Modeling injury severity of crashes involving golf carts: A case study of The Villages, Florida

Abstract

Objective

Crashes involving golf carts (GCs) have been on an increasing trend in recent years, particularly in the United States. This study focuses on analyzing GC crashes in the Florida community known as The Villages, one of the largest GC-oriented communities in the nation and worldwide. The objective was to evaluate the injury severity of crashes involving GCs in a retirement community where GCs are a common mode of transportation.

Methods

The ordinal logistic regression (OLR) and Decision Tree Ensemble (DTE) models were used to analyze the injury severity of 616 GC-related crashes. Models’ accuracy parameters were used to check their reliability.

Results

The analysis revealed that GC crash severity is influenced by various factors. Factors found to be significant by the OLR model in determining injury severity include ejection of one or more occupants from the GC, the extent of damage to the GC, GC speed prior to the crash, roadway characteristics (including divided roadways, traffic control devices, paved shoulders, and T-intersections), and roll-over incidents. The OLR model demonstrated an overall accuracy of approximately 71% in predicting injury severity. The DTE model performed better, with an overall accuracy of 78%. The OLR model’s findings were supported by the DTE model, which identified estimated GC speed, occupant(s) ejection from the GC, estimated GC vehicle damage, intersection type, and type of shoulder as the most important factors influencing GC crash severity.

Conclusions

Understanding these factors is vital for transportation agencies to develop effective strategies to reduce the severity of GC crashes, ensuring the safety of GC users. This study provides recommendations to transportation agencies on measures to improve the safety of GCs.

Keywords:

• Golf carts
• crash injury severity
• ordinal logistic regression
• decision tree ensemble

Acknowledgement

This study is based on a research project funded by the Florida Department of Transportation (FDOT) and conducted by the University of North Florida (UNF) and the Florida International University (FIU). The opinions, results, and findings expressed in this manuscript are those of the authors and do not necessarily represent the views of FDOT, UNF, or FIU.

Author contributions

The authors confirm contributions to the paper as follows: study conception and design: Kinero, A.; data collection: Kinero, A., Bukuru, K., and Mwambeleko, E.; analysis and interpretation of results: Kinero, A., Bukuru, K., Mwambeleko, E., Sando, T., and Alluri, P.; draft manuscript preparation: Kinero, A., Bukuru, K., Mwambeleko, E., Sando, T., and Alluri, P. All authors reviewed the results and approved the final version of the manuscript.

Disclosure statement

The authors hereby declare to have no financial, professional, or personal interest that could be perceived as influencing the objectivity or integrity of the research presented in this paper. This declaration is made in accordance with the guidelines and requirements of the journal. None of the authors have affiliations, financial relationships, or competing interests that could be considered potential conflicts of interest.

Data availability statement

Data, models, or codes supporting this study’s findings are available from the corresponding author upon reasonable request.

Additional information

Funding

This research was conducted and supported by the Graduate Assistantship programs at the University of North Florida (UNF) and Florida International University (FIU).