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Pine Island Academy, present in Nocatee Community, Jacksonville, Florida, uses various modes of transport for picking up and dropping off children at school. The parents using private vehicles to pick up and drop off their kids tend to take a long time due to delays and congestion in their routes. Parents who use golf carts (GC) for this activity tend to take a short time to pick up and drop off their kids. Due to this, it is necessary to analyze the use of GC as an alternative mode of transport for children pick up and drop off while reducing delay and congestion. This paper uses VISSIM microscopic simulation for analysis whereby the traffic flow in the morning for drop-off and the afternoon for pickup was observed at different GC traffic compositions regarding the relative delay, queue delay, and travel times. From the analysis, it was observed that the delays and travel times for private vehicles decrease as the GC composition increases in the network. These results show that a GC may be suitable for picking up and dropping off children at school. This factor is further supported by the ANOVA test of significance. Due to the results from this paper, recommendations to the transportation agencies responsible for school management include promoting GC usage, establishing designated GC lanes to ensure smooth traffic flow and minimal interaction with other vehicles, implementing a multi-modal approach that includes GCs, and emphasizing safety training and awareness for GC users. [ABSTRACT FROM AUTHOR]

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.