Work

Multi-Channel Spatio-Temporal Graph Convolutional Networks for Accurate Micromobility Demand Prediction Integrating Public Transport Data

Year: 2024

Type: conference-paper

Abstract: Accurately predicting city-wide, short-term micromobility (MM) demand, particularly for docked bike-sharing systems, is essential for optimizing urban transportation networks and promoting sustainable mobility. The integration of multimodal data, especially from public transportation (PT) systems like subways, plays a critical role in enhancing the precision of these predictions. However, current data-driven models often overlook the direct integration of public transport checkout data.

DOI: 10.1145/3681779.3696841

Authors: Linda Belkessa, Mostafa Ameli, Mohsen Ramezani, Mahdi Zargayouna

Institutions: Université Gustave Eiffel, The University of Sydney

Language: English

Topic: Traffic Prediction and Management Techniques

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Funder: Universitas Brawijaya

Award: G567712885