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

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.

CitySEIRCast: An Agent-Based City Digital Twin for Pandemic Analysis and Simulation

“…Optimizing the trade-offs between performance and the computing cost of running HPC-supported digital-twin simulations on the cloud or on compute clusters is an ongoing challenge that needs to be resolved if we are to replace batch scheduling with on-demand requests of simulation runs [42, 49]; this will make it possible to eventually bring simulations closer to real-time. More realistic modelling of actual movements across a city by residents can also take advantage of new advances made in the use of real-time data from IoT sensors that integrates high-resolution infrastructure data and road user behaviors and vehicle movements across a city at various timepoints during the day [38]. This continuous synchronization of data will enable DT models to be continuously updated based on real-time conditions, making it an effective tool to run virtual simulations and scenario planning.…”

Incorporating Multi-Modal Travel Planning into an Agent-Based Model: A Case Study at the Train Station Kellinghusenstraße in Hamburg

“…Such a link could allow for mid-simulation data streams from real-world data sources (e.g., sensors networks that are part of the Internet of Things (IoT)) into the model, enabling real-time correction of inaccuracies that might accumulate as time progresses in the simulation. This would help to keep the simulation trajectory in line with real-time developments, making for more valid and truthful simulation results and transforming the model into a Digital Twin (DT) of its real-world pendant [17,42].…”

Developing Human-Centered Urban Digital Twins for Community Infrastructure Resilience: A Research Agenda

“…Cheliotis (2020) simulated human spatial behaviors in public parks, considering agents' visions empowered by a 3D ABM. Lenfers et al (2021) integrated real-time data from IoT sensors into ABM simulation as a digital twin of its real-world counterpart to investigate how real-time data can improve predictive capabilities of ABM in studying active travel behaviors. Other microsimulation models, like micromobility simulation models, can also be applied to examine local planning policies in a more realistic model environment (Han, Zhao, andLi 2020a, 2020b).…”

“…UDTs benefit coastal communities by providing three-dimensional (3D) visualization, augmented reality (AR), virtual city model, and prediction capabilities, among others. A digital twin infrastructure platform can enable both ubiquitous networked immersion and virtual human teleportation to any location and scale of the built environment (Park et al 2019). This will assist planners in solving the compound societal and environmental challenges and designing needs while simultaneously allowing for augmented and virtual interaction.…”