Agent-based Simulation Approach Research Articles

The role of EV fast charging in the urban context: An agent-based model approach

Using an agent-based simulation approach, this paper investigates the role of fast-charging infrastructure in urban environments. The simulation model tracks the spatial and temporal behaviours of electric vehicles (EVs), facilitating a comprehensive analysis of the deployment of charging infrastructure. Notably, the model incorporates non-parametric queuing dynamics, information-sharing regarding waiting times, and diverse agent characteristics, deepening insights into the subject matter. Drawing on a large-scale implementation in the municipalities of Frederiksberg and Copenhagen, the study advocates for adopting fast chargers by demonstrating several key points. Firstly, information-sharing significantly reduces waiting times, particularly within the fast-charging network, with potential reductions of up to 30% during peak demand periods. Secondly, larger fast-charging clusters comprising 10–14 outlets outperform smaller clusters, primarily due to reduced waiting times and enhanced prediction accuracy of waiting times, which is a consequence of the information-sharing. Thirdly, placement strategies based on unserved demand metrics yield superior outcomes than those solely driven by observed demand patterns. By effectively monitoring both observed and unmet demand, these strategies tend to better optimize charging infrastructure placement. These insights, which emerge from the sophisticated and heterogeneous nature of the simulation framework, highlight the value of information and unserved demand in this field.

On the move: Influence of animal movements on count error during drone surveys.

The use of remote sensing to monitor animal populations has greatly expanded during the last decade. Drones (i.e., Unoccupied Aircraft Systems or UAS) provide a cost- and time-efficient remote sensing option to survey animals in various landscapes and sampling conditions. However, drone-based surveys may also introduce counting errors, especially when monitoring mobile animals. Using an agent-based model simulation approach, we evaluated the error associated with counting a single animal across various drone flight patterns under three animal movement strategies (random, directional persistence, and biased toward a resource) among five animal speeds (2, 4, 6, 8, 10 m/s). Flight patterns represented increasing spatial independence (ranging from lawnmower pattern with image overlap to systematic point counts). Simulation results indicated that flight pattern was the most important variable influencing count accuracy, followed by the type of animal movement pattern, and then animal speed. A awnmower pattern with 0% overlap produced the most accurate count of a solitary, moving animal on a landscape (average count of 1.1 ± 0.6) regardless of the animal's movement pattern and speed. Image overlap flight patterns were more likely to result in multiple counts even when accounting for mosaicking. Based on our simulations, we recommend using a lawnmower pattern with 0% image overlap to minimize error and augment drone efficacy for animal surveys. Our work highlights the importance of understanding interactions between animal movements and drone survey design on count accuracy to inform the development of broad applications among diverse species and ecosystems.

Distributed Space Team Coordination: An Agent-based Modeling and Simulation Approach

Distributed Space Team Coordination: An Agent-based Modeling and Simulation Approach

Charging infrastructure assessment for shared autonomous electric vehicles in 374 small and medium-sized urban areas: An agent-based simulation approach

This research examines the use of Shared Autonomous Electric Vehicles (SAEVs) in 374 U.S. small and medium-sized urban areas, focusing on fleet and infrastructure needs through agent-based simulations. It assesses metrics such as fleet size, trips per vehicle, and charging station requirements, considering two charger types: Level 2 and Level 3. The findings show significant spatial differences in SAEV operations and infrastructure across these cities. Statistical analysis links these variations to regional road networks and travel patterns. The study finds Level 3 chargers more efficient, requiring fewer stations and enabling more trips per vehicle compared to Level 2 chargers. Furthermore, Level 3 chargers exhibit a greater number of trips per SAEV and a higher ratio of vehicles to charging stations. These findings highlight the significance of considering charging infrastructure characteristics to optimize SAEV fleet performance and promote sustainable transportation systems in urban areas. This study significantly contributes by identifying the spatial variation and correlates of the SAEVs' operational and charging infrastructural performance. Policymakers, urban planners, and transportation service providers can leverage these insights to design and implement effective charging infrastructure for SAEV fleets, thereby advancing the transition to cleaner and more efficient mobility solutions.

Choice Behavior and Diffusion Impact Analysis of Connected and Autonomous Vehicle Travelers with Managed Lanes

Choice Behavior and Diffusion Impact Analysis of Connected and Autonomous Vehicle Travelers with Managed Lanes

An Agent-Based Simulation Approach for Urban Road Pricing Considering the Integration of Autonomous Vehicles With Public Transport

An Agent-Based Simulation Approach for Urban Road Pricing Considering the Integration of Autonomous Vehicles With Public Transport

Synchromodal transport re-planning: an agent-based simulation approach

In the rapidly evolving global marketplace, the logistics sector faces a multitude of challenges that demand implementation of more resilient solutions to respond to any future disturbance. Synchromodal transport, which is viewed as an extension of multimodal transport, is known as a key answer to this issue, as it provides more flexible and sustainable freight transport and also focuses on collaboration between different logistics players. We consider synchromodal transport as a collection of agents that not only have their own characteristics and behaviors, but also interact with each other, which impacts the entire system. In this paper, we study the system using an Agent-Based Modeling approach. The network represents the combination of long-haul and drayage transport, where pre-haulage and end-haulage are done only by truck, and the rest can be done by trucks, trains, or barges. A numerical experiment is conducted to evaluate cost savings and emissions reduction under different logistics service providers’ relation and re-routing scenarios. Our findings show that synchromodal scenarios are more economically and environmentally efficient, and that they lead to higher flexibility and reliability compared to business-as-usual scenarios. Additionally, our model verifies that the cost saving is considerable when logistics service providers collaborate with each other. The results of sensitivity analyses show consistent overall trends when comparing the different scenarios. Therefore, the conclusions drawn from the original experiment appear to be applicable, not only for that specific instance, but have broader relevance and applicability.

Off-peak hour delivery system: an agent-based modelling and simulation approach

Many cities around the world have experienced an increase in population and pose challenges in planning and managing the freight transportation within the city. Off-peak hour delivery (OPHD) is a strategy of delivering goods in the afternoon and evening to reduce congestion during peak periods, increase the efficiency of delivery companies, and reduce emissions. This research proposed a model of city logistics in the retail supply chain by implementing OPHD in the logistics system. The aim of this study is to explore the extent of potential implementation of OPHD in urban logistics. An agent-based modeling and simulation (ABMS) approach is applied to model the autonomous and dynamic interactions of stakeholders involved in city logistics systems. Preliminary results are presented in this paper to provide illustrations of the model. It shows that OPHD can significantly reduce the transportation cost and emissions cost, and ABMS is able to provide results that are relatively close to pilot test drive research which is carried out in real world.

Electrifying an urban delivery fleet: a case study

When carriers consider electrifying their delivery fleets, it is not only a societal and moral obligation, given road traffic's impact on emissions. It has also become an economic consideration as sustainability metrics can result in investment, and the life-cycle cost of electric vehicles is becoming more attractive. In this paper, we report on an actual case of a South African pharmaceutical company considering replacing and electrifying its current delivery fleet. The paper uses an agent-based simulation approach that models each delivery vehicle on an existing road network. Each vehicle's emissions characteristics are known, and its spatiotemporal movement results from solving a variant of the well-known routing problem. Vehicle movements are coupled with a detailed database of emissions factors to estimate the total emissions per vehicle per link, which can then be aggregated to any required level. The results show that electrifying an entire fleet may not be viable initially, suggesting a hybrid fleet as a more practical intervention in the foreseeable future.

Improving the environmental impact of empty containers in water-road hubs: a physical internet approach

The efficient and sustainable movement of goods within the supply chain is a cornerstone of global trade, and the Physical Internet (PI) paradigm presents a transformative approach to achieving this goal. This paper examines the environmental impact of empty containers (EC) within a water-road hub in the context of PI. An agent-based simulation approach through negotiation in Netlogo is proposed and applied to the multi-modal hub ”Halluin II” in the north France. Results indicate that PI-containers lower CO2 emissions by 27%, with ANOVA testing confirming the statistical significance of these outcomes. Furthermore, the analysis of simulation results highlights an improvement in efficiency when using PI-containers. This implies that the PI model not only enhances environmental sustainability but also improves operational efficiency.

Service and inventory model for maintenance workshop in the short cycle operation region: Agent-based simulation approach

Tools used in the North Sea region has a high cycle and come very often to maintenance workshop to be checked, repaired if needed and prepared for the next operations. The availability of spare parts at the workshop plays a significant role to keep the flow time as short as possible and meet such high-cycle operations. Supplying spare parts from the best-cost countries to the North Sea region with about one-year lead time makes the situation more critical and the economic order quantity and reorder point need to be found. However, having inventory at the workshop and ordering a batch of spare parts increases operational expenditures. Moreover, frequent supplies increase the environmental impacts of shipping CO2 and spare scrap rates, whereas repairing the used spare parts and reusing them again can offer a more sustainable solution. Therefore, the purpose of this paper is to develop a simulation model that can quantify the cost and benefits of reusing repaired spares compared to supplying newly built spares from the best-cost countries. To achieve this purpose, a case study has been implemented on a specific maintenance workshop within the North Sea region and the entire tool repair and spare part supply operations are conceptualized and modelled with the help of the simulation modelling approach. Two scenarios have been simulated: (1) the maintenance workshop fully depends on supplying newly built spares from the best-cost countries with no inventory stock or spare reuse, and (2) the Maintenance workshop is primarily dependent on repaired spares with an optimal level of spares stock. The simulation results, from the studied case, support the second scenario where a repair path cycle is introduced to the maintenance workshop, as a 78% reduction in lead times, a 116% improvement in worker utilization, a 73% reduction in crowding levels, a 52% reduction in scrap rate, and a potential profit increase of roughly three million NOK (20%). Therefore, it can be concluded that a local repair service is required to keep maintenance workshops in high-cycle regions at high-performance levels.

Identifying Impacts of School-Escorted Trips on Traffic Congestion and the Countermeasures in Bangkok: An Agent-Based Simulation Approach

The often-discussed issue of parental escorting of children to school and its potential disruption of traffic flow has been extensively examined in the literature. Still, the specific effects of traffic congestion remain understudied. To fill this gap, this study addresses the impacts of school trips on traffic congestion. An agent-based model is applied to simulate various scenarios and assess their impact. Our findings indicate that the traffic speed without school trips is higher speed during peak hours by around 8% and average travel time in the city is reduced significantly. We examine countermeasures: Firstly, flexible working hours can lead to a morning traffic speed improvement of approximately 1.9%. Secondly, staggered school schedules can result in a 6.9% increase in traffic speed at 7 a.m. Optimizing school loading spaces can also enhance road capacity, mitigating road lane blockages during drop-off and pick-up periods. This research sheds light on the impact of parent-escorting travel existence and offers potential solutions to alleviate traffic congestion.

Understanding bike-sharing as a commute mode in Singapore: An agent-based simulation approach

Understanding bike-sharing as a commute mode in Singapore: An agent-based simulation approach

An Agent-Based Decision Support Framework for a Prospective Analysis of Transport and Heat Electrification in Urban Areas

One of the main pathways that cities are taking to reduce greenhouse gas emissions is the decarbonisation of the electricity supply in conjunction with the electrification of transport and heat services. Estimating these future electricity demands, greatly influenced by end-users’ behaviour, is key for planning energy systems. In this context, support tools can help decision-makers assess different scenarios and interventions during the design of new planning guidelines, policies, and operational procedures. This paper presents a novel bottom-up decision support framework using an agent-based modelling and simulation approach to evaluate, in an integrated way, transport and heat electrification scenarios in urban areas. In this work, an open-source tool named SmartCityModel is introduced, where agents represent energy users with diverse sociodemographic and technical attributes. Based on agents’ behavioural rules and daily activities, vehicle trips and building occupancy patterns are generated together with electric vehicle charging and building heating demands. A representative case study set in London, UK, is shown in detail, and a summary of more than ten other case studies is presented to highlight the flexibility of the framework to generate high-resolution spatiotemporal energy demand profiles in urban areas, supporting decision-makers in planning low-carbon and sustainable cities.

Establishing industrial community energy systems: Simulating the role of institutional designs and societal attributes

The importance of decreasing industrial CO2 footprints has become evident, as also highlighted in COP26. As such, the transition to renewable energy in the industrial sector is essential to meet the targets. To this aim, establishing industrial community energy systems (InCES) where industries collectively invest in a shared energy system is an economically and environmentally attractive option. Yet, the emergence and continuity of such collective initiatives among industrial companies has neither received considerable attention in the scientific literature nor in practice. This research, as the first of its kind, aims to investigate institutional design options that allow for such collaboration to take place for the establishment and continuity of an InCES. Given the bottom-up and collaborative nature of such initiatives, we take an agent-based modeling and simulation approach, for the first time in this area, that incorporates the institutional and societal attributes that influence the formation and continuation of an InCES. We take data from an industrial cluster in Arak, one of the most prominent industrial cities in Iran. The results of this study confirm the economic feasibility of an InCES as compared to individual renewable energy investment in the cluster. The results also highlight the importance of flexible membership in increasing the number of investors (i.e., industrial companies) in such initiatives. Other important recommendations are: considering the installation of at least 15% extra capacity for the powerplant, restricting electricity consumption and enforcing on-time payment of monthly premium fees.

New product demand forecasting and production capacity adjustment strategies: Within-product and cross-product word-of-mouth

Manufacturers attach increasing importance to consumer word-of-mouth (WOM) as it assists them to improve demand forecasting and determine production capacity adjustment strategies. Communication among potential consumers occurs as a result of within-product and cross-product word-of-mouth. Within-product WOM has received due academic attention touching upon production volume adjustment under infinite capacity, while the impact of cross-product WOM on demand is under-researched with limited number of studies investigating production capacity adjustment. To address this research gap, we employ Agent-based Modeling and Simulation (ABMS) approach and the Bass model, together with considering within-product WOM and cross-product WOM, to modify previous demand forecasting methods and propose production capacity adjustment strategies. Results show: (1) higher performances delivered by manufactures depends more on production capacity adjustment strategies rather than production volume adjustment strategies; (2) production capacity adjustment strategies are closely associated with factors including unit capacity fixed cost and cross-product WOM; (3) on condition of low unit capacity fixed cost, proactive strategy leads to maximal expected net present value (NPV) of profit and minimal lost customer number (LCN); (4) greater within-product WOM and cross-product WOM might not as beneficial as expected, in that expanding within-product WOM and cross-product WOM in most cases leads to growing lost customer number.

Controlling Traffic Congestion in Urbanised City: A Framework Using Agent-Based Modelling and Simulation Approach

Urbanised city transportation simulation needs a wide range of factors to reflect the influence of certain real-life events accurately. The vehicle composition and the timing of the traffic light signal scheduling play an important role in controlling the traffic flow and facilitate road users, particularly in densely populated urban cities. Since road capacity in urban cities changes throughout the day, an optimal traffic light signal duration might be different. Hence, in this paper, the effect of vehicle composition and traffic light phases on traffic flow during peak and off-peak hours in Georgetown, Penang, one of the highly populated cities in Malaysia, is investigated. Through Agent-Based Modelling (ABM), this complex system is simulated by integrating the driver’s behaviour into the model using the GIS and Agent-Based Modelling Architecture (GAMA) simulation platform. The result of predicted traffic flow varies significantly depending on the vehicle composition while the duration of the traffic signal timing has little impact on traffic flow during peak hours. However, during off-peak hour, it is suggested that 20 s duration of green light provides the highest flow compared to 30 s and 40 s duration of green light. This concludes that the planning for traffic light phasing should consider multiple factors since the vehicle composition and traffic light timing for an effective traffic flow varies according to the volume of road users.

An agent-based simulation approach to investigate the shift of Switzerland’s inland freight transport from road to rail

An agent-based simulation approach to investigate the shift of Switzerland’s inland freight transport from road to rail

Examining the effects of Automated Mobility-on-Demand services on public transport systems using an agent-based simulation approach

Examining the effects of Automated Mobility-on-Demand services on public transport systems using an agent-based simulation approach

Self-reconfiguration simulations of turbines to reduce uneven farm degradation

This study explored the concept of a floating offshore wind farm (FOWF) that self-reconfigures wind turbine (WT) positions. The self-reconfiguration (SR) mechanism moves degraded turbines to different farm positions to delay failures occurring and reduce power loss. A 40-turbine agent-based simulation was created utilising wind and turbine performance data. For the first time, the SR mechanism was designed and optimised with principles of self-engineering complexity. The paper demonstrates the effectiveness of the SR mechanism through an agent-based simulation approach. The optimised SR was used in FOWF simulations of 50 years of operation; SR balanced fatigue across the FOWF and led to an increased income of £5–40M at years 27–33 of operation; however, outside of these years, there is no net positive income, and by year 50 SR has cost the FOWF £4–7M more in movement costs. Lastly, simulations with repair and maintenance restricted to 10 months or less were conducted. SR delayed turbine failures, so they occurred in months when repair could be conducted. The SR reduced power loss and increased net income by up to £20M, indicating that SR could be useful when repair and maintenance times are limited. In the absence of significant operational data, a qualitative validation with experts confirmed the approach and the validity of the simulation model for a range of FOWF scenarios.