Can we predict when and where people will rent electric scooters? Yes — and with impressive accuracy. A recent publication shows how advanced algorithms like XGBoost can revolutionize the management of micromobility in cities.
🌍 Context: Micromobility and Demand
In many cities, dockless electric scooters have become a daily transport option. But for operators, a crucial question remains:
Where and when will people want to rent a scooter?
Too many vehicles in one location is wasteful. Too few — lost revenue and frustrated users. That’s why accurately predicting demand is so important.
🧠 The Paper: A Three-Dimensional Prediction Model
The authors of arXiv 2507.02715 proposed a comprehensive machine learning framework combining three types of data:
- Temporal (e.g., hour, day of the week, weather),
- Spatial (e.g., proximity to public transport, number of POIs nearby),
- Network-based (e.g., how frequently users travel between different areas).
They generated 341 input features from this data and fed them into an XGBoost model.
🌳 What is XGBoost?
XGBoost stands for eXtreme Gradient Boosting. It’s a model that:
- Consists of multiple decision trees,
- Each subsequent tree corrects errors of the previous ones (gradient boosting),
- Works very efficiently with tabular data.
🛠️ How does it work?
- Starts with a simple prediction (e.g., average),
- Calculates error and gradients,
- Builds a new tree to model those gradients,
- Adds the tree’s output to the previous prediction.
Final formula:
$ y_{ ext{pred}} = f_0 + \eta \cdot T_1(x) + \eta \cdot T_2(x) + \dots + \eta \cdot T_N(x) $
📈 Why does it work?
In the paper, the model performs well because:
- The input features were well-engineered,
- XGBoost handles non-linearity (e.g., temp, time of day),
- SHAP analysis showed network topology (e.g., node centrality) were top features.
🔬 Results
- The model improved accuracy by 27–49% compared to other methods,
- Up to 90% better than classical time series models like Prophet.