# Understanding and Predicting the Usage of Shared Electric Scooter Services on University Campuses
## 6 Conclusions  
This study predicts SFFES use on a university campus using supervised and unsupervised machine learning techniques. A comprehensive feature selection analysis was conducted using k-means and hierarchical clustering, decision tree, random forest and Naive Bayes techniques. The 11 most important attributes were identified, including daily travel modes around the campus, the presence of green spaces and water, age, quality of the road surface, daily travel time and cost around the campus, monthly income, private vehicle ownership, connectivity between roads, modes of transport to/from campus, and gender.  
The random forest algorithm was developed to predict the usage frequency of SFFES using the identified important attributes. Simulation-based sensitivity analysis was conducted to gain deeper insights into the characterization and specification of SFFES users. Young females between 18 and 29 years old with an average monthly income were the most likely to always use SFFESs for their travels on campus. Males between 45 and 60 years with a high monthly salary were less likely to use SFFESs. The safety concerns and cost of renting e-scooters were the most important discouragement factors, while road features and suitable infrastructure, such as green spaces and separated lanes for scooters, were the most important encouragement factors. In addition, social distancing during the pandemic and no parking issues were the most considerable benefits of riding e-scooters from the respondents' perspective.  
The responsibility of the service providers and authorities is to provide all residents (especially people with limited transportation access) with accessible, equitable, safe, affordable, and sustainable transportation options. SFFES services are capable of helping to fill transportation gaps through providing an efficient, affordable alternative to cars for urban journeys. Scooters can have several benefits such as health, safety, and congestion opening, as well as some social/environmental equity benefits. To make an effective decision regarding whether and how SFFESs should be implemented in transportation systems of future cities, decision makers must first determine the definite role of these vehicles in the city. This can be determined through finding out the involved actors' visions of the future urban transport. As a result, to guarantee sustainable mobility, there is a need for not only technology and investment, but also fundamental research into related issues.  
S.M.H.M., conceptualization, methodology, software, formal analysis, investigation, resources, writing--original draft, supervision; Z.M., investigation, writing--review and editing, supervision; D.J.A., conceptualization, software, writing--review and editing; supervision; M.A., formal analysis, writing--review and editing; M.D.G., conceptualization, software, investigation; Y.C.W., conceptualization, resources, supervision; D.V.U., writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Informed Consent Statement: Not applicable.  
**Data Availability Statement:** The data are available from the corresponding author upon reasonable request.  
**Acknowledgments:** We would like to acknowledge all the experts and staff in the BEAM scooter company and the University of Malaya for providing data and information. In particular, the authors would like to acknowledge the Centre for Transportation Research (CTR), Faculty of Engineering, and University of Malaya for providing research facilities.  
**Conflicts of Interest:** The authors declare no conflict of interest.