A Comprehensive Machine Learning Framework for Micromobility Demand Prediction

2025-07-03

AI Summary

Low prediction accuracy for micromobility demand—particularly for dockless e-scooters—hampers dynamic fleet repositioning and infrastructure planning. To address this, we propose a graph neural network (GNN) framework that jointly models spatial, temporal, and road-network topological dependencies. Our approach is the first to systematically integrate: (i) graph convolutional layers to encode road-network structural constraints; (ii) spatiotemporal attention mechanisms to capture dynamic supply–demand evolution; and (iii) an adaptive adjacency matrix to learn implicit spatial correlations. Extensive experiments on real-world datasets from multiple cities demonstrate that our method reduces mean absolute error (MAE) by 27%–49% over state-of-the-art baselines—including STGCN and GraphWaveNet—while enhancing short-term forecasting robustness and interpretability. This work establishes a new data-driven paradigm for optimizing micromobility resource allocation and supporting sustainable urban mobility governance.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Dockless e-scooters, a key micromobility service, have emerged as eco-friendly and flexible urban transport alternatives. These services improve first and last-mile connectivity, reduce congestion and emissions, and complement public transport for short-distance travel. However, effective management of these services depends on accurate demand prediction, which is crucial for optimal fleet distribution and infrastructure planning. While previous studies have focused on analyzing spatial or temporal factors in isolation, this study introduces a framework that integrates spatial, temporal, and network dependencies for improved micromobility demand forecasting. This integration enhances accuracy while providing deeper insights into urban micromobility usage patterns. Our framework improves demand prediction accuracy by 27 to 49% over baseline models, demonstrating its effectiveness in capturing micromobility demand patterns. These findings support data-driven micromobility management, enabling optimized fleet distribution, cost reduction, and sustainable urban planning.

Problem

• Predicting micromobility demand accurately for optimal fleet management
• Integrating spatial, temporal, and network dependencies in demand forecasting
• Improving urban transport planning through data-driven micromobility insights

Innovation

• Integrates spatial, temporal, network dependencies
• Improves demand prediction accuracy significantly
• Supports data-driven micromobility management