Nicolas Marilleau

An agent based meta-model for urban mobility modeling

Mobility study is composed by many research areas which one interests us: urban mobility. Urban movement study aims at looking and designing human displacements in an urban environment. In the literature, two main kinds of mobility are emergent: macroscopic displacements and microscopic movements. Our study takes place at the boundary of them. We want to describe human displacements in a city by describing their behaviour. We use two kinds of tools: multi-agent sytems (MAS) and theories based on probabilistic laws. People behaviours depend on many parameters like social class, geographical location... So, we need to look on these elements. In this article, we suggest an agent based meta-model which intends to describe displacements of a population in a defined environment: a city. This meta-model is encapsulated in a method which begins by the creation of a theoretical mobility model and finishes by the development of a simulator. A toolkit has been created to program this kind of application. It is a multi-agent library which extends MadKit. It keeps the philosophy of the presented meta-model.

An agent based framework for urban mobility simulation

Mobility study is composed of many research areas like urban mobility. In the literature, urban mobilities are represented by analytical techniques like stochastic laws or they are defined by simulation tools like multi-agents systems (MAS). The goal of our work is to define citizen behaviour in order to observe population dynamics by a simulation. This strategy is facilitated by a meta-model and a toolkit which are used with a particular method. The latter begins by a conceptual representation of each mobile and finishes by a mobility simulator. This paper aims at describing the mobility simulation toolkit. Thanks to this framework, mobility simulator development is simplified. It allows us to create distributed applications which are based on MAS.

Multiscale Modeling: Application to Traffic Flow

Abstract: Traffic modeling is a particularly active field, the origins of which can be traced back to the pioneering work of Greenshield in the 1930s. Greenshield was the first to formulate a structural relation between the speed of vehicles on a road and the distance between them. This relation between the flow rate/density, at the heart of the so-called fundamental diagram, has been used by all families of traffic flow models developed ever since. These families of models can be grouped into three distinct but strongly interconnected subcategories: macroscopic models, which consider flows of vehicles, microscopic models, which consider individual vehicles and their interactions, and mesoscopic models, which lie in between the other two categories.

Introduction to NetLogo

Abstract NetLogo is a programming environment which allows for the construction and exploration of agent-based models. Developed at the Center for Connected Learning, the software currently draws from StarLogoT, which is available for Mac OSX, and StarLogo, which was developed at MIT’s Media Laboratory. It is the latter that has had the greatest influence on the programming language used by NetLogo, known as Logo, which was itself inspired by the Lisp programming language family. The history of Logo allows for a partial understanding of NetLogo’s philosophy.

Exploring Complex Models in NetLogo

Models and their simulators are becoming increasingly complex. They are typically the fruit of studying a phenomenon, which leads to a careful arrangement of theories, hypotheses, field data and paradigms (ODEs, PDEs, MASes, cellular automata, etc.). But the challenges and ambitions of modelers are constantly changing and in particular expanding, causing models and simulations to become increasingly expensive, both in terms of their design and analysis. Developing high-performance calculation systems and software for systematic exploration can help to iron out some of these difficulties. But, it is not enough.

1 – NetLogo, an Open Simulation Environment

Abstract: NetLogo is a generic simulation environment in the sense that it was not designed with any specific domain of application in mind. NetLogo offers a wide range of features and generic operators to its users. Additionally, to make up for any missing features, NetLogo is compatible with other platforms and libraries, as we will demonstrate throughout this book.

Introduction to the Agent Approach

When we need to study a real system made up of interconnected elements, where each of these systems has its own dynamics, it is often impossible to foresee the emergence of a global dynamics for the system. In this case, what is in question is a complex system, because any one modification, even if it is marginal in terms of its one or several constituent elements, may lead to a dramatic change in overall operation of the system. It becomes clear that these phenomena may well be understood and observed only through the construction of a model. Even if in certain particular cases the model may be resolved analytically, as is the case for the Lotka–Voltera prey-predator models, computer simulation is indispensable in all other cases, i.e. in most thematically interesting cases. As such, agent modeling is one possible response for studying complex spatial systems.

Agent-Based Model Exploration

Abstract The previous chapters have allowed us to introduce the basics of agent-based model creation with NetLogo. This has resulted in a model such as the one used in our running example. Once the model has been built, the aim is to manipulate it in such a way that new knowledge about the modeled phenomenon can be created. For example, we could look to study the rate of infection resulting from certain parameter values. The use and study of a model is nonetheless as complex as its creation. As such, using our model, we could launch the simulation with standard initial parameters (say 300 humans, 500 mosquitoes, a contamination distance of 5 and a work–home distance of 500), and we would obtain the graph indicating no infection beyond the source mosquito, which would lead us to conclude that these parameters lead to no infections. However, upon relaunching the simulation with the exact same values, we might obtain the graph with the infection present in almost 100% of individuals after 1,000 iterations.

Improving Individual Accessibility to the City

In this work we address the issue of sustainable cities by focusing on one of its very central components, daily mobility. Indeed, if cities can be interpreted as spatial organisations favouring social interactions, the number of daily movements needed to reach this goal is continuously increasing. Therefore, improving urban accessibility merely results in increasing the traffic and its negative externalities (congestion, accidents, pollution, noise…), while reducing at the end the accessibility of people to the city. We therefore propose to investigate this issue from the complex systems point of view. The real spatio-temporal urban accessibility of citizens can not be approximated just by focusing on space and implies to take into account the space-time activity patterns of individuals, in a more dynamic way. However, given the importance of local interactions in such a perspective, an agent based approach seems to be a relevant solution. This kind of individual based and “interactionist” approach allows exploring the possible impact of individual behaviours on the global behaviour of city but also the possible impact of global measures on individual behaviours.