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

2026-02-18

AI Summary

This study addresses the challenge of insufficient accuracy in urban shared micromobility demand forecasting, which hampers efficient fleet dispatching and traffic management. To this end, we propose a lightweight gradient boosting framework that, for the first time, enables high-precision demand prediction across multiple temporal granularities—from five minutes to one hour—by effectively integrating spatiotemporal and contextual features while supporting edge deployment. Evaluated on real-world datasets of electric scooters and e-bikes from five major cities, our approach significantly outperforms existing methods and generative AI models, demonstrating a strong capability to capture complex mobility dynamics. The proposed framework thus offers an efficient, scalable solution for data-driven decision support in sustainable urban transportation systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Learning on the Edge & Model Compression
• Application Domains: Mobility, Driving & Flight

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: The sharing economy

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.

Problem

• shared mobility
• demand forecasting
• urban mobility
• spatiotemporal prediction
• micro-mobility

Innovation

• gradient boosting
• demand forecasting
• shared mobility
• edge-ready prediction
• spatiotemporal modeling