Graph Neural Network-Based Design Decision Support for Shared Mobility Systems
Name
md_145_9_091703.pdf
Description
Published version
Size
1.27 MB
Format
Adobe PDF
Checksum (MD5)
16762d00fd9820182e66748a60f14815
Author(s)
Xiao, Yinshuang
Ahmed, Faez
Sha, Zhenghui
Date Issued
June 23, 2023
Journal
Journal of Mechanical Design
Publisher
ASME International
Citation
Xiao, Y., Ahmed, F., and Sha, Z. (June 23, 2023). "Graph Neural Network-Based Design Decision Support for Shared Mobility Systems." ASME. J. Mech. Des. September 2023; 145(9): 091703.
Version
Final published version
Abstract
Emerging shared mobility systems are gaining popularity due to their significant economic and environmental benefits. In this paper, we present a network-based approach for predicting travel demand between stations (e.g., whether two stations have sufficient trips to form a strong connection) in shared mobility systems to support system design decisions. In particular, we answer the research question of whether local network information (e.g., the network neighboring station’s features of a station and its surrounding points of interest (POI), such as banks, schools, etc.) would influence the formation of a strong connection or not. If so, to what extent do such factors play a role? To answer this question, we propose using graph neural networks (GNNs), in which the concept of network embedding can capture and quantify the effect of local network structures. We compare the results with a regular artificial neural network (ANN) model that is agnostic to neighborhood information. This study is demonstrated using a real-world bike sharing system, the Divvy Bike in Chicago. We observe that the GNN prediction gains up to 8% higher performance than the ANN model. Our findings show that local network information is vital in the structure of a sharing mobility network, and the results generalize even when the network structure and density change significantly. With the GNN model, we show how it supports two crucial design decisions in bike sharing systems, i.e., where new stations should be added and how much capacity a station should have.
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Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1115/1.4062666
