Agent-based Simulation Platform Research Articles

Multicellular PID control for robust regulation of biological processes.

This article presents the first implementation of a proportional-integral-derivative (PID) biomolecular controller within a consortium of different cell populations, aimed at robust regulation of biological processes. By leveraging the modularity and cooperative dynamics of multiple engineered cell populations, we develop a comprehensive in silico analysis of the performance and robustness of P, PD, PI and PID control architectures. Our theoretical findings, validated through in silico experiments using the BSim agent-based simulation platform for bacterial populations, demonstrate the robustness and effectiveness of our multicellular PID control strategy. This innovative approach addresses critical limitations in current control methods, offering significant potential for applications in metabolic engineering, therapeutic contexts and industrial biotechnology. Future work will focus on experimental validation in vivo and further refinement of the control models.

Quantifying traffic emission reductions and traffic congestion alleviation from high-capacity ride-sharing

Despite the promising benefits that ride-sharing offers, there has been a lack of research on the benefits of high-capacity ride-sharing services. Prior research has also overlooked the relationship between traffic volume and the degree of traffic congestion and emissions. To address these gaps, this study develops an open-source agent-based simulation platform and a heuristic algorithm to quantify the benefits of high-capacity ride-sharing with significantly lower computational costs. The simulation platform integrates a traffic emission model and a speed-density traffic flow model to characterise the interactions between traffic congestion levels and emissions. The experiment results demonstrate that ride-sharing with vehicle capacities of 2, 4, and 6 passengers can alleviate total traffic congestion by approximately 3%, 4%, and 5%, and reduce traffic emissions of a ride-sourcing system by approximately 30%, 45%, and 50%, respectively. This study can guide transportation network companies in designing and managing more efficient and environment-friendly mobility systems.

Simulation-Based Assessment of Hyperconnected Megacity Parcel Logistics

Background: The concept of Hyperconnected Megacity Parcel Logistics (HMPL) was introduced in 2018 and aims to enhance the efficiency, responsiveness, resilience, and sustainability of parcel movements in megacities. However, evaluating such fundamental solutions presents challenges and requires a comprehensive understanding of all stakeholders and decisions involved. Methods: This study introduces a discrete-event agent-based simulation platform that encompasses critical stakeholders and addresses various levels of decision-making. This platform provides an opportunity to evaluate key decisions within an HMPL structure. Results: To demonstrate the capability of the simulator, we assess the impact of package routing and consolidation strategies facilitated by HMPL compared to traditional practices. Preliminary findings suggest that increased interconnection among nodes in HMPL reduces transit times, thereby enabling tighter customer delivery services. However, examining different consolidation heuristics reveals potential trade-offs between handling and shipping costs under fixed shipment schedules, prompting further investigation into dynamic shipment services. Conclusions: The findings of this study suggest that the benefits of innovative approaches in a complex environment, such as parcel logistics, cannot be evaluated in isolation from other decisions. Accurate assessment of the ultimate outcomes and underlying trade-offs requires multi-faceted models that incorporate all key variables.

The calculation and distribution of CAV carbon emissions on urban transportation systems: A comparative analysis of renewable and non-renewable energy sources

When powered by electricity, Connected and Autonomous Vehicles (CAVs), an emerging mode of transportation, possess the capacity to reduce exhaust emissions greatly. However, accurately measuring carbon emissions in urban transportation remains a challenge, especially considering emissions from electricity generation and gasoline consumption. This paper proposes an innovative method for calculating CAVs' carbon emissions distribution, utilizing both renewable and non-renewable energy. The study employs SUMO, an agent-based simulation platform, to develop an intelligent driver model and cooperative adaptive cruise control modules, tracking vehicle movement behavior across various vehicle types, including Gasoline Vehicles (GVs), Electric Vehicles (EVs), Human-Driven Vehicles (HDVs), and CAVs. Subsequently, a lifecycle electric carbon emission model is constructed, integrating the energy consumption model of EVs with carbon emission factors of renewable and non-renewable energy. Visualization models are then developed to clarify the carbon emission distribution within the traffic network. A case study conducted in Suzhou, China validates the model, analyzing the spatiotemporal distribution of carbon emissions. Results show EVs can reduce carbon emissions by 70%–90 % compared to GVs on urban roads during rush hour, while CAVs can further reduce emissions by 35%–50 % compared to HDVs. Additionally, carbon emissions from non-renewable energy sources were found to exceed those from renewable sources.

An Agent-Based Simulation Platform for a Safe Election: From Design to Simulation

Managing the logistics and safety of an election system, from delivering voting machines to the right locations at the right time to ensuring that voting lines remain reasonable in length is a complex problem due to the scarcity of resources, especially human poll workers, and the impact of human behavior and disrupting events on the performance of this critical system. These complexities grew with the need for physical distancing during the COVID-19 pandemic coinciding with multiple key national elections, including the 2020 general presidential election in the USA. In this paper, we propose a digital clone platform leveraging agent-based simulation to model and experiment with resource allocation decisions and voter turnout fluctuations and facilitate “what-if” scenario testing of any election. As a use case, we consider three different concurrent polling location problems, namely, resource allocation, polling layout, and management. The main aim is to reduce voter waiting time and provide visibility of different scenarios for polling and state-level managers. We explain the proposed simulation platform based on Fulton County for the 2020 presidential US election. Fulton County had 238 polling locations in 2020, which provided publicly available voter turnout data. The developed platform realistically models at the county level and at specific locations, suggesting the possible allocation of finite resources among locations in the county and the configuration of each location, accounting for physical, legal, and technical constraints. Multiple realistic scenarios were developed and embedded into the simulation platform to evaluate and verify the different systems. The system performance and key attributes of the election system, such as waiting time, resource utilization, and layout safety, were tested and validated.

Hyperconnected Logistic Platform for Heavy-Duty Machinery: Leveraging Physical Internet Principles to Drive the Composting Industry

The Physical Internet (PI) envisions a global logistics system that integrates physical, digital and operational connections. This study aims to develop a hyperconnected logistic platform for heavy-duty machinery (HDM) in the composting industry by utilizing a systematic methodology. The proposed architecture consists of four layers: the Domain Model, the MBSE Model, the Information Sharing Model and the Agent-based Simulation Platform. The Domain Model analyzes the current situation and investigates stakeholder viewpoints, and the MBSE Model reduces complexity and describes mutual interactions between requirements and needs. The Information Sharing Model focuses on the information exchange among the main components, and the Agent-based Simulation Platform implements the proposed platform. The feasibility of the proposed architecture is demonstrated through a use case in Styria, Austria. Three simulation-based scenarios are analyzed, starting from the semi-hyperconnected approach up to the hyperconnected approach with PI vision integration. The results indicate that the hyperconnected platform is successful in serving all composting facilities, leveraging underutilized resources and promoting high-quality compost production. Thus, the platform provides support in a local, communal setting, resulting in enhancing the circular economy within the composting sector. Our efforts aim to contribute to the realization of the Physical Internet vision and promote composting to ultimately achieve a more sustainable future.

A framework for modeling human behavior in large-scale agent-based epidemic simulations

Agent-based modeling is increasingly being used in computational epidemiology to characterize important behavioral dimensions, such as the heterogeneity of the individual responses to interventions, when studying the spread of a disease. Existing agent-based simulation frameworks and platforms currently fall in one of two categories: those that can simulate millions of individuals with simple behaviors (e.g., based on simple state machines), and those that consider more complex and social behaviors (e.g., agents that act according to their own agenda and preferences, and deliberate about norm compliance) but, due to the computational complexity of reasoning involved, have limited scalability. In this paper, we present a novel framework that enables large-scale distributed epidemic simulations with complex behaving social agents whose decisions are based on a variety of concepts and internal attitudes such as sense, knowledge, preferences, norms, and plans. The proposed framework supports simulations with millions of such agents that can individually deliberate about their own knowledge, goals, and preferences, and can adapt their behavior based on other agents’ behaviors and on their attitude toward complying with norms. We showcase the applicability and scalability of the proposed framework by developing a model of the spread of COVID-19 in the US state of Virginia. Results illustrate that the framework can be effectively employed to simulate disease spreading with millions of complex behaving agents and investigate behavioral interventions over a period of time of months.

Agent-Based Mobility Simulation for Potential Cost and Benefits of Subscription Services with Associated Cost Structures

This study aims to present a method to analyze the problems that arise from the increasing use of shared mobility systems to solve the current problems of congestion. The use of shared and/or autonomous vehicles (SAVs) is beneficial since it could reduce the number of vehicles on the roads. On the other hand, the increased usage of SAVs to fulfill the same transportation needs would affect the total life of those vehicles. Current cost models are based on average values and life cycles of 15 years; they may not be useful in the case of increased daily travel miles of vehicles when subscription-based shared mobility systems become prevalent. In this paper, we present a comprehensive framework which considers the interactions of new cost structures and the system performance under new mobility schemes. This paper envisions the scenarios that will exemplify the importance of considering the changing costs because of the increased mileage of vehicles. Cost functions are properly designed to assess the new scenarios, a pricing scheme is introduced from the service provider’s point of view to adjust the demand for the services and the profits, an agent-based simulation platform is presented, and a framework is demonstrated to consider the interactions and optimize the system.

Winner Strategies in a Simulated Stock Market

In this study, we explore the dynamics of the stock market using an agent-based simulation platform. Our approach involves creating a multi-strategy market where each agent considers both fundamental and technical factors when determining their strategy. The agents vary in their approach to these factors and the time interval they use for technical analysis. Our findings indicate that investing heavily in reducing the value–price gap was a successful strategy, even in markets where there were no trading forces to reduce this gap. Furthermore, our results remain consistent across various modifications to the simulation’s structure.

SUMMIT: A multi-modal agent-based co-simulation of urban public transport with applications in contingency planning

In this article, we present SUMMIT (Singapore Urban Multi-Modal Integrated Transport Simulator), a multi-modal agent-based simulation platform for public transport calibrated using real world mobility data sets from Singapore. SUMMIT uses a co-simulation approach in order to model multiple modes of public transport in tandem with commuters seamlessly transitioning between different mode simulations. This is implemented through a message passing framework codenamed Fabric (Fast, Agent-Based, Reproducible, Integrated Co-simulation) which helps to align and synchronise the simulation as it transitions through the different simulation time steps. Three stand-alone simulators, each modelling a key mode in the Singapore public transport system, namely train, bus and taxi, are integrated with a commuter control model, which simulates the commuter behaviour and takes care of commuters’ transfer between different modes. We present one application of SUMMIT in public transport contingency planning, where train services are disrupted in multiple lines of Singapore Mass Rapid Transit (MRT) network. Different scenarios are simulated and key performance indicators are compared to help planners evaluate the efficacy of mitigation measures. The results show that bridging bus services, a commonly used mitigation measure, are effective in reducing crowd sizes within the train stations. However, we demonstrate that bridging buses could also result in worsening of commuters’ travel time due to over-demand for these buses. Our results also show early dissemination of information to commuters during a train service disruption could reduce the negative impacts of the disruption event significantly.

A cooperative approach to avoiding obstacles and collisions between autonomous industrial vehicles in a simulation platform

Industry 4.0 leads to a strong digitalization of industrial processes, but also a significant increase in communication and cooperation between the machines that make it up. This is the case with autonomous industrial vehicles (AIVs) and other cooperative mobile robots which are multiplying in factories, often in the form of fleets of vehicles, and whose intelligence and autonomy are increasing. While the autonomy of autonomous vehicles has been well characterized in the field of road and road transport, this is not the case for the autonomous vehicles used in industry. The establishment and deployment of AIV fleets raises several challenges, all of which depend on the actual level of autonomy of the AIVs: acceptance by employees, vehicle location, traffic fluidity, collision detection, or vehicle perception of changing environments. Thus, simulation serves to account for the constraints and requirements formulated by the manufacturers and future users of AIVs. In this paper, after having proposed a broad state of the art on the problems to be solved in order to simulate AIVs before proceeding to experiments in real conditions, we present a method to estimate positions of AIVs moving in a closed industrial environment, the extension of a collision detection algorithm to deal with the obstacle avoidance issue, and the development of an agent-based simulation platform for simulating these two methods and algorithms. The resulting/final/subsequent simulation will allow us to experiment in real conditions.

In-silico evaluation of adenoviral COVID-19 vaccination protocols: Assessment of immunological memory up to 6 months after the third dose.

The immune response to adenoviral COVID-19 vaccines is affected by the interval between doses. The optimal interval is unknown. We aim to explore in-silico the effect of the interval between vaccine administrations on immunogenicity and to analyze the contribution of pre-existing levels of antibodies, plasma cells, and memory B and T lymphocytes. We used a stochastic agent-based immune simulation platform to simulate two-dose and three-dose vaccination protocols with an adenoviral vaccine. We identified the model's parameters fitting anti-Spike antibody levels from individuals immunized with the COVID-19 vaccine AstraZeneca (ChAdOx1-S, Vaxzevria). We used several statistical methods, such as principal component analysis and binary classification, to analyze the correlation between pre-existing levels of antibodies, plasma cells, and memory B and T cells to the magnitude of the antibody response following a booster dose. We find that the magnitude of the antibody response to a booster depends on the number of pre-existing memory B cells, which, in turn, is highly correlated to the number of T helper cells and plasma cells, and the antibody titers. Pre-existing memory T cytotoxic cells and antibodies directly influence antigen availability hence limiting the magnitude of the immune response. The optimal immunogenicity of the third dose is achieved over a large time window, spanning from 6 to 16 months after the second dose. Interestingly, after any vaccine dose, individuals can be classified into two groups, sustainers and decayers, that differ in the kinetics of decline of their antibody titers due to differences in long-lived plasma cells. This suggests that the decayers may benefit from a tailored boosting schedule with a shorter interval to avoid the temporary loss of serological immunity.

Scenario-Based Demand Assessment of Urban Air Mobility in the Greater Munich Area

In this work, we project the idea of aerial passenger transport in the near future: on-demand urban air mobility (UAM) for the greater Munich area in the year 2030. We propose a simulation framework that facilitates analyzing the potential demand of UAM. We define five scenarios (ranging from representations of a niche application to a mass transport option) by varying the fleet size, available technologies, infrastructure placement, and pricing strategies. These scenarios are fed into an extended version of the agent-based transport simulation platform MATSim chained with the microscopic transportation orchestrator. The simulation results show that the system configuration has a big impact on UAM demand. The infrastructure capacity and fleet size were identified as the main bottlenecks. We see high levels of demand during peak hours and for trips up to 20 km; however, none of the scenarios result in a sufficiently high modal share of UAM that substantially reduces traffic volumes of ground-based modes, and hence have no significant impact on congestion. Yet, the different trip purposes generate significantly different demands. UAM for airport access could be an especially relevant modal choice in the premium airport-passenger segment. When aiming for an effective and sustainable UAM service, our recommendations are as follows: first, use it to support and improve, rather than cannibalize, existing public transport options; and second, minimize negative impacts on the environment and society to maximize public acceptance.

Household-based E-commerce demand modeling for an agent-based urban transportation simulation platform

ABSTRACT The e-commerce market has grown rapidly in the past two decades. The need for predicting e-commerce demand and evaluating relevant policies and solutions is increasing. However, the existing simulation models for e-commerce demand are still limited and do not consider the impacts of delivery options and their attributes that shoppers face on multiple dimensions of e-commerce demand. We propose a novel framework involving disaggregate behavioral models that jointly predict e-commerce expenditure, purchase amount per transaction, delivery mode, and option choices. The proposed framework can simulate the changes in e-commerce demand and be used to evaluate the impacts of a range of policies and solutions. We specify the model parameters based on various sources of relevant information, integrate the model into an urban freight simulator, and conduct a demonstrative simulation for a prototypical North American city. The results of the analysis highlight the capability and applicability of the proposed modeling framework.

Innovative robotization of manual manufacturing processes

Manufacturing industries are continually challenged to adapt to a competitive environment. Consequently, there is an urgency to opt for automation technologies to upgrade their manufacturing facilities and make them more flexible. Especially this relates to automating manual manufacturing processes, which is often challenging to structure to ensure repetitiveness and generalization to other processes within the facility. Consequently, innovating systematic approaches to identify robotization opportunities is an interesting proposition for manufacturing set-ups that would like to integrate collaborative robots on the shop floor and struggle with the decision steps to follow.In this paper, a framework for supporting robotization effort for manufacturing set-ups is proposed. The methodology consisting of five phases, culminating in the identification of robotization opportunities. A case for manual milling manufacturing processes is demonstrated as a ‘proof-of-concept’. The first step of the proposed approach focuses on task decomposition, in which manual manufacturing tasks are characterized. This is followed by task allocation to a robot and human agent based on intrinsic characteristics of the task to capabilities of the agent. Next, alternative layout configurations for candidate cell layouts are generated. In the final step, a candidate layout is selected and modeled in an agent-based simulation platform, considering factors such as realism, interaction safety between the robot and human agent, and interesting manufacturing metrics such as resource utilization and throughput rate. A final configuration is optimized, which visualizes a collaborative robot performs loading and unloading tasks alongside an operator performing highly cognitive tasks. For safety, zoning of the manufacturing cell is visualized, considering a working area separated by a safety fence.

The Camp Nou Stadium as a Testbed for City Physiology: A Modular Framework for Urban Digital Twins

In this paper, the Camp Nou stadium is used as a testbed for City Physiology, a theoretical framework for urban digital twins. With this case study, the modularity and adaptability of the framework, originally intended for city-scale simulations, are tested on a large facility venue. As a proof of concept, several statistical techniques and an agent-based simulation platform are coupled to simulate a crowd in the stadium, and a process of four steps is followed to build the case study. Both the conceptual (interdomain) and technical (domain specific) layers of the digital twin are defined and connected in a nonlinear process so that they represent the complexity of the object to be simulated. The result obtained is a strategy to build a digital twin from the domain point of view, paving the way for more complex, more ambitious simulators.

BioDynaMo: a modular platform for high-performance agent-based simulation.

MotivationAgent-based modeling is an indispensable tool for studying complex biological systems. However, existing simulation platforms do not always take full advantage of modern hardware and often have a field-specific software design.ResultsWe present a novel simulation platform called BioDynaMo that alleviates both of these problems. BioDynaMo features a modular and high-performance simulation engine. We demonstrate that BioDynaMo can be used to simulate use cases in: neuroscience, oncology and epidemiology. For each use case, we validate our findings with experimental data or an analytical solution. Our performance results show that BioDynaMo performs up to three orders of magnitude faster than the state-of-the-art baselines. This improvement makes it feasible to simulate each use case with one billion agents on a single server, showcasing the potential BioDynaMo has for computational biology research.Availability and implementationBioDynaMo is an open-source project under the Apache 2.0 license and is available at www.biodynamo.org. Instructions to reproduce the results are available in the supplementary information.Supplementary informationAvailable at https://doi.org/10.5281/zenodo.5121618.

From Infection to Immunity: Understanding the Response to SARS-CoV2 Through In-Silico Modeling.

BackgroundImmune system conditions of the patient is a key factor in COVID-19 infection survival. A growing number of studies have focused on immunological determinants to develop better biomarkers for therapies.AimStudies of the insurgence of immunity is at the core of both SARS-CoV-2 vaccine development and therapies. This paper attempts to describe the insurgence (and the span) of immunity in COVID-19 at the population level by developing an in-silico model. We simulate the immune response to SARS-CoV-2 and analyze the impact of infecting viral load, affinity to the ACE2 receptor, and age in an artificially infected population on the course of the disease.MethodsWe use a stochastic agent-based immune simulation platform to construct a virtual cohort of infected individuals with age-dependent varying degrees of immune competence. We use a parameter set to reproduce known inter-patient variability and general epidemiological statistics.ResultsBy assuming the viremia at day 30 of the infection to be the proxy for lethality, we reproduce in-silico several clinical observations and identify critical factors in the statistical evolution of the infection. In particular, we evidence the importance of the humoral response over the cytotoxic response and find that the antibody titers measured after day 25 from the infection are a prognostic factor for determining the clinical outcome of the infection. Our modeling framework uses COVID-19 infection to demonstrate the actionable effectiveness of modeling the immune response at individual and population levels. The model developed can explain and interpret observed patterns of infection and makes verifiable temporal predictions. Within the limitations imposed by the simulated environment, this work proposes quantitatively that the great variability observed in the patient outcomes in real life can be the mere result of subtle variability in the infecting viral load and immune competence in the population. In this work, we exemplify how computational modeling of immune response provides an important view to discuss hypothesis and design new experiments, in particular paving the way to further investigations about the duration of vaccine-elicited immunity especially in the view of the blundering effect of immunosenescence.

COVID-19 mitigation by digital contact tracing and contact prevention (app-based social exposure warnings)

A plethora of measures are being combined in the attempt to reduce SARS-CoV-2 spread. Due to its sustainability, contact tracing is one of the most frequently applied interventions worldwide, albeit with mixed results. We evaluate the performance of digital contact tracing for different infection detection rates and response time delays. We also introduce and analyze a novel strategy we call contact prevention, which emits high exposure warnings to smartphone users according to Bluetooth-based contact counting. We model the effect of both strategies on transmission dynamics in SERIA, an agent-based simulation platform that implements population-dependent statistical distributions. Results show that contact prevention remains effective in scenarios with high diagnostic/response time delays and low infection detection rates, which greatly impair the effect of traditional contact tracing strategies. Contact prevention could play a significant role in pandemic mitigation, especially in developing countries where diagnostic and tracing capabilities are inadequate. Contact prevention could thus sustainably reduce the propagation of respiratory viruses while relying on available technology, respecting data privacy, and most importantly, promoting community-based awareness and social responsibility. Depending on infection detection and app adoption rates, applying a combination of digital contact tracing and contact prevention could reduce pandemic-related mortality by 20–56%.

Simulation of Motion of an Ensemble of Unmanned Ground Vehicles Using FLAME GPU

A new approach to modeling the spatial dynamics of unmanned ground vehicles (UV) and conventional vehicles (CV) using the FLAME GPU supercomputer agent-based simulation platform is presented. A new simulation model of an artificial road network (ARN) of the "Manhattan Grid" type is proposed, within the framework of which the spatial dynamics of the UV and CV ensemble is studied under various scenario conditions. The effects of "turbulence" and "crush" (traffic congestion) resulting from intensive and dense traffic of vehicles are investigated.