Agent-based Simulation Framework Research Articles

Optimizing bikeshare service to connect affordable housing units with transit service

Affordable housing and transit accessibility have long been focal points for housing and urban development agencies at all levels, from national to regional. However, these elements are often considered and planned for independently, leading over time to an expanding spatial gap between affordable housing and transit services. This separation creates issues of mobility equity. Fortunately, bikeshare, an emerging mobility solution, provides the potential to bridge this spatial gap between affordable housing communities and transit services. To leverage this, we designed a bikeshare station (BSS) location optimization tool that aims to alleviate the impacts of this spatial divide through bikeshare services. Specifically, we developed a multimodal agent-based modeling (ABM) and simulation framework aimed at enhancing accessibility to a variety of destinations. To solve this optimization problem, we developed a genetic algorithm to determine the optimal locations for BSS. There are three main conclusions from this research. First, the strategic positioning of BSS will enhance accessibility for affordable housing community (AHC) residents by reducing average transit travel time and walk distance, or increasing the number of destinations accessible by transit. This underscores the advantages of combining bikeshare and transit systems to cater to the mobility requirements of AHC residents, especially for non-work-related trips in suburban areas. Second, the relationship between transit and bikeshare is twofold: complementation and substitution. In instances where a lengthy trip involves multiple transit transfers and significant waiting time, bikeshare could replace some transit trips. Lastly, but most importantly, this study advises a forward-looking approach for governments and operators to avoid the waste of public resources when planning bikeshare systems. The tool developed by this research can facilitate this forward-looking approach in practice.

How individuals’ opinions influence society’s resistance to epidemics: an agent-based model approach

BackgroundProtecting public health from infectious diseases often relies on the cooperation of citizens, especially when self-care interventions are the only viable tools for disease mitigation. Accordingly, social aspects related to public opinion have been studied in the context of the recent COVID-19 pandemic. However, a comprehensive understanding of the effects of opinion-related factors on disease spread still requires further exploration.MethodsWe propose an agent-based simulation framework incorporating opinion dynamics within an epidemic model based on the assumption that mass media channels play a leading role in opinion dynamics. The model simulates how opinions about preventive interventions change over time and how these changes affect the cumulative number of cases. We calibrated our simulation model using YouGov survey data and WHO COVID-19 new cases data from 15 different countries. Based on the calibrated models, we examine how different opinion-related factors change the consequences of the epidemic. We track the number of total new infections for analysis.ResultsOur results reveal that the initial level of public opinion on preventive interventions has the greatest impact on the cumulative number of cases. Its normalized permutation importance varies between 69.67% and 96.65% in 15 models. The patterns shown in the partial dependence plots indicate that other factors, such as the usage of the pro-intervention channel and the response time of media channels, can also bring about substantial changes in disease dynamics, but only within specific ranges of the dominant factor.ConclusionsOur results reveal the importance of public opinion on intervention during the early stage of the pandemic in protecting public health. The findings suggest that persuading the public to take actions they may be hesitant about in the early stages of epidemics is very costly because taking early action is critical for mitigating infectious diseases. Other opinion-related factors can also lead to significant changes in epidemics, depending on the average level of public opinion in the initial stage. These findings underscore the importance of media channels and authorities in delivering accurate information and persuading community members to cooperate with public health policies.

An uncertainty quantification framework for agent-based modeling and simulation in networked anagram games

ABSTRACT In a networked anagram game, players are provided letters with possible actions of requesting letters from their neighbours, replying to letter requests, or forming words. The objective is to form as many words as possible as a team. The experimental data show that behaviours among players can vary significantly. However, simulations using agent-based models (ABM) in the literature often have not incorporated proper uncertainty quantification methods to characterise diverse behaviours of players. In this work, we propose an uncertainty quantification framework to build, exercise, and evaluate agent behaviour models and simulations for networked group anagram games. Specifically, using the data of game experiments, the proposed framework considers the clustering of game players based on their performance to reflect players’ heterogeneity. Moreover, we also quantify uncertainty within each cluster through statistical modelling and inference. Numerical studies of networked game configurations are conducted to demonstrate the merits of the proposed framework.

An adaptive route choice model for integrated fixed and flexible transit systems

Over the past decade, there has been a surge of interest in the application of agent-based simulation models to evaluate flexible transit solutions characterized by different degrees of short-term flexibility in routing and scheduling. A central modelling decision in the development is how one chooses to represent the mode- and route-choices of travellers. The real-time adaptive behaviour of travellers is important to model in the presence of a flexible transit service, where the routing and scheduling of vehicles is highly dependent on supply-demand dynamics at a near real-time temporal resolution. We propose a utility-based transit route-choice model with representation of within-day adaptive travel behaviour and between-day learning where station-based fixed-transit, flexible-transit, and active-mode alternatives may be dynamically combined in a single path. To enable experimentation, this route-choice model is implemented within an agent-based dynamic public transit simulation framework. We first explore model properties in a choice between fixed- and flexible-transit modes for a toy network. The adaptive route choice framework is then applied to a case study based on a real-life branched transit service in Stockholm, Sweden. This case study illustrates level-of-service trade-offs, in terms of waiting times and in-vehicle times, between passenger groups and analyzes traveller mode choices within a mixed fixed- and flexible transit system. Results show that the proposed framework is capable of capturing dynamic route choices in mixed flexible and fixed transit systems and that the day-to-day learning model leads to stable fixed-flexible mode choices.

Agent-based simulation of vehicle-sharing systems

ABSTRACT Agent-based simulation is a powerful tool that allows emulating the complex behaviour of individuals within a system in order to assess its global performance. In this context, this paper develops a modular agent-based simulation framework for being applied to vehicle-sharing systems. The modular structure of the simulator implies that each sub-system is treated separately, so that each module can be replaced or modified without affecting the others. In this way, the simulator can be used to analyse specific problems such as the optimal location of stations, the design of repositioning strategies, or different vehicle usage policies, by focusing only on the relevant module. The simulation framework proposed has been implemented and tested in the analysis of two different systems: a station-based bike-sharing system and a mixed (i.e., station-based and free-floating) car-sharing initiative. The paper shows some of the results obtained from such implementations as an example of its possible applications.

Integrated Agent-Based Simulation and Game Theory Decision Support Framework for Cash Flow and Payment Management in Construction Projects

Effective cash flow management has become crucial for projects and stakeholders given the wide payment-related problems and financial risks encountered in the construction industry worldwide. Previous studies mostly addressed cash flow and payments from the perspective of a specific stakeholder, resulting in an imbalanced cash flow management culture that is further intensified by the power asymmetry of the top-down payment decision-making process. This research proposes an adaptive decision support framework for evaluating and negotiating payment options in construction projects while incorporating the individual and collective financial roles of stakeholders. The framework is comprised of three modules for data acquisition, payment simulation, analysis, and negotiation, as well as decision support. It integrates agent-based simulation, data envelopment analysis, and game theory for a multi-level study of project performance while capturing the driving forces of stakeholders in payment negotiations. A case study project is used to demonstrate the framework implementation under varying payment conditions and interest rates. The results provide quantitative profiles of stakeholders to identify incurred charges, balanced payment conditions, and suitable compensation. Finally, the framework can be utilized by stakeholders and jurisdictions to move towards enhanced contractual arrangements that alleviate economic and financial risks with the informed collaboration of its entities.

Demand-responsive transport for students in rural areas: A case study in Vulkaneifel, Germany

In rural areas with low population density, demand-responsive transport (DRT) is considered a promising alternative to conventional public transport (PT). With a fleet of smaller vehicles, DRT provides a much more flexible and convenient service. This characteristic makes DRT also a potential mode of transport to serve students in rural areas. If DRT vehicles are used to serve students, then the funding for conventional school buses (or adapted public transport schedules) can be reinvested in the DRT system. This may help to relieve the financial burden experienced by DRT operators and enable the operation of a large-scale DRT service in rural areas. In this study, a demand model for school commutes based on real-world, open-source data for Landkreis Vulkaneifel, a rural region in Germany, is built. Then a feasibility study is carried out using an agent-based transport simulation framework. In the feasibility study, various setups and operational schemes are explored, which are followed by a systematic cost analysis. Based on a conservative estimation, an annual budget of around 1600 Euro per student will be needed to maintain and operate a fleet of DRT vehicles that can transport all the students in the region from home to school on time in the morning. During the remaining time of the day and on school holidays, the vehicles can be used for conventional DRT service for the public.

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.

An integrated agent-based simulation modeling framework for sustainable production of an Agrophotovoltaic system

The Agrophtovoltaic (APV) system is an alternative for sustainable crop production, where solar power is generated via Photovoltaic (PV) modules. Since both crop production and solar power generation activities are heavily dependent on dynamic environmental conditions, it is challenging to design an APV system based on accurate estimation of its performance. To this end, this study aims to introduce an agent-based simulation (ABS) framework integrated with polynomial regression and ridge regression. In particular, two agent types are devised, as follow: (1) The photovoltaic agent calculates electricity produced via PV modules, and estimates its profits; and (2) the crop production agent calculates crop harvests underneath the PV modules, and estimates their profits. To validate the proposed framework, field experiment data with five types of crops (i.e., corn, sesame, soybean, mung bean, and red bean) at the APV system at Jeollanam-do Agricultural Research and Extension Services in South Korea with three different shading ratios of 21.3%, 25.6%, and 32.0% have been used. In addition, for the sustainable operation of an APV system in terms of electricity generation as well as crop production, three climate change scenarios based on the shared socioeconomic pathway (SSP) are considered. The proposed framework identifies that the agrophotovoltaic system with 32% sharing ratio increases up to 20% of the total profit of normal farmland. As a result, the proposed framework enables the performance of an APV system under dynamic climate conditions to be accurately estimated, so that APV system designers can utilize it to identify a profitable long-term APV system.

An Agent-Based Framework for Policy Simulation: Modeling Heterogeneous Behaviors With Modified Sigmoid Function and Evolutionary Training

This article proposes an agent-based policy simulation framework that can be applied to the cases satisfying: 1) the agents try to maximize some intertemporal preference and 2) the impacts of different factors on agents’ behavioral tendency are monotonic. By combining the simulation and optimization methods, this framework balances the flexibility and validity of agent-based models (ABMs): the sigmoid function is modified and used to model agents’ decision-making rules, and the evolutionary training method is used to calibrate agents’ behavioral parameters. Based on an example for the emission trading scheme, the application of the framework is presented and evaluated in detail.

An agent-based simulation framework for developing the optimal rescue plan for older adults during the emergency evacuation

Timely evacuation and rescue in an emergency are significantly important to avoid serious injuries and death in a residential building. Previous studies regarding building emergency plans tended to adopt the data of the average population, while ignoring the elderly people with their sensory impairment, limited mobility, and a great potential for chronic disease, are more vulnerable to emergencies and thus have a high risk of death. Therefore, this study proposes an agent-based simulation framework that integrates the behaviour of both evacuees and rescuers to determine the optimal rescue plan for older adults requiring assistance in emergency evacuation procedures. A case study of a senior residence is provided to demonstrate the applicability of the framework for rescue plan formulation under different evacuation scenarios. Furthermore, a sensitivity analysis is conducted to explore the influence of rescuers’ delay time on evacuation and rescue performance and a method comparison is made to verify the effectiveness of the framework proposed in this study for rescue process optimization.

UNCERTAINTY VISUALIZATION FOR CHARACTERIZING HETEROGENEOUS HUMAN BEHAVIORS IN DISCRETE DYNAMICAL SYSTEM MODELS

When modeling human behavior in multi-player games, it is important to understand heterogeneous aspects of player behaviors. By leveraging experimental data and agent-based simulations, various data-driven modeling methods can be applied. This provides a great opportunity to quantify and visualize the uncertainty associated with these methods, allowing for a more comprehensive understanding of the individual and collective behaviors among players. For networked anagram games, player behaviors can be heterogeneous in terms of the number of words formed and the amount of cooperation among networked neighbors. Based on game data, these games can be modeled as discrete dynamical systems characterized by probabilistic state transitions. In this work, we present both Frequentist and Bayesian approaches for visualizing uncertainty in networked anagram games. These approaches help to elaborate how players individually and collectively form words by sharing letters with their neighbors in a network. Both approaches provide valuable insights into inferring the worst, average, and best player performance within and between behavioral clusters. Moreover, interesting contrasts between the Frequentist and Bayesian approaches can be observed. The knowledge and inferences gained from these approaches are incorporated into an agent-based simulation framework to further demonstrate model uncertainty and players’ heterogeneous behaviors.

Integrating shared autonomous vehicles in Last-Mile public transportation

While public transportation (PT) plays a critical role in urban mobility, the discomfort and the disutility of the last-mile trips make PT unattractive. We study the feasibility of shared autonomous vehicles (AV) as an on-demand alternative to fixed-route buses for last-mile travel. To this end, the bus routes that operate along the edges of the PT network in Bursa, Turkey are selected. The origin and destination (OD) pairs of the trips made on these bus routes are inferred at an individual level. The vehicle routing problem is then formulated by considering the proposed transportation service. Several solution methods are developed for both solution construction and solution improvement phases of the problem. To assess solution methods with real-world data, an agent-based simulation framework is built. It is found that the solution methods successfullysolved a dynamic problem. The results show that the integration of PT using AVs is well-suited to improve the service quality and energy efficiency in the last-mile mobility.

Retinal self-organization: a model of retinal ganglion cells and starburst amacrine cells mosaic formation.

Individual retinal cell types exhibit semi-regular spatial patterns called retinal mosaics. Retinal ganglion cells (RGCs) and starburst amacrine cells (SACs) are known to exhibit such layouts. Mechanisms responsible for the formation of mosaics are not well understood but follow three main principles: (i) homotypic cells prevent nearby cells from adopting the same type, (ii) cell tangential migration and (iii) cell death. Alongside experiments in mouse, we use BioDynaMo, an agent-based simulation framework, to build a detailed and mechanistic model of mosaic formation. We investigate the implications of the three theories for RGC's mosaic formation. We report that the cell migration mechanism yields the most regular mosaics. In addition, we propose that low-density RGC type mosaics exhibit on average low regularities, and thus we question the relevance of regular spacing as a criterion for a group of RGCs to form a RGC type. We investigate SAC mosaics formation and interactions between the ganglion cell layer (GCL) and inner nuclear layer (INL) populations. We propose that homotypic interactions between the GCL and INL populations during mosaics creation are required to reproduce the observed SAC mosaics' characteristics. This suggests that the GCL and INL populations of SACs might not be independent during retinal development.

An evaluation framework for operational interventions on urban mass public transport during a pandemic

Decision making in a rapidly changing context, such as the development and progression of a pandemic, requires a dynamic assessment of multiple variable and competing factors. Seemingly beneficial courses of action can rapidly fail to deliver a positive outcome as the context changes. In this paper, we present a flexible data-driven agent-based simulation framework that considers multiple outcome criteria to increase opportunities for safe mobility and economic interactions on urban transit networks while reducing the potential for Covid-19 contagion in a dynamic setting. Using a case study of the Victoria line on the London Underground, we model a number of operational interventions with varied demand levels and social distancing constraints including: alterations to train headways, dwell times, signalling schemes, and train paths. Our model demonstrates that substantial performance gains ranging from 12.3–195.7% can be achieved in metro service provision when comparing the best performing operational scheme and headway with those realised on the Victoria line during the pandemic.

On the Impact of Quarantine Policies and Recurrence Rate in Epidemic Spreading Using a Spatial Agent-Based Model

Pandemic outbreaks often determine swift global reaction, proven by for example the more recent COVID-19, H1N1, Ebola, or SARS outbreaks. Therefore, policy makers now rely more than ever on computational tools to establish various protection policies, including contact tracing, quarantine, regional or national lockdowns, and vaccination strategies. In support of this, we introduce a novel agent-based simulation framework based on: (i) unique mobility patterns for agents between their home location and a point of interest, and (ii) the augmented SICARQD epidemic model. Our numerical simulation results provide a qualitative assessment of how quarantine policies and the patient recurrence rate impact the society in terms of the infected population ratio. We investigate three possible quarantine policies (proactive, reactive, and no quarantine), a variable quarantine restrictiveness (0–100%), respectively, and three recurrence scenarios (short, long, and no recurrence). Overall, our results show that the proactive quarantine in correlation to a higher quarantine ratio (i.e., stricter quarantine policy) triggers a phase transition reducing the total infected population by over 90% compared to the reactive quarantine. The timing of imposing quarantine is also paramount, as a proactive quarantine policy can reduce the peak infected ratio by over ×2 times compared to a reactive quarantine, and by over ×3 times compared to no quarantine. Our framework can also reproduce the impactful subsequent epidemic waves, as observed during the COVID-19 pandemic, according to the adopted recurrence scenario. The suggested solution against residual infection hotspots is mobility reduction and proactive quarantine policies. In the end, we propose several nonpharmaceutical guidelines with direct applicability by global policy makers.

Crowdsourced on-demand food delivery: An order batching and assignment algorithm

Since the early 2010s, the meal delivery business went through a veritable revolution due to online food delivery platforms. By allowing customers to quickly order from a wide range of restaurants and outsourcing currently available couriers using their vehicles (crowdsourcing), this typology of service dynamically bridges demand and supply. The main goal of online food delivery platforms consists of matching couriers to meal orders within short time intervals to provide an efficient, reliable, and sustainable service. A way to increase efficiency consists of consolidating orders into batches, such that the same courier can serve several orders in multiple pickup and drop-off routes. Since such an assignment-batching problem becomes computationally prohibitively costly in real-world scenarios characterized by a large number of customer orders as well as uncertain demand and supply, heuristic solution methods come into play. This study proposes an order batching and assignment algorithm that leverages a graph-based approach after decomposing the original problem into more tractable sub-problems employing clustering. The solution is improved by local search moves and re-optimization procedures and integrated with advanced policies to improve solutions over time. An ‘Insertion policy’ aimed at increasing batch size, and a ‘Swap policy’ aimed at identifying more efficient assignments, are implemented and compared to a ‘Myopic policy’ that does not involve any re-optimization over time. An agent-based simulation framework is developed to implement dynamic policies where couriers’ operations and movements are realistically reproduced. The performance of the developed solution approach is tested through experiments based on a real-world case study. Results show that the algorithm allows for high-quality solutions in several configurations characterized by different demand and supply patterns (e.g., density levels, couriers availability) and problem sizes.In particular, the two advanced policies investigated considerably improve the solutions.

Optimization of demand-responsive transport: The rolling horizon approach

Demand-responsive Transport (DRT) is a popular topic in the field of transportation. The rolling horizon approach can be used to achieve an efficient operation of the DRT system in the context of trip pre-booking. In this study, a rolling horizon optimizer is implemented and used in an agent-based transport simulation framework. A series of experiments are performed to study the performance of the rolling horizon approach. The results of the experiments provide general guidelines on how to set up the rolling horizon optimizer for a DRT system. With a suitable setup, the rolling horizon approach demonstrates a clear advantage over the other approaches available in the simulation framework.

Resource-constrained replanning in MATSim applied to the simulation of peer-to-peer car sharing services

Peer-to-peer car sharing is a service model in which car owners let others use their vehicle while it is not in use by themselves. To simulate such a service in the agent-based transport simulation framework MATSim, a novel synchronization concept is proposed that considers limited resources in the decision-making of the agents. The concept is introduced in detail and demonstrated for peer-to-peer car sharing on a conceptual use case and a large-scale simulation for Berlin. The concept is generalizable to other constrained resources, such as parking or micromobility.

Equity Analysis of Freight Transportation Using a Large-Scale Agent-Based Modeling Framework

Freight transportation is one of the largest contributors to the transportation sector's greenhouse gases (GHG) and noise pollution. The adverse effects of the industry are not distributed equally due to large spatial disparities, with minority and low-income populations more prone to live near freight-related facilities and truck routes. However, equity analysis focusing on freight transport is limited since empirical analysis requires costly data collection processes, and analysis in large-scale areas is challenging. The principal objective of this study is to conduct an equity analysis of the impact of freight-related trips in the Atlanta-Chattanooga-Knoxville region using a large-scale agent-based simulation framework. The main results show that low-income (less than $52k) and more heavily non-white population neighbourhoods are disadvantaged neighbourhoods in terms of noise and GHG emissions. The results and methods presented in this paper can be used to provide tools to develop transportation equity and social justice analysis in large networks and can be used to develop policies and regulations to reduce the impact of freight transportation on disadvantaged populations.