David Florence, PE

TTI Assistant Research Engineer
Program Manager

Education

• M.S., Transportation (Civil Engineering), Texas A&M University, 2017
• B.S.C.E., Transportation (Civil Engineering), Texas A&M University, 2015

Short Biography

David Florence, P.E. is an Assistant Research Engineer in the System Reliability Division of the Texas A&M Transportation Institute (TTI). In his 10-year career at TTI, David has performed research for the Texas Department of Transportation (TxDOT), Federal Highway Administration (FHWA), Crash Avoidance Metrics Partners (CAMP) and various University Transportation Centers (UTCs). He has engaged over 20 studies covering weather responsive traffic management, intelligent transportation systems (ITS), wrong-way driving, traffic signal control, sensors, traveler information systems, and/or microsimulation modeling and training. David has authored over 25 reports, published six peer-reviewed papers in the Transportation Research Record: Journal of the Transportation Research Board, and made numerous presentations on his research findings.

David has been engaged in weather responsive traffic management, ITS, and automation for 10 years. He has generated tools to automate functions for numerous deployments and simulations. These tools have enabled teams to automatically enact changes in the field for deployments or easily generate performance metrics from raw data for field systems or simulation outputs.

Mr. Florence is a skilled programmer. David has developed software for informing travelers about various other conditions on the roadway ranging from wrong-way drivers detected to signal timing information. David has created tools in C++, C#, Python, R, and JavaScript with most of his applications written in either C++ or Python. These applications each performed various modes of automation including processing weather data for alerts, sending real-time alerts to cell-phone apps about turning busses, converting event-based data from traffic signal controllers to connected vehicle data, and creating a passthrough between real signal controllers and traffic simulation tools.

David has led research efforts to evaluate queue responsive signal control in multimodal environments and impacts of partially autonomous vehicles on signalized corridors. He has conducted several operational evaluations on arterials and freeway facilities involving different innovative technologies. In these projects David provided a comprehensive analysis of system performance including data from both the field and microsimulation models of the facilities. David has also led or assisted with projects concerning light rail operations, roadway design, driver behavior, freight operations and detection, and connected and autonomous vehicles.