###### Abstract
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Purposes** & **Methods** & **Country** & **Variables** & **Ref.** \\ \hline System modelling to understand the input-output relationship of an e-scooter and rider system & ML algorithms, Statistical analysis & Germany & Tire size, speed, rider mass, loading on arms, ability to ascend the curb & [28] \\ \hline Forecasting demand for e-scooters with high spatial resolution & NN–decoder & South Korea & Start time and start location of every e-scooter usage, time and location log of every (mobile) app execution, the size of the grid & [29] \\ \hline Developing a demand forecasting model for the use of shared e-scooters & LSTM & South Korea & Time, weather & [30] \\ \hline A dockless e-scooter demand prediction model & FCN & Canada & Rent and return the e-scooter was rented, the date, hour, day of the week, trip duration, trip length, and starting/ending geolocations (latitude, longitude) & [31] \\ \hline Estimating greenhouse gas emission reductions affected by Shared micro-mobility systems & \(k\)NN, LightGBM, LR, RF, SVM, XGBoost systems & United States & Land use, weather, and temporal variables & [2] \\ \hline Forecasting the sharply fluctuating and erratic micro-mobility demand & GA, DLA & South Korea & The rental and return data & [32] \\ \hline A multi-criteria GIS-based analysis proposal aimed at planning priority networks for e-scooters & Criteria selection & Italy & Number of points of interest, closeness to public transport stops and stations, number of residents, number of employees, number of accidents that occurred, traffic flows attested by GPS points, maximum road speed allowed & [5] \\ \hline Estimating the usage efficiency of e-scooters & LR, ANN, RF & Sweden & Start time of idle parking, Idle parking occurring on weekdays or weekends, Battery power of trip end, Distance to the nearest motorway, Distance to the closest pedestrian way, Distance to the nearest residential way, Distance to the nearest bus stop, Distance to the nearest POI, density of POIs in the corresponding administrative division unit, density of bus stops in the corresponding administrative division unit & [33] \\ \hline Estimating hourly trips of e-scooter pilots & \(k\)NN, DT, RF & United States & Time, days since pilot start, day type, holiday temperature, rainfall, wind speed, average transportation & [34] \\ \hline Forecasting the delivery time and energy cost of packages or mail distributed with e-scooter & NN, \(k\)NN, GB, RF & Turkey & Demographic, weather, geographical, time, operational, cost & This study \\ \hline \hline \end{tabular} Abbreviations: POI—point of interest; NN—Neural Network; GB—Gradient Boosting; RNN—k-nearest neighbours; RF—random forest; LR—linear regression; SVM—support vector machine; GA—genetic algorithm; DLA—deep learning algorithm; FCN—fully convolutional network; LSTM—long short-term memory; ANN—artificial neural network; DT—decision tree.  
\end{table} 
Table 1: Features of selected studies related to this study. five different main factors are important in choosing an e-scooter for package or mail delivery. In addition to the fact that the actual data of all parameters are within the scope of a specific date, the daily number of transactions, the duration of the transaction, and the daily distance travelled was used for each driver. By keeping the number and type of data of the study large, we aimed to reduce the deviation rate in the forecast data and to increase the consistency rates of the forecast data. Input parameters of studies were generally determined to analyse electric mobility vehicle usage for studies for particular purposes. One study used probabilistic behavioural models developed within random utility theory, examining factors such as drivers' demographics, drivers' households, type of use, and road conditions to analyse the effects that led to the use of e-micro mobility for commuting [35].