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Improving Model Predictions—Integration of Real-Time Sensor Data into a Running Simulation of an Agent-Based Model

Author

Listed:
  • Ulfia A. Lenfers

    (Department of Computer Science, Hamburg University of Applied Sciences, Berliner Tor 7, 20099 Hamburg, Germany)

  • Nima Ahmady-Moghaddam

    (Department of Computer Science, Hamburg University of Applied Sciences, Berliner Tor 7, 20099 Hamburg, Germany)

  • Daniel Glake

    (Department of Computer Science, Hamburg University of Applied Sciences, Berliner Tor 7, 20099 Hamburg, Germany)

  • Florian Ocker

    (Department of Computer Science, Hamburg University of Applied Sciences, Berliner Tor 7, 20099 Hamburg, Germany)

  • Daniel Osterholz

    (Department of Computer Science, Hamburg University of Applied Sciences, Berliner Tor 7, 20099 Hamburg, Germany)

  • Jonathan Ströbele

    (Department of Computer Science, Hamburg University of Applied Sciences, Berliner Tor 7, 20099 Hamburg, Germany)

  • Thomas Clemen

    (Department of Computer Science, Hamburg University of Applied Sciences, Berliner Tor 7, 20099 Hamburg, Germany)

Abstract

The current trend towards living in big cities contributes to an increased demand for efficient and sustainable space and resource allocation in urban environments. This leads to enormous pressure for resource minimization in city planning. One pillar of efficient city management is a smart intermodal traffic system. Planning and organizing the various kinds of modes of transport in a complex and dynamically adaptive system such as a city is inherently challenging. By deliberately simplifying reality, models can help decision-makers shape the traffic systems of tomorrow. Meanwhile, Smart City initiatives are investing in sensors to observe and manage many kinds of urban resources, making up a part of the Internet of Things (IoT) that produces massive amounts of data relevant for urban planning and monitoring. We use these new data sources of smart cities by integrating real-time data of IoT sensors in an ongoing simulation. In this sense, the model is a digital twin of its real-world counterpart, being augmented with real-world data. To our knowledge, this is a novel instance of real-time correction during simulation of an agent-based model. The process of creating a valid mapping between model components and real-world objects posed several challenges and offered valuable insights, particularly when studying the interaction between humans and their environment. As a proof-of-concept for our implementation, we designed a showcase with bike rental stations in Hamburg-Harburg, a southern district of Hamburg, Germany. Our objective was to investigate the concept of real-time data correction in agent-based modeling, which we consider to hold great potential for improving the predictive capabilities of models. In particular, we hope that the chosen proof-of-concept informs the ongoing politically supported trends in mobility—away from individual and private transport and towards—in Hamburg.

Suggested Citation

  • Ulfia A. Lenfers & Nima Ahmady-Moghaddam & Daniel Glake & Florian Ocker & Daniel Osterholz & Jonathan Ströbele & Thomas Clemen, 2021. "Improving Model Predictions—Integration of Real-Time Sensor Data into a Running Simulation of an Agent-Based Model," Sustainability, MDPI, vol. 13(13), pages 1-14, June.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:13:p:7000-:d:579488
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    References listed on IDEAS

    as
    1. Ulfia A. Lenfers & Julius Weyl & Thomas Clemen, 2018. "Firewood Collection in South Africa: Adaptive Behavior in Social-Ecological Models," Land, MDPI, vol. 7(3), pages 1-17, August.
    2. Agustín Zaballos & Alan Briones & Alba Massa & Pol Centelles & Víctor Caballero, 2020. "A Smart Campus’ Digital Twin for Sustainable Comfort Monitoring," Sustainability, MDPI, vol. 12(21), pages 1-33, November.
    3. Ana Lavalle & Miguel A. Teruel & Alejandro Maté & Juan Trujillo, 2020. "Improving Sustainability of Smart Cities through Visualization Techniques for Big Data from IoT Devices," Sustainability, MDPI, vol. 12(14), pages 1-17, July.
    4. Pablo Pico-Valencia & Juan A Holgado-Terriza, 2018. "Agentification of the Internet of Things: A systematic literature review," International Journal of Distributed Sensor Networks, , vol. 14(10), pages 15501477188, October.
    5. Ramon Sanchez-Iborra & Luis Bernal-Escobedo & José Santa, 2020. "Eco-Efficient Mobility in Smart City Scenarios," Sustainability, MDPI, vol. 12(20), pages 1-15, October.
    6. Christian Berger & Mari Bieri & Karen Bradshaw & Christian Brümmer & Thomas Clemen & Thomas Hickler & Werner Leo Kutsch & Ulfia A. Lenfers & Carola Martens & Guy F. Midgley & Kanisios Mukwashi & Victo, 2019. "Linking scales and disciplines: an interdisciplinary cross-scale approach to supporting climate-relevant ecosystem management," Climatic Change, Springer, vol. 156(1), pages 139-150, September.
    7. Qi Zhang & Hongyang Li & Xin Wan & Martin Skitmore & Hailin Sun, 2020. "An Intelligent Waste Removal System for Smarter Communities," Sustainability, MDPI, vol. 12(17), pages 1-27, August.
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