Comparative Insights Into E‐Scooter Usage Prediction Through Machine Learning and Deep Learning Techniques

Shared micromobility services are experiencing rapid growth, particularly in addressing last‐mile transportation needs. The most crucial questions focus on identifying the determinants of user behavior and the factors driving demand for micromobility vehicles. Investigating this topic is thus essential for meeting the demand of micromobility vehicles, ensuring their dynamic and flexible deployment, and optimizing overall system planning. In this study, demand forecasting was performed using a shared electric scooter (e‐scooter) dataset and by comparing 19 distinct machine learning (ML) and deep learning (DL) algorithms, including traditional ML algorithms, neural network–based (NN) models , ANN and metaheuristic hybrid models, and ensemble models. Algorithm performance, evaluated using R 2 and RMSE metrics, shows that boosting and hybrid models significantly outperform traditional algorithms. In this study, the algorithms were compared not only with RMSE and R 2 but also with their running times. Our analysis reveals that GRU, ANN–Grid–Search, ANN–Bayesian, ANN–Randomize–Search, ANN‐PSO, and ANN‐GA models achieve the highest performance, though this performance is inversely related to their computational cost. When the running time is included in the analysis, the GRU algorithm ranks best (RMSE: 0.945248, R 2 : 0.174226, runtime: 6.1), followed by ANN‐GA and ANN‐PSO models. These findings will help e‐scooter providers plan effectively and make informed investment decisions.