Agent-based Microsimulation Model Research Articles

The health equity implications of the Health Resources and Services Administration's Ryan White HIV/AIDS Program.

Investigate the role of the Ryan White HIV/AIDS Program (RWHAP) - which funds services for vulnerable and historically disadvantaged populations with HIV - in reducing health inequities among people with HIV over a 10-year horizon. We use an agent-based microsimulation model to incorporate the complexity of the program and long-time horizon. We use a composite measure (the Theil index) to evaluate the health equity implications of the RWHAP for each of four subgroups (based on race and ethnicity, age, gender, and HIV transmission category) and two outcomes (probability of being in care and treatment and probability of being virally suppressed). We compare results with the RWHAP fully funded versus a counterfactual scenario, in which the medical and support services funded by the RWHAP are not available. The model indicates the RWHAP will improve health equity across all demographic subgroups and outcomes over a 10-year horizon. In Year 10, the Theil index for race and ethnicity is 99% lower for both outcomes under the RWHAP compared to the non-RWHAP scenario; 71-93% lower across HIV transmission categories; 31-44% lower for age; and 73-75% lower for gender. Given the large number of people served by the RWHAP and our findings on its impact on equity, the RWHAP represents an important vehicle for achieving the health equity goals of the National HIV/AIDS Strategy (2022-2025) and the Ending the HIV Epidemic Initiative goal of reducing new infections by 90% by 2030.

Modeling Crossing Behaviors of E-Bikes at Intersection With Deep Maximum Entropy Inverse Reinforcement Learning Using Drone-Based Video Data

The crossing behaviors modeling of non-networked road users can improve Connected and Autonomous Vehicles (CAVs)’s awareness of the imminent hazards in shared space while planning routes. In this study, an agent-based microsimulation model is utilized to simulate the crossing behavior of e-bikes which is difficult to model due to their greater maneuverability. To deeply understand the crossing mechanism of e-bikes, a customized neural network-based nonlinear reward function is developed from five dimensions, including travel efficiency, travel direction, travel destination, risk avoidance, and travel location. A Deep Maximum Entropy Inverse Reinforcement Learning(Deep MEIRL), which can recover the nonlinear reward function, is introduced to predict trajectories of e-bikes from the real drone-based video dataset, collected at the intersection of Jixiangcun, Xi’an (China). The results reveal that the Deep MEIRL can simulate the e-bike crossing trajectory more precisely, particularly in the microscopic behaviors of riders. Comparing Deep MEIRL with the baseline model MEIRL, it can be found that the nonlinear reward function designed in this paper is more advantageous in terms of continuous space modeling, with an improvement of 19.77%. Notably, Deep MEIRL outperforms MEIRL in modeling distance to potentially risk target states for left-and right-turn crossing behavior, improving Mean Absolute Error (MAE) by 71% and 30%, respectively. It means that the Deep MEIRL with a designed neural network is of great value to provide a logical result in modeling e-bike interaction with other road users. Therefore, the research is conducive to CAVs to understand e-bike behaviors in complex traffic scenarios, thereby assisting CAVs in making decisions efficiently.

An agent-based simulation to analyze trucking sector regulation policies

ABSTRACT This paper introduces an agent-based micro-simulation model to acquire the responses of road freight transportation systems like the productivity of trucks, the capability of road freight transportation companies, and the economic state of actors to different regulatory reforms. The shippers, freight companies, and drivers are the key decision-makers . These agents have different choices, including transport carrier choice, shipping price, truck allocation to shipment, and empty backhauls. Performance measures were used for the calibration phase with less than 3.7% average error. Finally, three viable regulatory reforms were evaluated, and their performance measures were estimated and discussed. We found that the shipping cost will reduce by 6% if a competitive market structure replaces the mandatory freight assignment procedure. Furthermore, we observed a 20% increase in the market share of the top fifty transport companies and a 3% improvement in the overall productivity when the physical freight companies’ branch requirements are relaxed.

Examining the long term impacts of COVID-19 using an integrated transport and land-use modelling system

ABSTRACT This paper investigates the long-term impacts of COVID-19 on transport and land-use systems through understanding changes in households’ long-term choices, such as residential location choice, travel tool ownership and type choice. For that, it first develops two possible scenarios, which are, (i) without COVID-19 pandemic, (ii) with COVID-19 pandemic. Specific behavioural attributes of households associated with each scenario are modelled and simulated up to the year 2030 within an integrated transport, land-use and energy (iTLE) model. iTLE is an agent-based microsimulation model developed for Nova Scotia, Canada. Results show that, if COVID-19 persists, most people who do not own a car will continue restraining themselves from purchasing one up to the year 2027. Most households having heads aged between 30 and 75 years and a yearly income above $100,000 will purchase new vehicles in year 2022, making the vehicle purchase rate doubled in the province. In terms of vehicle type choice decision, the use of SUVs will increase by 25% and 27% by 2025 and 2030, respectively, compared to year 2020. The pandemic will cause a 0%–74% increase in vehicle ownership in suburban areas by 2030. Additionally, the ability to ‘work from home’ and online school facilities may influence people to live further away from downtown. The results of this study will offer transport and land-use planners insights into how households’ long-term decision making may evolve in the future due to the COVID-19 crisis and help develop policies to continue focusing on the sustainability goals for communities.

An agent-based transportation impact sketch planning (TISP) model system

A traffic impact sketch planning (TISP) model is presented for the estimation of the likely travel demand generated by a major land-use development or redevelopment project. The proposed approach overcomes the problems with the non-behavioral transportation-related studies used in practice for assessing the development design impacts on the local transportation system. The architectural design of the development, in terms of the number and type of dwellings, by number of bedrooms per unit, and the land-use categories of the non-residential floorspace, are reflected in the TISP model through an integrated population and employment synthesis approach. The population synthesis enables the feasible deployment of an agent-based microsimulation (ABM) model system of daily activity and travel demand for a quick, efficient, and detailed assessment of the transportation impacts of a proposed neighborhood or development. The approach is not restricted to a certain type of dataset of the control variables for the geographic location of the development. Datasets for different geographic dimensions of the study area, with some common control variables, are merged and cascaded into a synthesized, disaggregate population of resident persons, households and jobs. The prototype implementation of the TISP model is for Waterfront Toronto’s Bayside Development Phase 2, using the operational TASHA-based GTAModel V4.1 ABM travel demand model system. While the conventional transportation studies focus on the assessment of the local traffic impacts in the immediate surroundings of the development, the TISP model investigates and assesses many transportation related impacts in the district, city, and region, for both residents and non-residents of the development. TISP model analysis includes the overall spatiotemporal trips distribution generated by the residents and non-residents of the development for the auto and non-auto mobility systems and the simulated agents diurnal peaking travel times. The model results are compared with the trips estimates by a prior project traffic impact study and the Institute of Transportation Engineers (ITE) Trip Generation Manual (TGM) rates of weekday trips for the relevant land uses. Future extensions and improvements of the model including the generalization and full automation of the model, and the bi-level macro-micro representation of the transportation network are also discussed.

A stochastic agent-based model of the SARS-CoV-2 epidemic in France.

Many European countries have responded to the COVID-19 pandemic by implementing nationwide protection measures and lockdowns1. However, the epidemic could rebound when such measures are relaxed, possibly leading to a requirement for a second or more, repeated lockdowns2. Here, we present results of a stochastic agent-based microsimulation model of the COVID-19 epidemic in France. We examined the potential impact of post-lockdown measures, including physical distancing, mask-wearing and shielding individuals who are the most vulnerable to severe COVID-19 infection, on cumulative disease incidence and mortality, and on intensive care unit (ICU)-bed occupancy. While lockdown is effective in containing the viral spread, once lifted, regardless of duration, it would be unlikely to prevent a rebound. Both physical distancing and mask-wearing, although effective in slowing the epidemic and in reducing mortality, would also be ineffective in ultimately preventing ICUs from becoming overwhelmed and a subsequent second lockdown. However, these measures coupled with the shielding of vulnerable people would be associated with better outcomes, including lower mortality and maintaining an adequate ICU capacity to prevent a second lockdown. Benefits would nonetheless be markedly reduced if most people do not adhere to these measures, or if they are not maintained for a sufficiently long period.

Role of Timetable, Rolling Stock Rescheduling, and Information Strategies to Passengers in Public Transport Disruptions

In the event of public transport disruption, operating companies produce disposition timetables depending on different rescheduling strategies, such as retiming or rerouting, with services fully/partially cancelled, and also taking into account more complex, adjusted, feasible rolling stock circulation. The aim is to reduce passengers’ delays, thereby limiting detriment to passengers’ activities and their related satisfaction. The key relation between the supply of operating companies and passengers’ satisfaction is information disseminated about running services. This paper innovatively combines an optimization model and an agent-based micro-simulation model (MATSim) to explore passengers’ (dis)satisfaction with different disposition timetables and information strategies, which is helpful for operating companies to offer better services to passengers in cases of public transport disruption. Activity-based agent behaviors in a multi-modal network are simulated and agents’ delays and scores for the city of Zürich, Switzerland, analyzed. Passengers’ (dis)satisfaction is indicated by their delays in the directly affected (i.e., disrupted) trip and utility for their whole trips and activities estimated by a score function. Disruption results in immediate delays for passengers whose planned services fail to run, plus delays for passengers on the line where extra services are planned to run (rerouted). The earlier information on the disposition timetable is disseminated to passengers, the higher their satisfaction during disruption. Compared with full cancellation of train services, computing a precise feasible rolling stock circulation able to handle partial train cancellations can significantly benefit passengers, especially those whose planned services are disrupted, against minor delays incurred by other group of passengers.

The role of information availability to passengers in public transport disruptions: An agent-based simulation approach

In public transport disruptions, a key relation between the services of operating company and passengers’ satisfaction is the disseminated information. This paper proposes rigorous mathematical descriptions to describe the effects of information availability to passengers, including the user equilibrium and non-equilibrium solutions. The information availability in disruption is summarised in a “who-when-where-what” four-dimensional framework. Based on different information, passengers’ behaviours are assumed and simulated to evaluate the benefits of information availability in public transport disruption. An agent-based micro-simulation model (MATSim) is applied for the city of Zürich, Switzerland, for the benefit of activity-based simulation in a multi-modal network. We use an existing day-to-day replanning method, and extend the study with a within-day replanning approach in MATSim, to study agents’ route choices responding to public transport disruption in one single iteration. The disruption is assumed as a rail track blockage between Zürich HB and Zürich Oerlikon via both Zürich Wipkingen and Zürich Hardbrücke. One benchmark of agents’ behaviours without disruption and other three scenarios based on different information are simulated. Statistic results are analysed for all the agents who may be involved in the defined disruption. Agents’ flow in related transit routes and transport modes shows their adaptations to the corresponding information availability. Agents’ delays and scores reveal that information significantly impacts agents’ satisfaction in public transport disruption.

How the issue time of information affects passengers in public transport disruptions: an agent-based simulation approach

Abstract In public transport disruptions, a key relation between the services of operating company and passengers’ satisfaction is the disseminated information. This paper focus on how the issue time of information affects passengers in the unplanned/unexpected public transport disruptions. Due to different information issue time, passengers’ behaviours are assumed and simulated. An agent-based micro-simulation model (MATSim) is applied for the city of Zurich, Switzerland, for the benefit of heterogeneous activity-based simulation in a multi-modal network. We develop a novel within-day replanning approach in MATSim, to study agents’ route choices responding to public transport disruptions in one single iteration. The disruption is assumed as a rail track blockage between Zurich HB and Zurich Oerlikon via both Zurich Wipkingen and Zurich Hardbrucke. Statistic results are analysed for all the agents who may be involved in the defined disruption. Agents’ travel chain shows their adaptations to the corresponding information scenarios. Agents’ scores reveal that the issue time of information significantly impacts agents’ satisfaction in public transport disruption. In other terms, an investment in better information might be complementary and comparable in an investment in a better management of the operations during the disruption, in interconnected multimodal networks.

A microeconomic framework for integrated agent-based modelling of activity-travel patterns and energy consumption

Abstract The sophistication in the demand management approaches in both transport and energy sectors and their interaction call for modelling approaches that consider both sectors jointly. For agent-based microsimulation models of travel demand and energy consumption, this implies the necessity to ensure consistent representation of user behaviour with respect to mobility and energy consumption behaviours across the model components. Therefore this paper proposes a microeconomic framework, termed the HOT model (Home, Out-of-home, Travel) grounded in the goods-leisure paradigm, but extended to incorporate emerging activity-travel behaviour patterns and their energy consumption implications. We discuss how the model can be operationalised and embedded within agent-based frameworks with a case study using time use and energy consumption data from the UK.

A Microsimulation Model for the Land Rental Market in Irish Farming

In this paper, we utilize Teagasc National Farm survey data to analyse the agricultural land rental market in Ireland with a newly developed agent-based microsimulation model. This model is compared to an equilibrium model of the land rental market. We conclude that the microsimulation model has a number of advantages over the equilibrium model in addressing the interactions between landowners and tenants and dealing with farm size concentration. The equilibrium model retains some advantages in dealing with the question of price determination and in illustrating the potential surpluses to be gained from a more active land rental market.

Microsimulating labour market job-worker matching

This paper introduces a new agent-based microsimulation (ABM) model of urban labour markets, in which workers actively seeking employment in each time period are matched with vacant jobs. The model is designed to operate within the Integrated Land Use, Transportation, Environment (ILUTE) urban simulation model system. In the current model application, 1986 is taken as the base year, with 20-year simulations being run (1986–2006) to test the model’s performance within a known historical time-period.

Agent-based microsimulation conceptual model for urban freight distribution

Abstract In this paper, we develop a conceptual model that integrates the microsimulation and the multi-agent systems in the urban goods distribution processes. The proposed model, which it is defined between the agent-based microsimulation models, allows to analyze the different behaviors and actions that are the result of an urban freight distribution process, facilitating the decision making to improve the efficiency of the logistic process. The model is formulated as a multi-layer urban freight distribution network which involve the street city network and the microsimulation of passenger trips, the use of a distribution center, the design of initial static pick-up and delivery routes, the behaviors of the agents of the system and their interactions that give feedback to the model, and finally assess the impact of the agents in the improvement of the re-routing optimization process at urban freight distribution, according to the multiple types of dynamics variables such as dynamic travel time by traffic conditions in different travels.

Overview of a labour market microsimulation model

This paper introduces a new agent-based microsimulation (ABM) model of urban labour markets, in which workers actively seeking employment in each time period are matched with vacant jobs. The model is designed to operate within the ILUTE (Integrated Land Use, Transportation, Environment) urban simulation model system. In the current model application, 1986 is taken as the base year, with 20-year simulations being run (1986-2006) to test the model’s performance within a known historical time-period.

Modeling the Demand for New Transportation Services and Technologies

This paper briefly discusses the current state of the art of urban travel demand modeling and research needs in the field. Special emphasis is given to both the challenges and the opportunities posed by modern information technology and the data, transportation services, and travel behaviors that this technology is generating. Travel demand modeling has made very significant strides over the past 20 years, especially in the development of operational activity- and tour-based regional travel demand forecasting systems. These model systems represent first-generation agent-based microsimulation models. Considerable need, opportunity, and scope exist for the development of significantly more powerful second-generation agent-based microsimulation models that build upon emerging big data sets (among other information sources) and high-performance computing. This task, however, will involve the development of new behavioral representations and computational algorithms implemented within much more flexible software environments that both fully exploit available computing power and enable flexible experimentation with and extension of representations of new transportation modes and services and evolving travel behavior.

Cost-Effective Siting of Electric Vehicle Charging Infrastructure With Agent-Based Modeling

Plug-in electric vehicles (PEVs) represent a significant opportunity for governments to reduce emissions of both air pollutants and greenhouse gases, in addition to reduce their dependency on foreign sources of energy. Comprehensive planning analysis prior to the rollout of electric vehicle charging stations (EVCS) can ensure that charging stations are effectively sited, providing the best returns on investment while also meeting critical service requirements. We present a detailed description of the PEV infrastructure (PEVI) model, a spatially explicit agent-based microsimulation model that represents charging infrastructure, charging behavior, competition for scarce EVCS, and driver adaptation. A differential evolution and a heuristic optimization scheme are compared in their ability to find a cost-effective distribution of EVCS. In addition, several key assumptions of the model are tested for their impact on critical outcomes. Results are presented from a case study in Delhi, India, that highlight the spatial distribution of chargers of different levels, the impact of several technical and policy trends on the need for EVCS, the spatiotemporal distribution of charging demand, and the technical potential for load shifting PEV demand.

Prevention of Hepatitis C by Screening and Treatment in U.S. Prisons.

The prevalence of hepatitis C virus (HCV) in U.S. prisoners is high; however, HCV testing and treatment are rare. Infected inmates released back into society contribute to the spread of HCV in the general population. Routine hepatitis screening of inmates followed by new therapies may reduce ongoing HCV transmission. To evaluate the health and economic effect of HCV screening and treatment in prisons on the HCV epidemic in society. Agent-based microsimulation model of HCV transmission and progression of HCV disease. Published literature. Population in U.S. prisons and general community. 30 years. Societal. Risk-based and universal opt-out hepatitis C screening in prisons, followed by treatment in a portion of patients. Prevention of HCV transmission and associated disease in prisons and society, costs, quality-adjusted life-years (QALYs), incremental cost-effectiveness ratio (ICER), and total prison budget. Implementing risk-based and opt-out screening could diagnose 41,900 to 122,700 new HCV cases in prisons in the next 30 years. Compared with no screening, these scenarios could prevent 5500 to 12,700 new HCV infections caused by released inmates, wherein about 90% of averted infections would have occurred outside of prisons. Screening could also prevent 4200 to 11,700 liver-related deaths. The ICERs of screening scenarios were $19,600 to $29,200 per QALY, and the respective first-year prison budget was $900 to $1150 million. Prisons would require an additional 12.4% of their current health care budget to implement such interventions. Results were sensitive to the time horizon, and ICERs otherwise remained less than $50,000 per QALY. Data on transmission network, reinfection rate, and opt-out HCV screening rate are lacking. Universal opt-out HCV screening in prisons is highly cost-effective and would reduce HCV transmission and HCV-associated diseases primarily in the outside community. Investing in U.S. prisons to manage hepatitis C is a strategic approach to address the current epidemic. National Institutes of Health.

A Bayesian network approach for population synthesis

Agent-based micro-simulation models require a complete list of agents with detailed demographic/socioeconomic information for the purpose of behavior modeling and simulation. This paper introduces a new alternative for population synthesis based on Bayesian networks. A Bayesian network is a graphical representation of a joint probability distribution, encoding probabilistic relationships among a set of variables in an efficient way. Similar to the previously developed probabilistic approach, in this paper, we consider the population synthesis problem to be the inference of a joint probability distribution. In this sense, the Bayesian network model becomes an efficient tool that allows us to compactly represent/reproduce the structure of the population system and preserve privacy and confidentiality in the meanwhile. We demonstrate and assess the performance of this approach in generating synthetic population for Singapore, by using the Household Interview Travel Survey (HITS) data as the known test population. Our results show that the introduced Bayesian network approach is powerful in characterizing the underlying joint distribution, and meanwhile the overfitting of data can be avoided as much as possible.

An agent-based evaluation of impacts of transport developments on the modal shift in Tehran, Iran

Changes in travel modes used by people, particularly reduction of the private car use, is an important determinant of effectiveness of transportation plans. Because of dependencies between the choices of residential location and travel mode, integrated modelling of these choices has been proposed by some researchers. In this paper, an agent-based microsimulation model has been developed to evaluate impacts of different transport development plans on choices of residential location and commuting mode of tenant households in Tehran, the capital of Iran. In the proposed model, households are considered as agents who select their desired residential location using a constrained NSGA-II algorithm and in a competition with other households. In addition, they choose their commuting mode by applying a multicriteria decision-making method. Afterwards, effects of development of a new highway, subway and bus rapid transit (BRT) line on their residential location and commuting mode choices are evaluated. Results show that despite the residential self-selection effects, these plans result in considerable changes in the commuting mode of different socio-economic categories of households. Development of the new subway line shows promising results by reducing the private car use among the all socio-economic categories of households. But the new highway development unsatisfactorily results in increase in the private car use. In addition, development of the new BRT line does not show significant effects on the commuting mode change, particularly on decrease in the private car use.

Synthesizing Population for Microsimulation-based Integrated Transport Models Using Atlantic Canada Micro-data

Due to the lack of availability of micro-data of population characteristics, the synthesis of individual and household attributes is a necessary step for developing a disaggregate, dynamic travel demand forecasting model. Agent-based micro-simulation models attempt to forecast travel behaviour of individuals and households by simulating the behaviour of the singular actors in the system. The framework for generating synthesis population presented in this paper is a fundamental contribution to the development of an Integrated Transport, Land Use and Environment Modelling System in Nova Scotia, Canada. In this paper, a population is synthesized for individuals and households in Atlantic Canada using the Fitness Based Synthesis (FBS) approach. A synthetic algorithm is designed that allows both individual and household attribute levels to synthesize simultaneously. Unequal probabilities based on the sampling weight are used in the household selection step of the algorithm. In this way, the performance and accuracy of the synthetic population produced has been improved. The synthetic algorithm is tested for two functions: first, using the one level (household) control tables; and second, using two levels (individual and household) control tables. The data used in this study is collected from the 2006 Canadian Census and the 2006 Public Use Micro-data File (PUMF). The algorithm is implemented using a high-level matrix programming language for numerical computation in MATLAB. The results show that the synthetic population with both individual and household level attributes has the best fitness value.