# Understanding and Predicting the Usage of Shared Electric Scooter Services on University Campuses
## 2 Related Works
An online survey was performed by the [41] on 1250 individuals in the five largest cities of Germany (Hamburg, Berlin, Frankfurt, Cologne, and Munich) in September 2019. It explored their overall mobility behavior and utilization of SFFES systems. It revealed that 42.7% of e-scooter users were aged between 18-25, and 28.8% between 26-35. The SFFES service substituted 49.1% of walking trips and 64.5% of public transport trips. A quantitative study was performed by the French [42]. They gathered 4382 user responses after various semi-structured and exploratory interviews. It reported that e-scooter renters were young (52% younger than 34), male (66%), highly educated (19% students, 53% work executives), and with a significant share of non-locals (42%). For the modal shift, users substituted walking (44%), public transport (30%), and bike trips (3% owned a bike; 9% shared a bike). Supervised learning algorithms learn correlation patterns from data (independent and target variables) and make decisions/predictions based on a specific objective. Decision trees (DT) are widely used in data-driven prediction analysis [43; 44; 45; 46]. Decision trees have been used for model evaluation and identifying important variables. Random forests (RF), a derivation of decision trees, can work in both supervised and unsupervised modes. It can handle continuous as well as categorical data in classification or regression tasks [47; 48]. Random forests are prioritized over other techniques, as it can manage highly non-linear data, and demonstrates many features, such as agility in locating noise in data and adjustability to parameters [49]. It has three main features: (i) estimating missing values automatically, (ii) Weighted Random Forest (WRF) for balancing errors in imbalanced data, and (iii) estimation of the significance of variables utilized for categorization [50]. Naive Bayes (NB) classifiers are also able to handle continuous and categorical variables and quickly make real-time predictions [51]. Unsupervised learning is designed to analyze unlabeled data [52]. As the amount of unlabeled data is exponentially rising, it is essential to explore unsupervised learning to perform feature selection. Data clustering (feature selection) is an important problem in knowledge discovery to improve the understandability, scalability and accuracy of resulting models. The clusters correspond to hidden models and the resulting outcomes represent data notions. In the context of supervised learning, feature selection refers to predictions based on provided outputs, while in unsupervised learning the features are clustered without any prior knowledge of the expected output. The importance of feature clustering is to improve prediction performance and provide a deeper understanding of the underlying process that produces the data. Examples of clustering algorithms are k-means, partitioning around medoids (PAM) and hierarchical clustering. This paper uses both supervised and unsupervised learning techniques for feature selections and predicting the usage frequency of SFFESs on campus.