No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Agent-Based Modeling and Simulation for Urban Air Quality Assessment
ResearchGate has not been able to resolve any citations for this publication.
Objective
This paper aims to provide a comprehensive overview of the current state of agent-based models, focusing specifically on their application in urban transportation research. It identifies research gaps and challenges while outlining the key potential directions for future research.
Methodology
To conduct this study, a bibliometric analysis has been performed on 309 documents obtained from the Scopus database. The resulting clustering analysis has been further supplemented with content analysis.
Findings
The analysis reveals the existence of nine distinct clusters representing a wide range of research methods and problem interpretations in the field. In-depth examination of selected publications within each cluster has helped to identify key challenges faced by agent-based modelling approaches. These challenges include enhancing computing efficiency, developing unified calibration and validation methods, ensuring reproducibility of work, and incorporating various modules or frameworks into models to accurately replicate the complexities of the transport system and travel behaviour within specific application contexts.
Air pollution is a big danger to medium and low‐income countries like India. It is one of the most serious pollution issues and the main source of adverse health consequences in humans. It is discovered that it is responsible for nearly 4.2 million premature deaths each year due to particulate matter (PM) pollution, which causes cardiovascular, infectious, and carcinogenic diseases. According to various epidemiological analyses, NO2, i.e., nitrogen dioxide and RSPM, i.e., respirable suspended particulate matter have a comparatively higher association with respiratory problems and a modest correlation with cough and asthma, indicating that NO2 and RSPM are of significant concern. The key cause of air pollution is an exponential increase of licensed cars, which causes traffic congestion, the proliferation of older vehicles, inadequate inspection and repair, the absence of public transit and carpooling, poor fuel quality, insufficient traffic enforcement, poor road conditions, a lack of public knowledge, and the inefficiency of pollution control boards. In 2020, based on the recommendations of the 15th Finance Commission, the Government of India has set aside about $1.7 billion to fight air pollution over the next 5 years for the 42 Indian cities that have million‐plus populations—provided they reduce their air pollution levels by 15% every year.
In this study, an activity-based travel demand model of the Ústí nad Labem district (Czech Republic) is created. To do this, an advanced travel demand synthesis process is presented by utilizing the Eqasim framework, which is a pipeline-processing, initial raw data to simulation step. The framework is extensively modified and extended with several algorithms in order to utilize multiple data points for increasing realism in mobility for travel demand models. Two major extensions are provided. First, the pipeline framework is improved to estimate inbound and outbound trips of the study area, comprising a main city and 23 surrounding municipalities. The extended framework assigns synthetic gates for the study area as hubs for the inclusion of inbound and outbound trips. Second, the pipeline framework is advanced to provide a more compatible match of travel destination and activity location state. To do this, the extended framework assigns a capacity for each facility identified for the study area, the expected number of visitors to each facility, and the number of residents in each building. The resulting demand model is presented and the generated trips are evaluated based on locational, transport mode, and sociodemographic characteristics with origin–destination (OD) bundling. Additionally, distribution analyses of the present model are conducted to understand the matching results on a detailed level. The results demonstrate that the present model provides a reasonable output for transport researchers when testing different mobility scenarios and the provided extensions helps them to reduce implausible reflections of the distribution of travel and activity characteristics in household travel surveys while creating demand models, thus increasing realism. Lastly, open-source playground and code repository for further future improvement of synthetic travel synthesis methods are created, which enhances a deep understanding of the preparatory and methodological backgrounds required for complex activity-based simulations in order to inspire transport planners.
In this review we discuss advances in the agent-based modeling of economic and social systems. We show the state of the art of the heuristic design of agents and how behavioral economics and laboratory experiments have improved the modeling of agent behavior. We further discuss how economic networks and social systems can be modeled and we discuss novel methodology and data sources. Lastly, we present an overview of estimation techniques to calibrate and validate agent-based models and show avenues for future research.
This paper introduces the eqasim framework with the aim to provide a consistent pipeline from raw data to a final transport simulation. It therefore lays the foundation to achieve fully reproducible agent-based transport simulations. While the pathway from raw data to a generic synthetic travel demand was covered previously for specific use cases, here the general methodology is summarized. Furthermore, the tools and methods for combining MATSim simulations with flexibly definable discrete choice models is described, which is the core of the existing simulation implementations of eqasim for île-de-France, Switzerland, Sao Paulo and California.
Over 30 years have passed since activity-based travel demand models (ABMs) emerged to overcome the limitations of the preceding models which have dominated the field for over 50 years. Activity-based models are valuable tools for transportation planning and analysis, detailing the tour and mode-restricted nature of the household and individual travel choices. Nevertheless, no single approach has emerged as a dominant method, and research continues to improve ABM features to make them more accurate, robust, and practical. This paper describes the state of art and practice, including the ongoing ABM research covering both demand and supply considerations. Despite the substantial developments, ABM’s abilities in reflecting behavioral realism are still limited. Possible solutions to address this issue include increasing the inaccuracy of the primary data, improved integrity of ABMs across days of the week, and tackling the uncertainty via integrating demand and supply. Opportunities exist to test, the feasibility of spatial transferability of ABMs to new geographical contexts along with expanding the applicability of ABMs in transportation policy-making.
The paper presents a critical review of the methodological approaches used in tour-based mode choice models within the activity-based modelling frameworks. Various components of the activity-based models, such as activity type choice, activity location choice, and activity duration have already matured significantly. However, the mode choice component is often simplified in many ways. Both trip-based and tour-based approaches are used in many cases. However, the tour-based approach is considered to be the most relevant to the activity-based modelling framework. This paper presents a synthesis of the strengths and weaknesses of existing tour-based mode choice models. The previous studies on tour-based mode choice models are grouped into seven categories, ranging from simplified main tour mode to complex dynamic discrete choice models. Besides, challenges with data-hungry models, simulation-based models and static models are discussed elaborately. In conclusion, it proposes a few methodological suggestions for researchers and practitioners for finding an appropriate mode choice modelling framework for activity-based models. In addition, the paper also provides a guideline on how to incorporate automated vehicles and Mobility-as-a-Service within the framework of tour-based mode choice models.
The most common travel demand model type is the trip-based model, despite major shortcomings due to its aggregate nature. Activity-based models overcome many of the limitations of the trip-based model, but implementing and calibrating an activity-based model is labor-intensive and running an activity-based model often takes long runtimes. This paper proposes a hybrid called MITO (Microsimulation Transport Orchestrator) that overcomes some of the limitations of trip-based models, yet is easier to implement than an activity-based model. MITO uses microsimulation to simulate each household and person individually. After trip generation, the travel time budget in minutes is calculated for every household. This budget influences destination choice; i.e., people who spent a lot of time commuting are less likely to do much other travel, while people who telecommute might compensate by additional discretionary travel. Mode choice uses a nested logit model, and time-of-day choice schedules trips in 1-minute intervals. Three case studies demonstrate how individuals may be traced through the entire model system from trip generation to the assignment.
In urban areas, traffic is one of the main causes of air pollution. Establishing an effective solution to raise public awareness of this phenomenon could help to significantly reduce the level of pollution in urban areas. In this study, we design and implement an agent-based simulation allowing to study the principles of production and dispersion of pollutants from road traffic in urban areas. The simulation takes into account different factors that can produce pollutants from the urban zone (the case of Hanoi city in Vietnam): roads and streets, vehicles (types, quantity), traffic, wind direction, etc. With this simulation, one can observe and study the emission and dispersion of pollutants from traffic by conducting experiments with various scenarios and parameters. This work is an interesting solution to sensitize the public's awareness on the air pollution from traffic in urban areas, so that people can change their behaviors to reduce the air pollution.
This paper presents the development of an integrated microscopic mobility simulator, SimMobility Short-Term (ST). The simulator is integrated because its models, inputs and outputs, simulated components, and code base are integrated within a multiscale agent- and activity-based simu- lation platform capable of simulating different spatiotemporal resolutions and accounting for different levels of travelers’ decision making. The simulator is microscopic because both the demand (agents and its trips) and the supply (trip realization and movements on the network) are microscopic (i.e., modeled individually). Finally, the simulator has mobility because it copes with the multimodal nature of urban networks and the need for the flexible simulation of innovative transportation ser - vices, such as on-demand and smart mobility solutions. This paper follows previous publications that describe SimMobility’s overall framework and models. SimMobility is an open-source, multiscale platform that considers land use, transportation, and mobility-sensitive behavioral models. SimMobility ST aims at simulating the high-resolution movement of agents (traffic, transit, pedestrians, and goods) and the operation of different mobility services and control and information systems. This paper presents the SimMobility ST modeling framework and system architecture and reports on its successful calibration for Singapore and its use in several scenarios of innovative mobility applications. The paper also shows how detailed performance measures from SimMobility ST can be integrated with a daily activity and mobility patterns simulator. Such integration is crucial to model accurately the effect of different technologies and service operations at the urban level, as the identity and preferences of simulated agents are maintained across temporal decision scales, ensuring the consistency and accuracy of simulated accessibility and performance measures of each scenario.
This paper reports a preliminary study of the evaluation and forecast of
transport-related air pollution dispersion in urban areas with the help of Geographic
Information System (GIS) platform and a simulative system with graphical interface.
The urban population growth, economic development, energy consumption,
growing transportation demand and living standards play major role in pollution
exposure in atmosphere. A lot of research has already been done to investigate the
functional relationship between air quality and air pollution from transport. This
study is an effort to develop a more flexible framework of model to find the
exposure of the air pollution in the atmosphere. This review article describes the
development of framework of different GIS inputs that help to find the exposure of
vehicular pollution in megacity.
In this paper we present the development of an integrated microscopic mobility simulator, SimMobility Short-Term (ST). “Integrated” as its models, inputs and outputs, simulated components and code-base are integrated within a multi-scale agent- and activity-based simulation platform capable of simulating different spatial-temporal resolutions and account for different levels of travelers’ decision making. “Microscopic” as both the demand – agents and its trips – and the supply –trip realization and movements on the network – are microscopic, i.e. modeled individually. Finally, “Mobility”, as it copes with the multimodal nature of urban networks and the need for the flexible simulation of innovative transportation services such as on-demand and smart mobility solutions. This paper follows previous publications that describe SimMobility’s overall framework and models. SimMobility is a multi-scale platform that considers land-use, transportation, and mobility-sensitive behavioral models. SimMobility ST aims at simulating the high-resolution movement of agents (traffic, transit, pedestrians and goods) and the operation of different mobility services and control and information systems. This paper presents SimMobility ST modelling framework and system architecture, reporting its successful calibration for Singapore and its use in several scenarios of innovative mobility applications. We also show how detailed performance measures from SimMobility ST can be integrated with a daily activity7 and mobility patterns simulator. Such integration is crucial to accurately model the impact of different technologies and service operations at the urban level, as the identity and preferences of simulated agents are maintained across temporal decision-scales ensuring the consistency and accuracy of simulated accessibility and performance measures of each scenario.
Even though clean air is considered as a basic requirement for the maintenance of human health, air pollution continues to pose a significant health threat in developed and developing countries alike. Monitoring and modeling of classic and emerging pollutants is vital to our knowledge of health outcomes in exposed subjects and to our ability to predict them. The ability to anticipate and manage changes in atmospheric pollutant concentrations relies on an accurate representation of the chemical state of the atmosphere. The task of providing the best possible analysis of air pollution thus requires efficient computational tools enabling efficient integration of observational data into models. A number of air quality models have been developed and play an important role in air quality management. Even though a large number of air quality models have been discussed or applied, their heterogeneity makes it difficult to select one approach above the others. This paper provides a brief review on air quality models with respect to several aspects such as prediction of health effects.
A partir des enquêtes, le trafic n’est pas une donnée, mais il est plutôt construit à partir d’hypothèses portant sur les relations entre des origines et des destinations. En vue de reconstruire un trafic routier plus proche de la mesure, et sur un ensemble de tronçons routiers plus important, il apparait alors intéressant de partir de données de comptages issues de capteurs urbains. Notre postulat de départ part de ce constat. L’insertion de ces données d’observations du trafic routier fournit l’opportunité d’expérimenter les potentiels d’exploitations des capteurs pour estimer les niveaux de Pollution Atmosphérique Automobile (PAA) à l’échelle intraurbaine. Cependant, il est alors nécessaire de modifier la nature de la mesure en vue d’extraire une information sur la circulation routière, ce qui a été envisagé ici à travers la construction d’un modèle de simulation multi-agents. D’une manière plus générale, en partant de la donnée, c’est une démarche de construction de la connaissance sur les émissions de PAA qui est abordée tout au long de ce travail.
La mise en oeuvre de la démarche de modélisation SCAUP (Simulation multi-agents à partir de Capteurs Urbains pour la Pollution atmosphérique automobile) a été réalisée en trois temps : 1. En se focalisant sur les dispositifs de quantification du trafic routier à travers les capteurs urbains ; 2. En proposant une démarche de modélisation et de simulation de ces données pour le trafic routier ; 3. En se rattachant aux référentiels nationaux utilisés par les AASQA pour le calcul des émissions de PAA. L’ensemble se lie et s’intègre au sein d’une matrice technique qui constitue la colonne vertébrale de ce manuscrit à travers trois dispositifs interdépendants : la quantification, la modélisation et l’évaluation. Ce travail s’inscrit dans une démarche expérimentale de simulation du trafic routier pour le calcul des émissions de PAA. Parrainé par l’AASQA locale ATMOSF’AIR BOURGOGNE, il s’inscrit aussi dans une optique de recherche appliquée en appui de ces organismes en charge de la surveillance de la qualité de l’air. À l’heure où le big data entre dans de nouveaux questionnements quant aux capacités des chercheurs à en extraire une connaissance, nous proposons une démarche géographique en vue de replacer la donnée au centre d’une démarche de simulation originale du trafic routier (data-driven).
Activity-based models belong to the third generation of travel demand models, which have received extensive interest in the past three decades. Activity-based modeling of travel demand treats travel as being derived from the demand for activity participation. This paper presents an overview of recent and on-going contributions made by "activity-based approaches" to forecast travel behavior. First, a brief history of two travel demand approaches is introduced. Then, four modeling methods in activity travel analysis are reviewed. On top of that, essential applications of this approach for addressing contemporary policy and planning issues are provided. Finally, some of the directions are highlighted for future development of activity-based analysis.
For any kind of statistical analysis of trip matching algorithms, working on the samples of mobility surveys is hopeless as these typically cover not much more than 2% of travellers. The usual inference process works on a discrete representation of the territory (zones) and simply replace each trip in the sample by as many equal trips as the corresponding multiplicative coefficient N (inverse of the sampling coefficient). This paper presents an innovative application of the mobility surveys data (with geo-referenced information) as an alternative to Activity-based procedures using discrete choice models. We introduce some principles of fuzzy logic inference processes, which allow the production of a synthetic population of trips with a continuous representation of trips in space and in time. The proposed procedure uses a statistical approach to trip dispersion, using a Monte Carlo Simulation process, based on the survey data and the land use characteristics in order to preserve the mobility patterns observed in the survey. The procedure was applied to the Lisbon Metropolitan Area (LMA) and the results show a good match with the original data, but with a greater, more realistic space and time coverage. The results suggest a significant added value of this approach for the modelling of transport modes requiring space and time matching that cannot be correctly modelled with the traditional discrete representation of the territory as well as for estimation of traffic loads in low hierarchy arcs of the network.
There is a parallel evolution of a political economy and the transportation system either in developed or developing countries. The growth of economic will increase the number of ton-km of freight transportation. As flows of freight and their impacts increase, transportation planners, managers, and operators have a more concern to develop a better methods for tracking and monitoring the flows of commodities and analyzing the impacts of these flows to the transportation infrastructures. Current estimation of freight flows and analysis methods have several problems, which are related to the data and the method itself. This paper attempts to elaborate the relationship of the economics activities of commodities toward export -specifically the production of goods and services that generate freight transportation, the spatial data of commodities related to the economics' activities (resources, factories, and outlets), the transportation data (infrastructures, modes, and services), and government regulations, in formulating the method for freight transportation planning to produce an efficient freight movement to enhance sustainable mobility and economic development in the regional scale.
Urban environments are often associated with high traffic density. Especially road traffic is a major source for air pollution in cities. The cause-and-effect chain from the traffic activity towards the concentration of air pollutants is complex. Modeling the outcome needs a lot of inputs in terms of methodology and data. Against this background, an approach is developed that links the agent-based transport model MATSim with the emission factors and traffic situations of HBEFA. The goal is to approximate link travel times as well as the resulting emissions of air pollutants while still being applicable to large-scale scenarios. This paper aims at laying down the foundations for this innovative approach. A test case is developed where link travel times are simulated and the resulting emissions are calculated for MATSim test vehicles. The results are then compared to real-world data. Further, it is discussed how to extend this approach to a large-scale scenario and what prerequisites are needed. Finally, it is analyzed what additional information the model provides in order to achieve a more sustainable transport and urban planning.
Air pollution is a global health issue with serious public health implications, particularly for children. Usually respiratory effects of air pollutants are considered, but this review highlights the importance of non-respiratory health hazards. In addition to short-term effects, exposure to criteria air pollutants from early life might be associated with low birth weight, increase in oxidative stress and endothelial dysfunction, which in turn might have long-term effects on chronic non-communicable diseases. In view of the emerging epidemic of chronic disease in low- and middle- income countries, the vicious cycle of rapid urbanization and increasing levels of air pollution, public health and regulatory policies for air quality protection should be integrated into the main priorities of the primary health care system and into the educational curriculum of health professionals.
At the operational level of Flexible Manufacturing Systems (FMS), several research works have developed solutions not fully realizable considering the operational constraints, which can be difficult to model. One of the well-known problems in FMS is the Job-Shop Scheduling Problem (JSSP) with transportation tasks. Indeed, this problem is addressed by many researchers, but most of their studies do not consider operational constraints such as collision avoidance between transporters. Moreover, optimized scheduling may contain deadlock situations due to often neglected geometric constraints. Therefore, in this paper, we propose a simulation approach based on a multi-agent system to test the optimized results known in the literature in more realistic conditions, where collision avoidance between transporters is considered. We propose as well a sim-optimization approach to explore suitable solutions considering collision avoidance constraints. In addition, an algorithm is proposed to solve deadlocks. The obtained results highlight the impact of collision avoidance on the performance of the literature solutions while being more realistic. Furthermore, these results demonstrate the efficiency of the proposed algorithms, since all identified collisions are avoided and all deadlocks are also solved. The proposed approach is expected to be more attractive to the industries as it ensures a small gap between the theoretical and actual results.
In light of the growing concern about the adverse effects of noise pollution on health, a better understanding is needed of the relationships between urban transport and individual exposure. To improve the scientific community's modeling capabilities specific to this issue, we propose a noise exposure modeling framework that uses agent-based activity, multi-agent travel simulation and a European standardized noise emission and propagation model. Based on two open source software packages, MATSim and NoiseModelling, this framework aims to simulate the spatiotemporal distributions of daily individual activity and road traffic noise. The proposed approach allows to use the tools and methods proposed in the NoiseModelling software by importing MATSim outputs , therefore, taking full advantage of the development work carried out within the two communities. As such, it enables both characterizing the individual exposure to road traffic-related noise and investigating noise exposure inequality problems based on the attributes of individuals and their activities.
Traffic flow modeling has evolved over a few decades with numerous models at the macroscopic and microscopic levels being proposed. It appears that traffic flow modeling has been adequately addressed and it is natural to ask if there is still any gap to fill or any room to improve. After examining existing models and comparing them against ideal outcomes, we identified four limitations in both traffic flow theory and simulation, namely (1) the lack of model consistency among macroscopic models and between microscopic and macroscopic models, (2) the lack of model flexibility to admit driver heterogeneity, (3) the lack of model capability to look ahead into the near future, and (4) the lack of model expandability beyond one dimensional traffic. Strategies to address these limitations are proposed with emphasis on general approaches rather than proposing specific models.
Air quality models (AQMs) are useful for studying various types of air pollutions and provide the possibility to reveal the contributors of air pollutants. Existing AQMs have been used in many scenarios having a variety of goals, e.g., focusing on some study areas and specific spatial units. Previous AQM reviews typically cover one of the forming elements of AQMs. In this review, we identify the role and relevance of every component for building AQMs, including (1) the existing techniques for building AQMs, (2) how the availability of the various types of datasets affects the performance, and (3) common validation methods. We present recommendations for building an AQM depending on the goal and the available datasets, pointing out their limitations and potentials. Based on more than 40 works on air quality, we concluded that the main utilized methods in air pollution estimation are land-use regression (LUR), machine learning, and hybrid methods. In addition, when incorporating LUR methods with traffic variables, it gives promising results; however, when using kriging or inverse distance weighting techniques, the monitoring stations measurements of air pollution data are enough to have good results. We aim to provide a short manual for people who want to build an AQM given the constraints at hands such as the availability of datasets and technical/computing resources.
This is the era of Big Data and computational social science. It is an era that requires tools which can do more than visualise data but also model the complex relation between data and human action, and interaction. Agent-Based Models (ABM) - computational models which simulate human action and interaction – do just that.
This textbook explains how to design and build ABM and how to link the models to Geographical Information Systems. It guides you from the basics through to constructing more complex models which work with data and human behaviour in a spatial context. All of the fundamental concepts are explained and related to practical examples to facilitate learning (with models developed in NetLogo with all code examples available on the accompanying website). You will be able to use these models to develop your own applications and link, where appropriate, to Geographical Information Systems.
All of the key ideas and methods are explained in detail:
geographical modelling;
an introduction to ABM;
the fundamentals of Geographical Information Science;
why ABM and GIS;
using QGIS;
designing and building an ABM;
calibration and validation;
modelling human behavior.
An applied primer, that provides fundamental knowledge and practical skills, it will provide you with the skills to build and run your own models, and to begin your own research projects.
Microscopic traffic simulation is an invaluable tool for traffic research. In recent years, both the scope of research and the capabilities of the tools have been extended considerably. This article presents the latest developments concerning intermodal traffic solutions, simulator coupling and model development and validation on the example of the open source traffic simulator SUMO.
This paper discusses the development of an agent-based modeling software development kit, and the implementation and validation of a model using it that integrates dynamic simulation of travel demand, network supply and network operations. A description is given of the core utilities in the kit: a parallel discrete event engine, interprocess exchange engine, and memory allocator, as well as a number of ancillary utilities: visualization library, database IO library, and scenario manager. The overall framework emphasizes the design goals of: generality, code agility, and high performance. This framework allows the modeling of several aspects of transportation system that are typically done with separate stand-alone software applications, in a high-performance and extensible manner. The issue of integrating such models as dynamic traffic assignment and disaggregate demand models has been a long standing issue for transportation modelers. The integrated approach shows a possible way to resolve this difficulty. The simulation model built from the POLARIS framework is a single, shared-memory process for handling all aspects of the integrated urban simulation. The resulting gains in computational efficiency and performance allow planning models to be extended to include previously separate aspects of the urban system, enhancing the utility of such models from the planning perspective. Initial tests with case studies involving traffic management center impacts on various network events such as accidents show the potential of the system.
As road traffic is a major source for urban air pollution there is a need to link traffic models with the modelling of air pollution in order to analyse the impacts of transport policies on the environment and human health. The cause-and-effect chain from the traffic activity towards the concentration of air pollutants and population exposure is complex. Against this background, an approach is developed that links the multi-agent-based transport model (MATSim) with the calculation of air pollution using the Operational Street Pollution Model (OSPM). Traffic-related air pollution is modelled as detailed as possible while still being applicable to large-scale scenarios. Simulated hourly mean nitrogen oxide (NOx) concentrations are compared with concentration measurements showing a similar pollution level and diurnal pattern at a site along a street canyon in Munich, Germany. Nitrogen dioxide (NO2) emissions and concentrations are simulated for an area bounded by the major ring road of Munich, Germany. Locations with a high concentration level can be identified and the effects of a changing traffic demand through the introduction of a speed limit are shown.
Air pollution from motor vehicles is one of the most serious and rapidly growing problems in metropolitan areas. It is occurred especially in major arterial streets inside the metropolitan central district because of the heavy traffic congestion suffering. Although transportation networks operate as an integrated system, at a regional level we can safely assume that local urban congestion will not affect other urban areas that are geographically distinct. This suggests a manageable problem, i.e., instead of solving for region-wide congestion patterns, we can augment the current capabilities of logistical air quality management system (AQMS) software with a module to predict localized urban congestion on a special major arterial street and its impacts on the amount of generated air pollution. In this paper, a GIS-based multi-agent traffic micro-simulation decision support approach utilized in order to manage and control navigation under dynamic traffic identification and modelling to determine the air pollution, particularly CO, generated by heavy traffic congestion in one of the major arterial urban streets. Our preliminary work in this area indicates that agent technology can significantly help designers and decision makers in this context.
This paper describes the parallel implementation of the TRansportation ANalysis and SIMulation System (TRANSIMS) traffic micro-simulation. The parallelization method is domain decomposition, which means that each CPU of the parallel computer is responsible for a different geographical area of the simulated region. We describe how information between domains is exchanged, and how the transportation network graph is partitioned. An adaptive scheme is used to optimize load balancing.We then demonstrate how computing speeds of our parallel micro-simulations can be systematically predicted once the scenario and the computer architecture are known. This makes it possible, e.g., to decide if a certain study is feasible with a certain computing budget, and how to invest that budget. The main ingredients of the prediction are knowledge about the parallel implementation of the micro-simulation, knowledge about the characteristics of the partitioning of the transportation network graph, and knowledge about the interaction of these quantities with the computer system. In particular, we investigate the differences between switched and non-switched topologies, and the effects of 10 Mbit, 100 Mbit, and Gbit Ethernet.As an example, we show that with a common technology – 100 Mbit switched Ethernet – one can run the 20 000-link EMME/2-network for Portland (Oregon) more than 20 times faster than real time on 16 coupled Pentium CPUs.
Handbook Emission Factors for Road Transport
- M Keller
- P Wuthrich
Polaris: agent-based modeling framework development and implementation for integrated travel demand and network and operations simulations
- J A Auld
- JA Auld