Author ORCID Identifier

Year of Publication


Degree Name

Master of Science (MS)

Document Type

Master's Thesis




Computer Science

First Advisor

Dr. Nathan Jacobs


Understanding free-flow speed is fundamental to transportation engineering in order to improve traffic flow, control, and planning. The free-flow speed of a road segment is the average speed of automobiles unaffected by traffic congestion or delay. Collecting speed data across a state is both expensive and time consuming. Some approaches have been presented to estimate speed using geometric road features for certain types of roads in limited environments. However, estimating speed at state scale for varying landscapes, environments, and road qualities has been relegated to manual engineering and expensive sensor networks. This thesis proposes an automated approach for estimating free-flow speed using LiDAR (Light Detection and Ranging) point clouds and satellite imagery. Employing deep learning for high-level pattern recognition and feature extraction, we present methods for predicting free-flow speed across the state of Kentucky.

Digital Object Identifier (DOI)

Funding Information

We gratefully acknowledge the financial support of the National Science Foundation (CAREER, IIS-1553116) in 2018-2020 and Orbital Insight, Inc. in 2019-2020, and computing resources provided by the University of Kentucky Center for Computational Sciences.