Paper Type

Master's Thesis


College of Computing, Engineering & Construction

Degree Name

Master of Science in Civil Engineering (MSCE)



NACO controlled Corporate Body

University of North Florida. School of Engineering

First Advisor

Dr. Thobias Sando

Second Advisor

Dr. Ramin Shabanpour

Third Advisor

Dr. Priyanka Alluri

Department Chair

Dr. Alan Harris

College Dean

Dr. William Klostermeyer


United States cities have adopted shared micromobility systems to mitigate problems facing the transportation industry. The State of Florida has at least 13 established shared micromobility systems. Identifying the factors influencing the injury severity is critical for improving the safe operations of micromobility devices. This study investigates the leading factors affecting the injury severity for micromobility related crashes in Florida. The study investigates 463 crashes involving e-scooters or e-bikes between 2018 and 2022 in Florida, extracted by the Text-Mining approach from the SignalFour Analytics database. Analyses consisted of exploratory data analysis to discover trends and patterns. First, using the Pedestrian and Bicycle Crash Analysis Tool (PBCAT) to assess the crash types based on the motorist and non-motorist maneuvers. It then presents the descriptive analysis of the explanatory factors. The relationship between crash severity and other explanatory factors was modeled using Ordered Logistic Regression (OLR) to identify the significant factors. Then the Bayesian Network (BN) model was used to determine the interdependency between the variables. PBCAT results concluded that e-scooters riders are more vulnerable when crossing the intersection from the right-hand side of the motorist. At the same time, for e-bikes, most crashes occur on the travel/bike lane when the rider is traveling parallel to the vehicle. OLR suggested the shoulder type, AADT, driver's action, sidewalk width, NM age, gender and maneuver, time of the day, crash location and the type of control device as the most significant factors affecting the severity of micromobility crashes. Finally, The BN model result suggests the highest likelihood of fatal/severe injury is associated with the non-motorist (NM) crossing the signal-controlled intersection with the driver failing to yield the right of way. In roadway segment crashes, the highest likelihood is when the motorist is moving straight on a roadway with three or more lanes with curbed shoulders. Finally, the study suggests recommendations to mitigate the injury severity of micromobility crashes.

Available for download on Friday, January 10, 2025