conditions, such as abnormally high, unbalanced,
or widely varying directional and intersecting traffic due to
emergencies or planned special events.12 Police officer/human
control often replace traffic signal controls. Thus, comparing
the results presented in Table 2 to those crash types that occur
at a traffic signal, angle crashes were approximately just as likely
to occur at a traffic signal as at a location with a police officer/
human control. Conversely, pedestrian and sideswipe (meeting
in the opposite direction) crash types were more likely to occur at
police officer/human controlled locations. As police officer/human
control are often used to increase safety and traffic flow, these
relationships illustrate the use that police officer/human control
may have the ability to increase safety by reducing some crash
types that are often associated with higher severity; however, at the
same time, others may be increased.
Yield sign. Yield sign controlled locations exhibited more
angle, head-on, and pedestrian collisions, all often high-severity
crash types. Crash types of backing, parked vehicle, sideswipe (any
type), and animal were less likely to occur to a significant degree
and are also often less severe crash types. Pedal cycle crashes were
less likely occur at yield signs; however, it is noted that this form of
transportation may be less common at yield sign locations.

Conclusions
This study presented the relationship between crash types and
various traffic control devices on a large scale from North Carolina
and Ohio crash data obtained from the HSIS database. The results
revealed were found to be acceptable and intuitively reasonable.
Further research is needed to consider all locations where
crashes could have occurred; all locations where crashes did
not occur were not taken into consideration for this study.
Additionally, crashes were not normalized for exposure. An
improved model would account for the true speeds on a given
roadway segment, traffic volumes, number of lanes, and number
of locations with each traffic control device, and roadway
environment, among other variables.
Overall, the results of this study can guide transportation
professionals to create more informed safety decisions, complementing current safety processes and practices. The countermeasure selection process can be a more informed process with
the results presenting the critical crash types at common traffic
control devices, especially at locations with little crash history or
data available.

Acknowledgements
The authors would like to thank the Highway Safety Information
System for their assistance in providing the data used in this study.
38

J u ne 2021

i te j o urnal

References
1. Kim, K., Nitz, L., Richardson, J., and Li, L. " Analyzing the Relationship
Between Crash Types and Injuries in Motor Vehicle Collisions in Hawaii. "
Transportation Research Record, Vol. 1467 (1994):9-13.
2. Chen, H., Cao, L., and Logan, D. B. " Analysis of Risk Factors Affecting the
Severity of Intersection Crashes by Logistic Regression. " Traffic Injury
Prevention, Vol. 13, No. 3 (2012):300-307.
3. Yasmin, S., Eluru, N., Pinjari, A. R., and Tay, R. " Examining driver injury
severity in two vehicle crashes - A copula based approach. " Accident
Analysis & Prevention, Vol. 66 (2014):120-135.
4. Wang, X. and Kim S. H. Prediction and Factor Identification for Crash
Severity: Comparison of Discrete Choice and Tree-Based Models.
Transportation Resesarch Record, Vol. 2673, No. 9 (2019):640-653.
5. Office of Highway Policy Information. Highway Statistics 2017.
Washington, DC, USA, 2018.
6. Federal Highway Administration. Traffic Volume Trends, January December 2017. Washington, DC, USA, 2018.
7. Geedipally, S. R., Patil, S., and Lord, D. " Examination of Methods to
Estimate Crash Counts by Collision Type. " Transportation Research Record,
Vol. 2165 No. 1 (2010):12-20.
8. Baldock, M. R. J., Long, A. D., Lindsay, V. L., and McLean, A. J. Rear End
Crashes. Adelaide, Australia: Centre for Automotive Safety Research, 2005.
9. Ripley, B. and Venables, W. Modern Applied Statistics with S. Fourth Edition.
New York City: Springer, 2002.
10. Eluru, N., Paleti, R., Pendyala, R. M., and Bhat, C. R. " Modeling Injury
Severity of Multiple Occupants of Vehicles. " Transportation Research
Record, Vol. 2165, Nov. 1, (2010):1-11.
11. Abdel-Aty, M. and Keller, J. " Exploring the overall and specific crash
severity levels at signalized intersections. " Accident Analysis & Prevention,
Vol. 37, No. 3 (2005):417-425.
12. Parr, S., Wolshon, B., and Ericson, J. " Human performance modeling
for manual traffic control. " In: Proceedings of the Human Factors and
Ergonomics Society. Vol. 59 (2015):691-695.

Alyssa Ryan (S) is a Ph.D. candidate in the transportation engineering program at the University of
Massachusetts Amherst. She has been an active member
of ITE for several years and currently serves as a member
of the ITE Diversity & Inclusion Committee. Ryan's
research focuses on leveraging advanced technologies and data analytics
to address transportation safety, economic, and equity challenges.
Michael Knodler, Ph.D. (M) is a professor in the
Department of Civil & Environmental Engineering at
the University of Massachusetts Amherst and serves as
Director of the UMass Transportation Center. Knodler
has more than 20 years of experience in the field of
traffic operations and safety. Knodler has been an active member at
all levels of ITE, and previously served as Chair of the Transportation
Education Council and President of the Northeastern District.

ITE Journal - June 2021

Table of Contents for the Digital Edition of ITE Journal - June 2021