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Article

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

Department of Computer Science, Hamburg University of Applied Sciences, Berliner Tor 7, 20099 Hamburg, Germany
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Academic Editors: Philippe J. Giabbanelli and Arika Ligmann-Zielinska
Sustainability 2021, 13(13), 7000; https://doi.org/10.3390/su13137000
Received: 18 May 2021 / Revised: 15 June 2021 / Accepted: 19 June 2021 / Published: 22 June 2021
(This article belongs to the Special Issue Modelling and Simulation of Human-Environment Interactions)
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. View Full-Text
Keywords: agent-based model; model development; IoT sensors; smart cities; real-time data; MARS; simulation correction; decision support systems; urban planning; multimodal travel agent-based model; model development; IoT sensors; smart cities; real-time data; MARS; simulation correction; decision support systems; urban planning; multimodal travel
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MDPI and ACS Style

Lenfers, U.A.; Ahmady-Moghaddam, N.; Glake, D.; Ocker, F.; Osterholz, D.; Ströbele, J.; Clemen, T. Improving Model Predictions—Integration of Real-Time Sensor Data into a Running Simulation of an Agent-Based Model. Sustainability 2021, 13, 7000. https://doi.org/10.3390/su13137000

AMA Style

Lenfers UA, Ahmady-Moghaddam N, Glake D, Ocker F, Osterholz D, Ströbele J, Clemen T. Improving Model Predictions—Integration of Real-Time Sensor Data into a Running Simulation of an Agent-Based Model. Sustainability. 2021; 13(13):7000. https://doi.org/10.3390/su13137000

Chicago/Turabian Style

Lenfers, Ulfia A., Nima Ahmady-Moghaddam, Daniel Glake, Florian Ocker, Daniel Osterholz, Jonathan Ströbele, and Thomas Clemen. 2021. "Improving Model Predictions—Integration of Real-Time Sensor Data into a Running Simulation of an Agent-Based Model" Sustainability 13, no. 13: 7000. https://doi.org/10.3390/su13137000

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