Integrating Multi-agent System and Microsimulation for Dynamic Modeling of Urban Freight Transport

This work aims to apply an integration between a multi-agent system and microsimulation to take advantage of the large amount of data generated in urban freight transport to improve the overall performance of the urban supply chain without forgetting the principles of autonomy that govern each of its actors, responding to the different dynamic scenarios that may arise in the operational context. The integration framework produces a satisfactory communication process in those modeling methods measured by two indexes: throughput and latency. The results of this integration show a robust response to dynamic scenarios and allow reacting to the different quantity of changes without dismissing the search for optimum solutions

Can geocomputation save urban simulation? Throw some agents into the mixture, simmer and wait ...

There are indications that the current generation of simulation models in practical, operational uses has reached the limits of its usefulness under existing specifications. The relative stasis in operational urban modeling contrasts with simulation efforts in other disciplines, where techniques, theories, and ideas drawn from computation and complexity studies are revitalizing the ways in which we conceptualize, understand, and model real-world phenomena. Many of these concepts and methodologies are applicable to operational urban systems simulation. Indeed, in many cases, ideas from computation and complexity studies—often clustered under the collective term of geocomputation, as they apply to geography—are ideally suited to the simulation of urban dynamics. However, there exist several obstructions to their successful use in operational urban geographic simulation, particularly as regards the capacity of these methodologies to handle top-down dynamics in urban systems. This paper presents a framework for developing a hybrid model for urban geographic simulation and discusses some of the imposing barriers against innovation in this field. The framework infuses approaches derived from geocomputation and complexity with standard techniques that have been tried and tested in operational land-use and transport simulation. Macro-scale dynamics that operate from the topdown are handled by traditional land-use and transport models, while micro-scale dynamics that work from the bottom-up are delegated to agent-based models and cellular automata. The two methodologies are fused in a modular fashion using a system of feedback mechanisms. As a proof-of-concept exercise, a micro-model of residential location has been developed with a view to hybridization. The model mixes cellular automata and multi-agent approaches and is formulated so as to interface with meso-models at a higher scale

Land use change through market dynamics : a Microsimulation of land development, the bidding process, and location choices of households and firms

textRapid urbanization is a pressing issue for planners, policymakers, transportation engineers, air quality modelers and others. Due to significant environmental, traffic and other impacts, the process of land development highlights a need for land use models with behavioral foundations. Such models seek to anticipate future settlement and transport patterns, helping ensure effective public and private investment decisions and policymaking, to accommodate growth while mitigating environmental impacts and other concerns. A variety of land use models now exist, but a market-based model with sufficient spatial resolution and defensible behavioral foundations remains elusive. This dissertation addresses this goal by developing and applying such a model. Real estate markets involve numerous interactive agents and real estate with a great level of heterogeneity. In the absence of tractable theory for realistic real estate markets, this research takes a “bottom-up” approach and simulates the behavior of tens of thousands of individual agents based on actual data. Both the supply and demand sides of the market are modeled explicitly, with endogenously determined property prices and land use patterns (including distributions of households and firms). Notions of competition were used to simulate price adjustment, and market-clearing prices were obtained in an iterative fashion. When real estate markets reach equilibrium, each agent is aligned with a single, utility-maximizing location and each allocated location is occupied by the highest bidding agent(s). This approach helps ensure a form of local equilibrium (subject to imperfect information on the part of most agents) along with useroptimal land allocation patterns. The model system was applied to the City of Austin and its extraterritorial jurisdiction. Multiple scenarios reveal the strengths and limitations of the market simulation and available data sets. While equilibrium prices in forecast years are generally lower than observed or expected, the spatial distributions of property values, new development, and individual agents are reasonable. Longer-term forecasts were generated to test the performance the model system. The forecasted households and firm distributions in year 2020 are consistent with expectations, but property prices are forecasted to experience noticeable changes. The model dynamics may be much improved by more appropriate maximum bid prices for each property. More importantly, this work demonstrates that microsimulation of real estate markets and the spatial allocation of households and firms is a viable pursuit. Such approaches herald a new wave of land use forecasting opportunities, for more effective policymaking and planning.Civil, Architectural, and Environmental Engineerin

Introduction to urban simulation design and development of operational models

This chapter has sought to explain the context, policy applications, and major design choices in the process of developing an operational urban simulation model, with specific reference to UrbanSim as a case study. It has been argued that careful design at each stage of the process is needed to make the model sensitive to the policies of principal concern, to make the data and computational requirements manageable, to make the model usable by staff and other users with appropriate levels of training, and to fit into the operational practices of the relevant organizations. To be useful (relevant) in the policy process, model design should carefully integrate the elements discussed in the chapter into a design that fits well into a specific institutional and political context, and evolve to adapt to changing conditions. This introduction to the design process sets the stage for more in-depth discussion of specification and operational issues in model use. The UrbanSim system is being further developed to adapt to varying data availability, different factors influencing agent choices in locations ranging from newer and rapidly growing US metropolitan areas in other parts of the world. Considerable effort is now being devoted to developing environmental components of the system such as land cover change, and to developing a robust interface and tools for visualization and evaluation of policy scenarios. Document type: Part of book or chapter of boo

Integrated transportation and energy activity-based model

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 130-141).There is a long history of projects and regulations that have had limited or even counterproductive results. These unforeseen effects are due to the failure of planners to capture all of the complexity inherent in urban dynamics. With the increasing risks of global warming, policymakers and planners need to make optimal or close-to-optimal decisions on how to use the available resources in order to reduce energy and fuel consumption. This thesis develops the framework for an urban model that serves as a decision support tool to inform sustainable policies and investments. The model discussed integrates the modeling of land use, transportation, and energy consumption by micro-simulating the behavior of households and firms in an urban area. This approach derives transport and energy consumption from human activities and includes the two-way feedback between each agent's behavior and the area's overall dynamics. We build upon complex systems theory and make an analogy with epidemiology modeling to derive the properties of heterogeneity-based, organized complex systems. We then translate the properties of these models with respect to the spatial and temporal resolutions of transportation, land market and energy systems' models. To achieve the integration of the three complex systems with activities in our framework, we present three different extensions to activity-based modeling in the household context.(cont.) We first expand the scope of activities considered in activity-based modeling to fit the integrated transportation and energy scope. We then present the econometric techniques of latent variable and latent class modeling to capture individual heterogeneity. Third, we formulate the motivation behind activity participation and model the short- and long-term activity dynamics by operationalizing the concept of stress. We illustrate the potential of iTEAM in modeling different scenarios to demonstrate the role of our integrated transportation and energy model as a decision support tool for sustainable urban planning.by Anwar Ghauche.S.M

Scale effect in a LUTI model of Brussels: challenges for policy evaluation

The aim of this paper is to assess the reliability of policy evaluation based on Land Use and Transport Interactions models, relative to the choice of the Basic Spatial Units. An UrbanSim (+ MATsim) model applied to Brussels (Belgium) is used as the case study. The evolution of the study area over twenty years is forecasted for four levels of Basic Spatial Units and five scenarios (businessas-usual and four alternatives). Results show larger variations between Basic Spatial Units levels than across scenarios. These findings are valid for various sustainability indicators and for a simple cost-benefit analysis aiming at ranking the scenarios. The direction of the variations resulting from the implementation of the scenarios remains, however, the same for all Basic Spatial Units levels. Hence, the influence of the scale on policy evaluation based on Land Use and Transport Interactions models appears limited when it is only intended to compare scenarios, but it will have a crucial role when evaluations are based on absolute variations or threshold values