Abstract

Wrong way driving (WWD) is dangerous. Recent utilization of advanced WWD Intelligent Transportation Systems (ITS) countermeasures has demonstrated a reduction in WWD activities. Examples of these advanced WWD ITS countermeasures include Rectangular Flashing Beacons (RFBs) and Light Emitting Diodes (LEDs). Agencies need to decide which highway sites would be best to deploy such devices to be most cost effective while minimizing the WWD risk in the highway network. Previous UCF research developed a highway segment model for determining WWD hotspots on limited access facilities. This hotspot model was applied to toll road networks in the state of Florida. Also, UCF previous research developed an optimization algorithm which was integrated with the WWD hotspot model to provide a cost-effective deployment of WWD countermeasures for use by highway agencies. This thesis examines the transferability and scalability of the UCF WWD hotspot and optimization methodology to a Florida statewide network. Different Wrong Way Crash Risk (WWCR) hotspot models were tested, and the Poisson model was selected which uses four-exit segments and five years of WWD event data. Sixty-three segments with 169 exit ramps not currently equipped with ITS countermeasures were identified as hotspots. It was found that 96 of the 169 ramps chosen by the optimization were not identified in the hotspots, indicating an improved investment utilization of 56.8% compared to just using the hotspot model. Comparing the WWD detection and turnaround rankings of sites currently equipped with RFBs or LEDs to the optimization rankings indicated a significant monotonic association between optimization rankings and turnaround percentage and detection rankings, thereby verifying the accuracy of the optimization. By showing the transferability and scalability of the UCF WWD hotspot and optimization methodology, this thesis can assist transportation agencies in reducing WWD in a cost-effective manner saving lives and money.

Notes

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Graduation Date

2020

Semester

Fall

Advisor

Al-Deek, Haitham

Degree

Master of Science (M.S.)

College

College of Engineering and Computer Science

Department

Civil, Environmental and Construction Engineering

Degree Program

Civil Engineering; Transportation System Engineering

Format

application/pdf

Identifier

CFE0008301; DP0023738

URL

https://purls.library.ucf.edu/go/DP0023738

Language

English

Release Date

December 2025

Length of Campus-only Access

5 years

Access Status

Masters Thesis (Campus-only Access)

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