Year of Publication

2019

Season of Publication

Fall

Paper Type

Master's Thesis

College

College of Computing, Engineering & Construction

Degree Name

Master of Science in Civil Engineering (MSCE)

Department

Engineering

NACO controlled Corporate Body

University of North Florida. School of Engineering

First Advisor

Dr. Thobias Sando

Department Chair

Dr. Christopher Brown

College Dean

Dr. William Klostermeyer

Abstract

Freeway merging maneuvers demand considerable attention by drivers and are among the more complex operations drivers must perform on freeways. Aging drivers, a growing population in the United States, face added challenges when merging. This study utilized Vissim models created in a previous study that modeled the behavior of aging drivers during freeway merging. An algorithm for Cooperative Merging Assistance System (CMAS) that utilizes Connected Vehicle (CV) technology was developed in this study. The Vissim models were created for two interchanges on I-75 in Fort Myers, Florida, each with different geometric characteristics. Acceleration lane lengths of 1000ft and 1500ft were analyzed in this study, and the CV environment was created in Vissim through the Component Object Model (COM) Interface. A sensitivity analysis was conducted by varying CV penetration rates, composition of aging on-ramp drivers, and mainline and on-ramp traffic flows to analyze the effects of CV technology under different levels of service (LOSs). Merging location, merging speed and vehicle interaction states (braking for lane change, emergency stop and cooperative braking) together with deceleration rate were the measures of effectiveness (MOEs) considered. Findings showed the number of aging drivers merging late onto the freeway can be decreased by up to 60.0% when CMAS was employed, while there was no significant change in merging speed at 95% confidence level when CMAS was employed. Furthermore, the results showed that CMAS reduced the percentages of aging drivers braking for lane change or emergency stop and also hard braking by up to 100% for low traffic conditions (LOS A and B). A maximum reduction of 82.2% was observed for cooperative braking of mainline vehicles when CMAS was employed. The reductions in interaction states were significant at 95% confidence level according to Mann-Kendall trend test.

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