# Understanding and Predicting the Usage of Shared Electric Scooter Services on University Campuses
## 5 Discussion  
This study is designed to deeply explore the attitudes and perceptions of students and staff towards SFFES usage on campus. Various attributes were considered for this propose, such as the sociodemographic information of respondents, characterization of trips, road features, concerns/barriers, and benefits of riding SFFESs. In addition, this study is one of the first to predict the likelihood of usage frequency of SFFESs by employing various machine learning techniques and the first study on SFFESs in Malaysia. Previous studies have been mostly conducted in the US, China, and recently European cities. Moreover, for the first time we have employed different feature selection methods and machine learning algorithms to deeply evaluate the weight of important factors that affect the mode choice and usage of SFFESs between university students and staff. The campus of University of Malaya (UM) was selected for conducting this study because:  
* Shared micromobility is new in Malaysia, and most people have limited knowledge about it. The university community is a natural laboratory to test new mobility services. * The shared e-scooter companies such as BEAM, TRYKE and Myscooter are very interested in providing their services to university campuses in this initial stage. * UM is the biggest university in Malaysia, with more than 30,000 students and staff. In addition, more than 5000 international students and staff are on UM campus of different races, ethics, nations and generations. The diversity of the population fits the study requirements well. A comprehensive feature selection was conducted before developing machine learning predictive models. The main propose of this step was to accurately recognize the significant factors and importance by adopting supervised and unsupervised machine learning techniques. In addition, it decreased the complication of the final model by decreasing the number of variables based on their significance. Although decreasing the number of variables may reduce the accuracy of the final model, this reduction can be minimized  
Figure 18: Optimization results and importance of variables based on the second scenario: Never use SFFESs. by adopting proper and accurate feature selection techniques. In other words, adopting accurate feature selection methods will promote the complexity and practicality of the final model, while the accuracy remains adequately high. In this study, the initial models were developed using all 22 variables. After implementing feature selection methods, the number of variables reduced to 11. According to the feature selection results, daily travel mode inside the campus (Camp.mod/d) was the most effective factor in determining SFFES usage frequency. Other travel characterizations, such as daily travel cost and time/duration, were among the most influential factors. Sociodemographic attributes such as age, gender, monthly income and private vehicle ownership, played significantly important roles in mode choice and SEFFES usage, as also proved by previous studies [33; 66]. In addition, based on the outputs of all three supervised feature selection models, road features such as greenery and the connectivity of roads influenced the mode choice significantly. The initial RF model (with 22 variables) outperformed DT and NB models with 99.45% accuracy. Therefore, we selected the RF model for further analysis and developing the final model using the 11 most important variables. As expected, reducing the number of variables caused a reduction in accuracy by 6%. However, the authors believe that the final model is a much more valuable model with acceptable accuracy and less complexity. To unpack and shed light on the attitudes of the survey participants towards SEFFES usage, a simulation-based optimization was developed. Interesting results have been gained which could be useful for future works, recommendations and policy-making. Four optimization scenarios were defined based on the four categories of possible SFFES users: always, frequently, occasionally and never.