MoDE-Boost: Boosting Shared Mobility Demand with Edge-Ready Prediction Models

TL;DR

This paper introduces MoDE-Boost, a set of gradient boosting models designed for accurate short-term demand forecasting in shared micro-mobility, leveraging data fusion and contextual features to improve urban transportation efficiency.

Contribution

It presents two novel gradient boosting model variations for demand classification and regression, capable of multi-horizon predictions using urban mobility data.

Findings

Effective demand prediction across five metropolitan areas.

Outperforms state-of-the-art methods and AI-based models.

Enhances micro-mobility management for sustainable cities.

Abstract

Urban demand forecasting plays a critical role in optimizing routing, dispatching, and congestion management within Intelligent Transportation Systems. By leveraging data fusion and analytics techniques, traffic demand forecasting serves as a key intermediate measure for identifying emerging spatial and temporal demand patterns. In this paper, we tackle this challenge by proposing two gradient boosting model variations, one for classiffication and one for regression, both capable of generating demand forecasts at various temporal horizons, from 5 minutes up to one hour. Our overall approach effectively integrates temporal and contextual features, enabling accurate predictions that are essential for improving the efficiency of shared (micro-) mobility services. To evaluate its effectiveness, we utilize open shared mobility data derived from e-scooter and e-bike networks in five metropolitan areas. These real-world datasets allow us to compare our approach with state-of-the-art methods as well as a Generative AI-based model, demonstrating its effectiveness in capturing the complexities of modern urban mobility. Ultimately, our methodology offers novel insights on urban micro-mobility management, helping to tackle the challenges arising from rapid urbanization and thus, contributing to more sustainable, efficient, and livable cities.