Towards Real-Time Crowd Simulation Under Uncertainty Using an Agent-Based Model and an Unscented Kalman Filter

Clay, R.D.; Kieu, Le Minh; Ward, Jonathan A.; Heppenstall, Alison; Malleson, Nicolas

doi:10.1007/978-3-030-49778-1_6

Cited by 17 publications

(19 citation statements)

References 17 publications

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“…It becomes apparent that this adaptive dynamic is generally neither possible nor sensible under any circumstances. However, referring to the analyses from Clay et al [9] and Kieu et al [18] cited above, we suggest that the study's findings as a first basic step for dynamically incorporating real-time data into ABMs.…”

Section: Discussionmentioning

confidence: 84%

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Multi-Agent Systems and Digital Twins for Smarter Cities

Clemen

Ahmady-Moghaddam

Lenfers

et al. 2021

Proceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

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An intelligent combination of the Internet of Things (IoT) and approaches to modeling and simulation is one of the most challenging endeavors for future cities, manufacturing industries, and predictive maintenance. Digital Twins take on a unique role here. However, the question of what a Digital Twin is and what differentiates it from a regular model is still open. We present an experimental setup for integrating an existing simulation model of Hamburg's traffic system with the city's real-time sensor network. The Digital Twin is implemented using the large-scale multi-agent framework MARS. The entire process from the model description to retrieving real-time data from the IoT sensors and incorporating it in the simulation is presented. As a first prototypical example, a multi-modal mobility model was connected to real-world bike-sharing locations in Hamburg. We find that the combination of multi-agent systems and IoT sensors as a Digital Twin shows enormous potential for city planners, policy stakeholders, and other decision-makers. By correcting the course of a simulation via real-time data, the corridorof-uncertainty that is intrinsic to some simulation models' use can be reduced significantly. Furthermore, any divergence of simulated and sampled data can lead to a deeper understanding of complex adaptive systems like big cities. CCS CONCEPTS• Computing methodologies → Multi-agent systems; Simulation types and techniques.

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Section: Discussionmentioning

confidence: 84%

“…Still, the ongoing utilization of real-world spatio-temporal data within simulation models holds many challenges. Clay et al [9] and Kieu et al [18] state that it is currently not possible to use ABMs for real-time simulation due to the absence of established mechanisms for dynamically incorporating real-time data.…”

Section: Related Workmentioning

confidence: 99%

Multi-Agent Systems and Digital Twins for Smarter Cities

Clemen

Ahmady-Moghaddam

Lenfers

et al. 2021

Proceedings of the 2021 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation

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“…The model in the current study also suffered from uncertainty such as the population structure, human movement speed during the evacuation, and choices they make when faced with decisions. Studies have found that even a well-calibrated model will diverge from the true state of the underlying system (Clay et al, 2020). In this case, to assure the significance of the conclusion, this study simulated 3,000 entities for each scenario and repeated 50 runs for each scenario.…”

Section: Discussionmentioning

confidence: 97%

An Agent-Based Approach to Integrate Human Dynamics Into Disaster Risk Management

Wu¹,

Lei

Yang

et al. 2022

Front. Earth Sci.

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Disaster risk management (DRM) is the application of disaster risk reduction policies and strategies to reduce existing disaster risk and manage residual risk. However, due to dynamic human factors, it is challenging to depict and assess the effectiveness of DRM measures, and their implementation usually lacks a sufficient evidence-based evaluation process. Therefore, this study developed an agent-based model to integrate dynamic human behaviors into the DRM measures and evaluated their effectiveness in casualty reduction. The model was calibrated to simulate the debris flow event at Longchi town, China in 13 August 2010. The early warning system (EWS) and related DRM measures were taken as examples. The effectiveness of different DRM measures was quantitated by comparing the number of potential casualties. The main findings were: 1) EWS was very effective for community-based DRM as it could significantly decrease the average casualties by 30%. 2) Credibility of EWS was critical to its effectiveness. Less credible EWS might reduce its effectiveness by 9%. 3) EWS could be supplemented by other measures to further reduce casualties by 6%. 4) The downside effects of other DRM measures to EWS might exist and reduce its effectiveness by up to 5%. This study put forward an evidence-based approach to help policymakers select more cost-effective DRM measure, especially in the less developed countries where the available resources for DRM are limited.

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“…Data assimilation refers to a suite of techniques that allow new observations from the real world to be incorporated into models (Lewis, Lakshmivarahan, and Dhall 2006). The techniques have largely evolved from fields such as meteorology and applications to agent‐based modeling are relatively rare, but some have begun to explore this area (e.g., Wang and Hu 2015; Ward, Evans, and Malleson 2016; Long and Hu 2017; Clay et al 2020; Malleson et al 2020). Although there are similarities, data assimilation is quite different from typical agent‐based parameter estimation.…”

Section: Challenges and Opportunities For Agent‐based Modelingmentioning

confidence: 99%

Future Developments in Geographical Agent‐Based Models: Challenges and Opportunities

Heppenstall

Crooks

Malleson

et al. 2020

Geographical Analysis

Self Cite

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Despite reaching a point of acceptance as a research tool across the geographical and social sciences, there remain significant methodological challenges for agent‐based models. These include recognizing and simulating emergent phenomena, agent representation, construction of behavioral rules, and calibration and validation. While advances in individual‐level data and computing power have opened up new research avenues, they have also brought with them a new set of challenges. This article reviews some of the challenges that the field has faced, the opportunities available to advance the state‐of‐the‐art, and the outlook for the field over the next decade. We argue that although agent‐based models continue to have enormous promise as a means of developing dynamic spatial simulations, the field needs to fully embrace the potential offered by approaches from machine learning to allow us to fully broaden and deepen our understanding of geographical systems.

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Towards Real-Time Crowd Simulation Under Uncertainty Using an Agent-Based Model and an Unscented Kalman Filter

Abstract: This is a repository copy of Towards Real-Time Crowd Simulation Under Uncertainty Using an Agent-Based Model and an Unscented Kalman Filter.

Cited by 17 publications

References 17 publications

Multi-Agent Systems and Digital Twins for Smarter Cities

Multi-Agent Systems and Digital Twins for Smarter Cities

An Agent-Based Approach to Integrate Human Dynamics Into Disaster Risk Management

Future Developments in Geographical Agent‐Based Models: Challenges and Opportunities

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