Extending TRANSIMS Technology to an Integrated Multilevel Representation

The TRANSIMS system developed at Los Alamos in the USA over the past decade is a world leader in providing an integrated land-use transportation dynamical model for large areas with a million or more inhabitants. TRANSIMS uses standard survey data to create synthetic micropopulations, including family structure, to simulate trip making and emergent traffic dynamics. We propose to extend TRANSIMS by adapting it to a new multi-level representation, allowing dynamics to be algebraically integrated at the micro-, meso- and macro-levels. The new representation builds a lattice hierarchy in a way that integrates non-partitional hierarchies of links and routes based on the usual hierarchy of geographical zones, e.g. neighbourhoods, districts, cities, counties and countries. Applying the representation to a big city starts by defining sets of zones at different levels. At the first level, N, is the street. This can be subdivided to building plots at level N-1, buildings at level N-2, and even rooms at level N-3. At level N+1 are the neighbourhoods, at level N+2 is the set of district zones (each of them containing the different neighbourhoods in the previous level), and at the top level N+3 (in this case), is just one zone, the city itself. If a larger study area is to be considered, we would have a whole set of N+3 zones defining N+4-level areas, and so on, extending to the level of counties, countries or even continents. This paper will explain the fundamentals of TRANSIMS technology and compare it to other systems. We will show how TRANSIMS and the new multi-level representation can be brought together to give new insights into the macro-dynamics of very large road systems such as London, England and even the whole of Europe

Multi-level agent-based modeling - A literature survey

During last decade, multi-level agent-based modeling has received significant and dramatically increasing interest. In this article we present a comprehensive and structured review of literature on the subject. We present the main theoretical contributions and application domains of this concept, with an emphasis on social, flow, biological and biomedical models.Comment: v2. Ref 102 added. v3-4 Many refs and text added v5-6 bibliographic statistics updated. v7 Change of the name of the paper to reflect what it became, many refs and text added, bibliographic statistics update

The Quest for Scalability and Accuracy in the Simulation of the Internet of Things: an Approach based on Multi-Level Simulation

This paper presents a methodology for simulating the Internet of Things (IoT) using multi-level simulation models. With respect to conventional simulators, this approach allows us to tune the level of detail of different parts of the model without compromising the scalability of the simulation. As a use case, we have developed a two-level simulator to study the deployment of smart services over rural territories. The higher level is base on a coarse grained, agent-based adaptive parallel and distributed simulator. When needed, this simulator spawns OMNeT++ model instances to evaluate in more detail the issues concerned with wireless communications in restricted areas of the simulated world. The performance evaluation confirms the viability of multi-level simulations for IoT environments.Comment: Proceedings of the IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications (DS-RT 2017

Distributed Hybrid Simulation of the Internet of Things and Smart Territories

This paper deals with the use of hybrid simulation to build and compose heterogeneous simulation scenarios that can be proficiently exploited to model and represent the Internet of Things (IoT). Hybrid simulation is a methodology that combines multiple modalities of modeling/simulation. Complex scenarios are decomposed into simpler ones, each one being simulated through a specific simulation strategy. All these simulation building blocks are then synchronized and coordinated. This simulation methodology is an ideal one to represent IoT setups, which are usually very demanding, due to the heterogeneity of possible scenarios arising from the massive deployment of an enormous amount of sensors and devices. We present a use case concerned with the distributed simulation of smart territories, a novel view of decentralized geographical spaces that, thanks to the use of IoT, builds ICT services to manage resources in a way that is sustainable and not harmful to the environment. Three different simulation models are combined together, namely, an adaptive agent-based parallel and distributed simulator, an OMNeT++ based discrete event simulator and a script-language simulator based on MATLAB. Results from a performance analysis confirm the viability of using hybrid simulation to model complex IoT scenarios.Comment: arXiv admin note: substantial text overlap with arXiv:1605.0487

From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

A hybrid cross entropy algorithm for solving dynamic transit network design problem

This paper proposes a hybrid multiagent learning algorithm for solving the dynamic simulation-based bilevel network design problem. The objective is to determine the op-timal frequency of a multimodal transit network, which minimizes total users' travel cost and operation cost of transit lines. The problem is formulated as a bilevel programming problem with equilibrium constraints describing non-cooperative Nash equilibrium in a dynamic simulation-based transit assignment context. A hybrid algorithm combing the cross entropy multiagent learning algorithm and Hooke-Jeeves algorithm is proposed. Computational results are provided on the Sioux Falls network to illustrate the perform-ance of the proposed algorithm

Modeling the Internet of Things: a simulation perspective

This paper deals with the problem of properly simulating the Internet of Things (IoT). Simulating an IoT allows evaluating strategies that can be employed to deploy smart services over different kinds of territories. However, the heterogeneity of scenarios seriously complicates this task. This imposes the use of sophisticated modeling and simulation techniques. We discuss novel approaches for the provision of scalable simulation scenarios, that enable the real-time execution of massively populated IoT environments. Attention is given to novel hybrid and multi-level simulation techniques that, when combined with agent-based, adaptive Parallel and Distributed Simulation (PADS) approaches, can provide means to perform highly detailed simulations on demand. To support this claim, we detail a use case concerned with the simulation of vehicular transportation systems.Comment: Proceedings of the IEEE 2017 International Conference on High Performance Computing and Simulation (HPCS 2017

Design Patterns for Multilevel Modeling and Simulation

Multilevel modeling and simulation (M&S) is becoming increasingly relevant due to the benefits that this methodology offers. Multilevel models allow users to describe a system at multiple levels of detail. From one side, this can make better use of computational resources, since the more detailed and time-consuming models can be executed only when/where required. From the other side, multilevel models can be assembled from existing components, cutting down development and verification/validation time. A downside of multilevel M&S is that the development process becomes more complex due to some recurrent issues caused by the very nature of multilevel models: how to make sub-models interoperate, how to orchestrate execution, how state variables are to be updated when changing scale, and so on. In this paper, we address some of these issues by presenting a set of design patterns that provide a systematic approach for designing and implementing multilevel models. The proposed design patterns cover multiple aspects, including how to represent different levels of detail, how to combine incompatible models, how to exchange data across models, and so on. Some of the patterns are derived from the general software engineering literature, while others are specific to the multilevel M&S application area