Improving Model Predictions—Integration of Real-Time Sensor Data into a Running Simulation of an Agent-Based Model
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 intermod...
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doaj-3eccc40a134a4751a5b52285617d801d2021-07-15T15:46:15ZengMDPI AGSustainability2071-10502021-06-01137000700010.3390/su13137000Improving Model Predictions—Integration of Real-Time Sensor Data into a Running Simulation of an Agent-Based ModelUlfia A. Lenfers0Nima Ahmady-Moghaddam1Daniel Glake2Florian Ocker3Daniel Osterholz4Jonathan Ströbele5Thomas Clemen6Department of Computer Science, Hamburg University of Applied Sciences, Berliner Tor 7, 20099 Hamburg, GermanyDepartment of Computer Science, Hamburg University of Applied Sciences, Berliner Tor 7, 20099 Hamburg, GermanyDepartment of Computer Science, Hamburg University of Applied Sciences, Berliner Tor 7, 20099 Hamburg, GermanyDepartment of Computer Science, Hamburg University of Applied Sciences, Berliner Tor 7, 20099 Hamburg, GermanyDepartment of Computer Science, Hamburg University of Applied Sciences, Berliner Tor 7, 20099 Hamburg, GermanyDepartment of Computer Science, Hamburg University of Applied Sciences, Berliner Tor 7, 20099 Hamburg, GermanyDepartment of Computer Science, Hamburg University of Applied Sciences, Berliner Tor 7, 20099 Hamburg, GermanyThe 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.https://www.mdpi.com/2071-1050/13/13/7000agent-based modelmodel developmentIoT sensorssmart citiesreal-time dataMARS |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ulfia A. Lenfers Nima Ahmady-Moghaddam Daniel Glake Florian Ocker Daniel Osterholz Jonathan Ströbele Thomas Clemen |
spellingShingle |
Ulfia A. Lenfers Nima Ahmady-Moghaddam Daniel Glake Florian Ocker Daniel Osterholz Jonathan Ströbele Thomas Clemen Improving Model Predictions—Integration of Real-Time Sensor Data into a Running Simulation of an Agent-Based Model Sustainability agent-based model model development IoT sensors smart cities real-time data MARS |
author_facet |
Ulfia A. Lenfers Nima Ahmady-Moghaddam Daniel Glake Florian Ocker Daniel Osterholz Jonathan Ströbele Thomas Clemen |
author_sort |
Ulfia A. Lenfers |
title |
Improving Model Predictions—Integration of Real-Time Sensor Data into a Running Simulation of an Agent-Based Model |
title_short |
Improving Model Predictions—Integration of Real-Time Sensor Data into a Running Simulation of an Agent-Based Model |
title_full |
Improving Model Predictions—Integration of Real-Time Sensor Data into a Running Simulation of an Agent-Based Model |
title_fullStr |
Improving Model Predictions—Integration of Real-Time Sensor Data into a Running Simulation of an Agent-Based Model |
title_full_unstemmed |
Improving Model Predictions—Integration of Real-Time Sensor Data into a Running Simulation of an Agent-Based Model |
title_sort |
improving model predictions—integration of real-time sensor data into a running simulation of an agent-based model |
publisher |
MDPI AG |
series |
Sustainability |
issn |
2071-1050 |
publishDate |
2021-06-01 |
description |
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. |
topic |
agent-based model model development IoT sensors smart cities real-time data MARS |
url |
https://www.mdpi.com/2071-1050/13/13/7000 |
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