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Podium Presentation at the Conference

Artificial Intelligence in Transportation

Session: AI-empowered Sensing and Visualization in Transportation

1373995 - Dynamic Visualization Platform for Travel-Related Data Integration to Support Sustainability-Based Decision-Making for Smart Cities

Friday, June 16, 2023
2:00 PM - 2:15 PM CST
Room: 408
TRACK: A

    Primary Author(s)

  • Yufei Xu

    Graduate research assistant
    Georgia Institue of Technology, United States

    • Co-Author(s)

  • Yufei Xu

    Graduate research assistant
    Georgia Institue of Technology, United States

    • Presenter(s)

  • MK

    Md Gulam Kibria

    Graduate Research Assistant
    Georgia Institute of Technology
    Clarkston, GA, United States

    • Co-Author(s)

  • Einat Tenenboim, n/a

    Postdoctoral fellow
    Georgia Institute of Technology
    Atlanta, Georgia, United States

  • Viswa Sri Rupa Anne, n/a

    PhD Student
    Georgia Institute of Technology
    Atlanta, Georgia, United States

  • Chaojie Wang, n/a

    Graduate Research Assistant
    Georgia Institute of Technology, United States

  • Zhiwei Chen, Ph.D.

    Assistant Professor
    Drexel University, United States

  • Srinivas Peeta, Ph.D.

    Frederick R. Dickerson Chair and Professor
    Georgia Institute of Technology
    Atalanta, Georgia, United States

Smart cities seek to leverage data from advanced information, communication, and sensor technologies for achieving their travel-related sustainability goals. However, the multi-source, multi-timescale nature of these disparate datasets introduces challenges to community decision-makers, hindering the use of these technologies in an efficient, effective, and holistic manner. Using statistical and machine learning methods, we developed a generalizable methodology for creating a dynamic visualization platform and implemented it in the City of Peachtree Corners (PTC), GA, which serves as a living lab for this study. The platform presents an integration of nine different datasets, including travel speed, traffic count, transit (supply and demand), autonomous vehicle (AV) shuttle service (supply and demand), accidents, weather, city facility types, parking, and socio-demographic data. These datasets were selected based on PTC’s travel-related sustainability goals – mobility, safety, accessibility, and equity. Given the heterogeneity of the different datasets (e.g., some include real-time data whereas others include historical data of different timescales), we created a distinct layer for each dataset. The different layers can be displayed in separation or in conjunction with one another, thereby allowing the user to visualize the dynamic spatiotemporal interactions between the different types of data. By selecting a specific subset of layers, the user can examine the interactions between those layers and identify city locations that require specific attention. To demonstrate the platform’s usability, we present a case study aimed at improving PTC’s existing AV shuttle service through enhancing accessibility and equity for residents. The methodology developed is customizable and can be transferred to other smart cities and scaled to larger-sized ones. Overall, this platform has the potential to support decision-makers in planning city development and in deploying different solution options to emerging mobility problems.

Learning Objectives:

Attendees can expect to learn the following from this session:
  • Upon completion, participant will be able to describe the challenges of integrating multi-source data.
  • Upon completion, participant will be able to describe how to integrate multi-source data.
  • Upon completion, participant will be able to explain how the visualization platform can be used to solve emerging mobility problems.