ORCID

0000-0002-1244-9492

Year

2021

Season

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

Second Advisor

Dr. Ramin Shabanpour

Third Advisor

Dr. Eren Ozguven

Department Chair

Dr. Osama Jadan

College Dean

Dr. William F. Klostermeyer

Abstract

ABSTRACT

Advanced traffic data collection methods, including the application of aerial sensors (drones) as traffic data collectors, can provide real-time traffic information more efficiently, effectively, and safely than traditional methods. Traffic trajectory data like vehicles’ coordinates and point timestamps are challenging to obtain at intersections using traditional field survey methods. The coordinates and timestamps crucial in calculating trajectories can be obtained using drones and their particular integrated software. Thus, this study explores the use of unmanned aerial systems (UAS), particularly tethered drones, to obtain traffic parameters for traffic mobility and safety studies at an unsignalized intersection in Tallahassee, Florida. Tethered drones provided more flexibility in heights and angles and collected data over a relatively larger space needed for the proposed approach.

Turning movement counts, gap study, speed study, and Level of Service (LOS) analysis for the stated intersection were the traffic studies conducted in this research. The turning movements were counted through ArcGIS Pro. From the drone footages, the gap study followed by the LOS analysis was carried out. A speed algorithm was developed to calculate speed during a speed study. Based on the results, the intersection operates under capacity with LOS B during the time. Also, the results indicated that the through movement traffic tends to slow down as they approach the intersection while south-bound right and east-bound left-turning traffic increase their speeds as they make a turn. Accuracy assessment was done by comparing the drone footages with the results displayed in ArcGIS software. The drone’s data collection was 100% accurate in traffic movement counting and 96% accurate in traffic movement classification. The level of accuracy is sufficient compared to other advanced traffic data collection methods.

In this study, safety was assessed by the surrogate safety measures (SSMs). SSMs can be the viable alternatives for locations with insufficient historical data and indicate potential future conflicts between roadway users. The surrogate measures used in this study include the Time to Collision (TTC), Deceleration-based Surrogate Safety Measure (DSSM), and Post-encroachment Time (PET). TTC and DSSM were used for rear-end conflicts, while PET was used to evaluate cross conflicts and other conflicts such as sideswipes.

The number of potential conflicts obtained in a one-hour study period was around 20 per 1000 vehicles traversing the intersection. The number of potential conflicts in one non-peak hour may indicate a safety problem associated with the intersection. This study’s findings can help develop appropriate guidelines and recommendations to transportation agencies in evaluating and justifying the feasibility of using tethered drones as safer and cheaper data collection alternatives while significantly improving intersection safety and operations.

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