Agent-based Simulation Tool Research Articles

Quantifying the external costs of autonomous on-demand ride pooling services

Mobility On Demand (MOD) services, such as ride-pooling, provide convenient and cost-effective transportation options. While previous studies focused on operational costs and service quality, we take a broader perspective by examining the external costs associated with autonomous ride-pooling services. Incorporating external costs into the design and evaluation of MOD services enables a comprehensive understanding of their impact on the entire urban population, informing effective regulations and incentives. We present an approach for calculating space-varying external costs, accounting for factors like air pollution, climate impact, noise and accidents. These costs are integrated into FleetPy, an agent-based simulation tool for ridesharing analysis and optimization. A case study in Munich uncovers the tradeoffs between external costs, internal costs, and service quality. Our findings suggest that mid-sized vehicles with a three-person capacity strike a balance between energy efficiency and transport capacity. By applying our approach, external costs can be reduced by up to 37%.

Agent-based decision-support model for bus route redesign in networks of small cities and towns: case study of Agder, Norway

Small cities and towns often struggle to provide high-quality public transport services to daily commuters. This is reflected in the modal split, where the share of car users dominates. Such a problem requires a modern solution, where transport planners can verify the impact of potential transport network improvements on the travel behavior of the residents before the changes are actually deployed. This study aims to demonstrate the usefulness of employing an agent-based simulation tool in the decision process for redesigning an express service regional bus route connecting a network of small cities and towns. The model was initially developed as a Mobility as a Service simulation solution for suburban areas of European metropolises. The model is adapted and applied to a case study for the region of Agder, Norway, simulating the impact of nine different scenarios on the patronage of a specific bus route. The simulation model proposes to upgrade the classic agent structure to a persona profile designed specifically for the case study. The main objective of this research is to identify the scenario that maximizes patronage while minimizing total route travel time and additional costs. The results suggest that the proposed model can be successfully adapted from suburban metropolitan areas to the realities of the considered case study, and potentially other similar regions. Specifically, out of the nine proposed scenarios, the model identified four promising ones. One of the four scenarios also fits the cost constraints imposed by the transport provider. The model provides a solid approach for analyzing complex transport systems that are practically impossible to consider in detail if the analysis is done without computer support. Thus, the results can be used as a decision support system for public transport planning and operations in networks of small cities and towns.

The biological cropping hypothesis over evolutionary time: an experimental test

Ecological disturbance has been proposed to have a variety of effects on biodiversity. These mechanisms are well studied over shorter timescales through experimental manipulation of ecosystems, but the effect of disturbance on longer timescales, where evolutionary processes operate, is less well understood. This is at least in part because evolutionary processes are too slow to observe in experimental manipulations of ecosystems. Here, we use the Rapid Evolutionary Simulator system (REvoSim) to solve this problem. REvoSim is a spatially explicit, agent-based simulation tool that models both ecological and evolutionary processes and is capable of simulating many thousands of generations of evolution per hour in a population of up to 1 million organisms. We use REvoSim to evaluate the biological cropping hypothesis, which predicts that the non-selective culling of organisms from populations (“cropping”) can enhance diversity in those populations over long timescales by reducing the homogenising effects of competitive exclusion. Our experiments demonstrate that cropping alone can increase diversity within populations under certain circumstances: those where it has the net effect of reducing the selection pressure acting on those populations. However, intense cropping pressure may increase the selection pressure on organisms to reproduce rapidly, potentially offsetting the effects of reduced competitive exclusion on diversity. We also show that cropping alone is not sufficient to result in reproductive isolation within populations. This implies that, while cropping can maintain a high species diversity within an ecosystem, additional mechanisms must be invoked to generate that high diversity in the first instance.

Trucks Tour Chaining for Electric Demand Estimation

We propose an efficient truck trip chaining method to model freight fleet electrification using an agent-based simulation tool to simulate passenger and truck tours. The estimated truck trajectories are used to estimate vehicle energy consumption and emissions. The electricity demand estimate suggests that passenger vehicles have a significantly higher demand due to higher adoption and a larger fleet. Therefore, the truck fleet electrification efforts would not significantly affect the grid, but its adoption rates, along with passenger vehicle electrification, would cause issues.

Developing an agent-based microsimulation for predicting the Bus Rapid Transit (BRT) demand in developing countries: A case study of Dhaka, Bangladesh

Bus Rapid Transit (BRT) has been widely recognised as an affordable and effective mass transport system that can solve various mobility issues in countries that are unable to afford rail-based mass transit options. However, it is extremely challenging to predict the demand for the first BRT service in a city of a developing country with a weak public transport system using aggregate models, given the radical difference in the level of service between the BRT and the existing modes. Further, there can be substantial changes in the activity and travel patterns in a city after the introduction of the BRT which simpler disaggregate level analysis tools are unable to predict. Agent-based simulation tools, which are the state-of-the-art tools for simulating complex travel behaviour, are hence more appropriate for predicting the network conditions after the introduction of a new BRT system. But the application of such simulation tools has been primarily limited to developed countries where the transport landscape and the travel behaviour are very different from the developing countries. To address this gap, this paper presents a demand forecasting model for BRT and integrates it into an activity-based micro-simulation tool in the context of Dhaka, the capital of Bangladesh and one of the fastest growing megacities in the world. The model was developed based on an existing multi-agent, activity-based, travel demand simulator (MATSim). The MATSim implementation in the context of Dhaka focused on two aspects: (1) implementing behaviour models in MATSim to reflect the mode choice in the presence of the proposed BRT (2) integrating multiple data sources (including stated-preference data) for calibrating the mode choice and other components of MATSim to realistically mimic the travel behaviour in the city. Once calibrated, different access scenarios for BRT were simulated using MATSim, and the sensitivity of the outputs to different modelling assumptions is tested. Results from the simulation showed that the marginal utility of travel time, travel cost, and pricing structure of BRT significantly influenced BRT travel demands. Also, BRT demand was found to be the highest (25% of the total trips) in the scenario with multi-modal access/egress connections. While such direct model outputs presented in this paper will be useful for the planners to maximise the ridership of the proposed BRT, the calibrated simulator will be also useful for the evaluation of other innovative transport modes in the context of Dhaka in the future.

First-mile reverse logistics: An agent-based modelling and simulation application for glass bottle recovery

Distribution networks aim to get the right products to consumers at the right time and in the right place. When consumers return these products, another type of logistics network is involved. This function of returning end-of-life products is referred to as reverse logistics. A critical characteristic of these reverse logistics networks is the dependence of material flow on the voluntary action of the consumer. Therefore, the success of an end-of-life collection strategy depends on the ability to anticipate the collection rate of end-of-life products at specific locations by understanding the factors that influence consumer behavior. However, there is no established tool or methodology for assessing the impact of consumer decisions on reverse logistics material flows. Therefore, this paper proposes an agent-based simulation tool to predict the performance of a reverse logistics network, taking into account the location of collection points, socio-demographic information, attitudes towards sustainable development, as well as the consumption profile. More specifically, this paper presents the configuration, calibration and validation of a simulation tool modelling the return of empty glass bottles in a small group of municipalities in Québec, Canada. Experiments also demonstrate the tool's ability to provide useful information to support the design of collection strategies that maximize return rates while minimizing installation and operating costs by optimizing the number of drop-off depots in the reverse logistics network.

Review of simulation software for cyber-physical production systems with intelligent distributed production control

ABSTRACT Many intelligent distributed production control architectures have been developed for cyber-physical production systems (CPPSs), but the difficulty in predicting performance has hindered acceptance by industry. Performance predictions for systems with conventional control can be made by simulating product flow using discrete-event simulation (DES) software. However, DES is inadequate for capturing the intricacies of intelligent distributed production control architectures. Alternatively, agent-based simulation (ABS) software is more effective for capturing distributed intelligence. A hybrid discrete-event and agent-based simulation tool combines the strengths of both approaches, making it effective for capturing the intertwined physical and cyber layers of a CPPS. In this paper, a review is carried out to determine which off-the-shelf simulation tools are capable of using hybrid discrete-event and agent-based simulation for performance predictions in the conceptual design phase of a CPPS. This review is carried out using the following structured steps. First, the scope and evaluation criteria are identified. Next, a selection of simulation tools is collected. The selected tools are then evaluated and classified. Finally, the most promising simulation tool according to this evaluation – Anylogic – is subjected to a case study to assess if hybrid simulation can be used to predict the performance of a CPPS.

Experimenting with Agent-Based Model Simulation Tools

Agent-based models (ABMs) are one of the most effective and successful methods for analyzing real-world complex systems by investigating how modeling interactions on the individual level (i.e., micro-level) leads to the understanding of emergent phenomena on the system level (i.e., macro-level). ABMs represent an interdisciplinary approach to examining complex systems, and the heterogeneous background of ABM users demands comprehensive, easy-to-use, and efficient environments to develop ABM simulations. Currently, many tools, frameworks, and libraries exist, each with its characteristics and objectives. This article aims to guide newcomers in the jungle of ABM tools toward choosing the right tool for their skills and needs. This work proposes a thorough overview of open-source general-purpose ABM tools and offers a comparison from a two-fold perspective. We first describe an off-the-shelf evaluation by considering each ABM tool’s features, ease of use, and efficiency according to its authors. Then, we provide a hands-on evaluation of some ABM tools by judging the effort required in developing and running four ABM models and the obtained performance.

Finding and Monitoring Number of Living Entities through Unmanned Aeria

The rapid development of Unmanned Aerial Vehicles UAVs technology opens many new application areas. One of these applications is remote monitoring for living entities (Humans or Animals) within a known area for safety and protection purposes. UAVs are programmable to carry missions according to a specified algorithm. Their ability of movements and actions provide solutions with a reasonable cost. However, how a set of UAVs doing a mission behave cooperatively need to be tested and evaluated in a lab with simulation before moving to operation field. In this paper, a model for scanning determined area using a set of cooperative UAVs is developed and tested. The objective is reducing operation time. The set of affecting parameters is examined. The model is simulated using NetLogo agent-based simulation tool. The results of the experiment of a various settings are conducted.

Assessing the effectiveness and the economic impact of evacuation: the case of the island of Vulcano, Italy

Abstract. Evacuation planning and management represent a key aspect of volcanic crises because they can increase people's protection as well as minimize potential impacts on the economy, properties and infrastructure of the affected area. We present an agent-based simulation tool that assesses the effectiveness of different evacuation scenarios using the small island of Vulcano (southern Italy) as a case study. Simulation results show that the overall time needed to evacuate people should be analysed together with the percentage of people evacuated as a function of time and that a simultaneous evacuation on Vulcano is more efficient than a staged evacuation. For example, during the touristic (high) season between July and August, even though the overall duration is similar for both evacuation strategies, after ∼ 6 h about 96 % of people would be evacuated with a simultaneous evacuation, while only 86 % would be evacuated with a staged evacuation. We also present a model to assess the economic impact of evacuation as a function of evacuation duration and of the starting period with respect to the touristic season. It reveals that if an evacuation lasting 3 to 6 months was initiated at the beginning or at the end of the touristic season (i.e. June or November), it would cause a very different economic impact on the tourism industry (about 78 %–88 % and 2 %–7 % of the total annual turnover, respectively). Our results show how the assessment of evacuation scenarios that consider human and economic impact carried out in a pre-disaster context helps authorities develop evacuation plans and make informed decisions outside the highly stressful time period that characterizes crises.

Modeling COVID-19 Outbreaks in Long-Term Care Facilities Using an Agent-Based Modeling and Simulation Approach.

The elderly, especially those individuals with pre-existing health problems, have been disproportionally at a higher risk during the COVID-19 pandemic. Residents of long-term care facilities have been gravely affected by the pandemic and resident death numbers have been far above those of the general population. To better understand how infectious diseases such as COVID-19 can spread through long-term care facilities, we developed an agent-based simulation tool that uses a contact matrix adapted from previous infection control research in these types of facilities. This matrix accounts for the average distinct daily contacts between seven different agent types that represent the roles of individuals in long-term care facilities. The simulation results were compared to actual COVID-19 outbreaks in some of the long-term care facilities in Ontario, Canada. Our analysis shows that this simulation tool is capable of predicting the number of resident deaths after 50 days with a less than 0.1 variation in death rate. We modeled and predicted the effectiveness of infection control measures by utilizing this simulation tool. We found that to reduce the number of resident deaths, the effectiveness of personal protective equipment must be above 50%. We also found that daily random COVID-19 tests for as low as less than 10% of a long-term care facility’s population will reduce the number of resident deaths by over 75%. The results further show that combining several infection control measures will lead to more effective outcomes.

The response of sleeping adults to smoke alarm signals in the Evacuation Decision Model

Simulating evacuation behaviour in fire emergencies is an increasingly important method for assessing design to mitigate the likelihood of life losses. To date, several approaches have been used to predict the pre-evacuation time (i.e. time between the first alarm or other initial cue until the moment an evacuee starts moving toward a safe place) using distribution functions or evacuation decision models. In this work a modified version of the Evacuation Decision Model (EDM) is developed and incorporated into an agent-based egress simulation tool. The paper identifies input distributions for the characteristic time to reach an investigating state when an agent receives an alarm alert using waking time measurements from the literature. The results from EDM simulations are compared to the New Zealand verification method (C/VM2) pre-travel activity times. Exceedance probabilities are identified for scenarios in which occupants are sleeping in a familiar building and exposed to a ‘standard’ alarm or sleeping in an unfamiliar building and exposed to a ‘standard’ or voice alarm. A corresponding pre-evacuation time for a scenario in which occupants are sleeping, familiar with the building and exposed to a voice alarm signal is forecast using EDM.

A Cellular Automata Agent-Based Hybrid Simulation Tool to Analyze the Deployment of Electric Vehicle Charging Stations

We present a hybrid model combining cellular automata (CA) and agent-based modeling (ABM) to analyze the deployment of electric vehicle charging stations through microscopic traffic simulations. This model is implemented in a simulation tool called SIMTRAVEL, which allows combining electric vehicles (EVs) and internal combustion engine vehicles (ICEVs) that navigate in a city composed of streets, avenues, intersections, roundabouts, and including charging stations (CSs). Each EV is modeled as an agent that incorporates complex behaviors, such as decisions about the route to destination or CS, when to drive to a CS, or which CS to choose. We studied three different CS arrangements for a synthetic city: a single large central CS, four medium sized distributed CSs or multiple small distributed CSs, with diverse amounts of traffic and proportions of EVs. The simulator output is found to be robust and meaningful and allows one to extract a first useful conclusion: traffic conditions that create bottlenecks around the CSs play a crucial role, leading to a deadlock in the city when the traffic density is above a certain critical level. Our results show that the best disposition is a distributed network, but it is fundamental to introduce smart routing measures to balance the distribution of EVs among CSs.

Simulating preventative testing of SARS-CoV-2 in schools: policy implications

BackgroundSchool testing for SARS-CoV-2 infection has become an important policy and planning issue as schools were reopened after the summer season and as the COVID-19 pandemic continues. Decisions to test or not to test and, if testing, how many tests, how often and for how long, are complex decisions that need to be taken under uncertainty and conflicting pressures from various stakeholders.MethodWe have developed an agent-based model and simulation tool that can be used to analyze the outcomes and effectiveness of different testing strategies and scenarios in schools with various number of classrooms and class sizes. We have applied a modified version of a standard SEIR disease transmission model that includes symptomatic and asymptomatic infectious populations, and that incorporates feasible public health measures. We also incorporated a pre-symptomatic phase for symptomatic cases. Every day, a random number of students in each class are tested. If they tested positive, they are placed in self-isolation at home when the test results are provided. Last but not least, we have included options to allow for full testing or complete self-isolation of a classroom with a positive case.ResultsWe present sample simulation results for parameter values based on schools and disease related information, in the Province of Ontario, Canada. The findings show that testing can be an effective method in controlling the SARS-CoV-2 infection in schools if taken frequently, with expedited test results and self-isolation of infected students at home.ConclusionsOur findings show that while testing cannot eliminate the risk and has its own challenges, it can significantly control outbreaks when combined with other measures, such as masks and other protective measures.

Simulating Preventative Testing of SARS-CoV-2 in Schools: Policy Implications

Background: School testing for SARS-CoV-2 infection has become an important policy and planning issue as schools are reopened after the summer season and as the COVID-19 pandemic continues. Decisions to test or not to test and, if testing, how many tests, how often and for how long, are complex decisions that need to be taken under uncertainty and conflicting pressures from various stakeholders.Method: We have developed an agent-based model and simulation tool that can be used to analyze the outcomes and effectiveness of different testing strategies and scenarios in schools with various number of classrooms and class sizes. We have applied a modified version of a standard SEIR disease transmission model that includes symptomatic and asymptomatic infectious populations, and that incorporates feasible public health measures. We also incorporated a pre-symptomatic phase for symptomatic cases. Every day, a random number of students in each class are tested. If they tested positive, they are placed in self-isolation at home when the test results are provided. Last but not least, we have included options to allow for full testing or complete self-isolation of a classroom with a positive case.Finding: We present sample simulation results for parameter values based on schools and disease related information, in the Province of Ontario, Canada. The findings show that testing can be an effective method in controlling the SARS-CoV-2 infection in schools if taken frequently, with expedited test results and self-isolation of infected students at home. Our findings show that while testing cannot eliminate the risk and has its own challenges, it can significantly control outbreaks when combined with other measures, such as masks and other protective measures.Funding Statement: Public Health Agency of Canada, Canadian Institute of Health Research, Ontario Research Funds, National Science and Engineering Research Council of Canada.Declaration of Interests: None to declare.

Teaching Market-Driven Engineering Design with an Agent-Based Simulation Tool

In product design, there is often a disconnect between the engineers creating the product and the marketing team determining the best characteristics for the product. The research areas of “design for market systems” and “decision-based design” seek to bridge that disconnect through quantitative approaches that facilitate simultaneous technical design and marketing decisions. However, these market driven design frameworks have been primarily evaluated in the context of industry case studies, with limited integration into engineering education. This article presents the development and implementation of a simulation tool for teaching market-driven design in undergraduate engineering design courses. This classroom tool demonstrates the basic relationships between product design decisions, pricing and marketing choices, and predicted market success, by simulating the interactions among producers and consumers in a market system. The product attributes influence the cost and consumer utility of the product, which, along with price, affect market dynamics. The simulator was implemented in a third-year undergraduate design course to introduce the concept of market-driven product design, allowing student design teams to assess the impacts of different design variables on the market success of their design projects. Surveys and written reflections by the students were used to evaluate the simulator’s value in contributing to self-reported learning. The results showed that a majority of students expressed that the simulator provides a meaningful and engaging way to learn about market-driven design concepts.

A Survey of Agent-Based Modelling and Simulation Tools for Educational Purpose

A Survey of Agent-Based Modelling and Simulation Tools for Educational Purpose

Analysing the Combined Health, Social and Economic Impacts of the Corovanvirus Pandemic Using Agent-Based Social Simulation

During the COVID-19 crisis there have been many difficult decisions governments and other decision makers had to make. E.g. do we go for a total lock down or keep schools open? How many people and which people should be tested? Although there are many good models from e.g. epidemiologists on the spread of the virus under certain conditions, these models do not directly translate into the interventions that can be taken by government. Neither can these models contribute to understand the economic and/or social consequences of the interventions. However, effective and sustainable solutions need to take into account this combination of factors. In this paper, we propose an agent-based social simulation tool, ASSOCC, that supports decision makers understand possible consequences of policy interventions, but exploring the combined social, health and economic consequences of these interventions.

An agent-based simulation framework for the study of urban delivery

In recent years, cities and especially urban mobility have undergone remarkable changes. Significant advances in technology have been translated into new mobility services for both goods and people. One evident change has been the transformation of traditional vehicle fleets into more open fleets, in the sense that their members can proactively decide whether or not they are part of a certain fleet and whether or not they perform certain services. Fleets of this type make the decision-making process to be highly distributed, and rule out some of the typically centralized decisions. The management and control of this type of open fleets is severely more complex and, for this reason, the availability of simulation tools that allow for their analysis can be very useful. In accordance with this, the main contribution of this work is the development of an agent-based simulation tool specifically designed for the simulation of new urban mobility models. In this way, the tool can simulate any type of fleet in different urban scenarios, including a solution of the Last Mile Delivery problem, which is also included as a proof of concept in this paper.

Operational benefits and challenges of shared-ride automated mobility-on-demand services

Abstract This paper presents a quantitative analysis of the operations of shared-ride automated mobility-on-demand services (SRAMODS). The study identifies (i) operational benefits of SRAMODS including improved service quality and/or lower operational costs relative to automated mobility-on-demand services (AMODS) without shared rides; and (ii) challenges associated with operating SRAMODS. The study employs an agent-based stochastic dynamic simulation framework to model the operational problems of AMODS. The agents include automated vehicles (AVs), on-demand user requests, and a central AV fleet controller that can dynamically change the plans (i.e. routes and AV-user assignments) of AVs in real-time using optimization-based control policies. The agent-based simulation tool and AV fleet control policies are used to test the operational performance of AMODS under a variety of scenarios. The first set of scenarios vary user demand and a parameter constraining the maximum user detour distance. Results indicate that even with a small maximum user detour distance parameter value, allowing shared rides significantly improves the operational efficiency of the AV fleet, where the efficiency gains stem from economies of demand density and network effects. The second set of scenarios vary the mean and coefficient of variation of the curbside pickup time parameter; i.e. how long an AV must wait curbside at a user’s pickup location before the user gets inside the AV. Results indicate that increases in mean curbside pickup time significantly degrade operational performance in terms of user in-vehicle travel time and user wait time. The study quantifies the total system (user plus fleet controller) cost as a function of mean curbside pickup time. Finally, the paper provides an extensive discussion of the implications of the quantitative analysis for public-sector transportation planners and policy-makers as well as for mobility service providers.