Are you struggling to find available docks to return your bikes or frustrated with finding no charging ports in a charging station for your EVs? Our future mobility systems require better simulation and prediction to support systems design decisions, such as station locations and capacities. Our recent study, led by Yinshuang Xiao and collaborated with Dr. Faez Ahmed, using cutting-edge graph neural networks to improve demand prediction in shared mobility networks was featured by ASME Journal of Mechanical Design. Let us rev up shared mobility for a greener and smarter commute!

Emerging shared mobility systems are gaining popularity because of their cost-effectiveness and positive environmental impact. In designing such urban transportation networks, questions such as where a station should be constructed and how much capacity it should offer are essential because they influence system usage, adoption rate, and return on investment. In this study, we develop a cutting-edge method using graph neural networks to support the design decision-making of shared mobility systems, like bike-sharing programs. One uniqueness of this method is that it accounts for the neighborhood information of a station, such as shopping malls or schools, in the formulation of node embedding in training the graph neural networks. When predicting if there are sufficient demands between stations, our results indicate the proposed method has superior performance, thus offering practical guidance for optimizing station locations and capacities. Our model can be readily transformed into a simulation tool or integrated into a decision-support platform for the design of more efficient and sustainable shared mobility networks.

ASME JCISE invites submissions to the Special Issue on Networks and Graphs for Engineering Systems and Design, guest edited by Zhenghui Sha, Astrid Layton, Babak Heydari, Megan Konar, and Douglas L. Van Bossuyt. This special issue is dedicated to promoting the dissemination of knowledge related to complex networks in engineering systems and design and highlighting the latest advances at the intersection of network science, graph theory, and engineering. Submissions are due September 30, 2024.

https://www.asme.org/publications-submissions/journals/administration/call-for-papers/special-issue-on-networks-and-graphs-for-engineering-systems-and-design

Abstract

In the ever-evolving landscape of engineering, the fusion of network science and graph theories has emerged as a dynamic force, revolutionizing the way we represent, design, model, and optimize complex systems. Networks, defined by nodes and edges, are particularly effective in modeling the interaction and interdependency among individual entities in complex systems. Networks have become the cornerstone for comprehending the intricate relationships underlying a myriad of engineering domains. From transportation networks optimizing urban mobility, power grids ensuring energy efficiency and resilience, and social networks shaping human interactions to biological networks inspiring human-engineered system design, the application of network science and graphs in engineering spans a vast spectrum of disciplines.

Topic Areas

THE SCOPE OF THIS ISSUE INCLUDES BUT IS NOT LIMITED TO:

• Network analysis, modeling, and visualization for sociotechnical systems
• Graph neural networks for systems engineering, prediction, and design
• Ecological/biological network-inspired system engineering and design
• Complex networks and artificial intelligence (AI), e.g., link prediction and optimization problems 
• Domain-specific networks and systems: design and manufacturing, food-energy-water nexus, supply chain, IoT, and inter-connected infrastructure systems (e.g., power grid, smart cities)
• Social and organizational network analysis in engineering applications, e.g., real-time social media mapping to determine infrastructure failure due to extreme events
• Network-based approaches to design for market systems
• Dynamics (e.g., formation, diffusion, propagation) on engineering networks 
• Spatiotemporal networks in engineering applications 
• Novel network data collection; network data science

Dr. Zhenghui Sha and the PhD student Pawornwan Thongmak attended the 57th Hawaii International Conference on System Sciences (HICSS) and presented the work, Geospatial Network Analysis of US Megaregions in 40 Years, led by the authors, P. Thongmak, Y. Xiao, P. A. Gavino, M. Zhang, and Z. Sha. By collaborating with Dr. Ming Zhang, the Director of Cooperative Mobility for Competitive Megaregions (CM2) (a University Transportation Center (UTC) at UT Austin funded by the Department of Transportation), we use network analysis approaches and models to gain insights into the evolution of megaregions, complex urban systems that “contain two or more roughly adjacent urban metropolitan areas that, through commonality of systems—of transport, economy, resources, and ecologies—experience blurred boundaries between the urban centers, such that perceiving and acting as if they are a continuous urban area is, for the purposes of policy coordination, of practical value” [Defining U.S. Megaregions].

In particular, this paper proposes a network analysis framework based on geographic information systems (GIS) to study the development of megaregions in support of urban planning and policy-making. The framework includes a new approach to model geo-shaped polygon data of census places as the Place Geo-Adjacency Network (PGAN). In particular, the integration of descriptive network analysis and degree distribution analysis supports the study of spatial connections, geospatial growth, hub effects, and expansion patterns in megaregions. To demonstrate this framework, a case study was conducted on four US megaregions to study their growth and expansion in the last 40 years since 1980. The degree distribution analysis captures the small-world property and quantifies the level of geospatial connectivity influenced by the hub effects. Policymakers can use the model as a decision support for urban planning and policy design to reduce disparities and improve connectivity in megaregion areas.