49,321 research outputs found
Optimizing simulation on shared-memory platforms: The smart cities case
Modern advancements in computing architectures have been accompanied by new emergent paradigms to run Parallel Discrete Event Simulation models efficiently. Indeed, many new paradigms to effectively use the available underlying hardware have been proposed in the literature. Among these, the Share-Everything paradigm tackles massively-parallel shared-memory machines, in order to support speculative simulation by taking into account the limits and benefits related to this family of architectures. Previous results have shown how this paradigm outperforms traditional speculative strategies (such as data-separated Time Warp systems) whenever the granularity of executed events is small. In this paper, we show performance implications of this simulation-engine organization when the simulation models have a variable granularity. To this end, we have selected a traffic model, tailored for smart cities-oriented simulation. Our assessment illustrates the effects of the various tuning parameters related to the approach, opening to a higher understanding of this innovative paradigm
Traffic at the Edge of Chaos
We use a very simple description of human driving behavior to simulate
traffic. The regime of maximum vehicle flow in a closed system shows
near-critical behavior, and as a result a sharp decrease of the predictability
of travel time. Since Advanced Traffic Management Systems (ATMSs) tend to drive
larger parts of the transportation system towards this regime of maximum flow,
we argue that in consequence the traffic system as a whole will be driven
closer to criticality, thus making predictions much harder. A simulation of a
simplified transportation network supports our argument.Comment: Postscript version including most of the figures available from
http://studguppy.tsasa.lanl.gov/research_team/. Paper has been published in
Brooks RA, Maes P, Artifical Life IV: ..., MIT Press, 199
The Spatial Variability of Vehicle Densities as Determinant of Urban Network Capacity
Due to the complexity of the traffic flow dynamics in urban road networks,
most quantitative descriptions of city traffic so far are based on computer
simulations. This contribution pursues a macroscopic (fluid-dynamic) simulation
approach, which facilitates a simple simulation of congestion spreading in
cities. First, we show that a quantization of the macroscopic turning flows
into units of single vehicles is necessary to obtain realistic fluctuations in
the traffic variables, and how this can be implemented in a fluid-dynamic
model. Then, we propose a new method to simulate destination flows without the
requirement of individual route assignments. Combining both methods allows us
to study a variety of different simulation scenarios. These reveal fundamental
relationships between the average flow, the average density, and the
variability of the vehicle densities. Considering the inhomogeneity of traffic
as an independent variable can eliminate the scattering of congested flow
measurements. The variability also turns out to be a key variable of urban
traffic performance. Our results can be explained through the number of full
links of the road network, and approximated by a simple analytical formula
Experiences with a simplified microsimulation for the Dallas/Fort Worth area
We describe a simple framework for micro simulation of city traffic. A medium
sized excerpt of Dallas was used to examine different levels of simulation
fidelity of a cellular automaton method for the traffic flow simulation and a
simple intersection model. We point out problems arising with the granular
structure of the underlying rules of motion.Comment: accepted by Int.J.Mod.Phys.C, 20 pages, 14 figure
PS-Sim: A Framework for Scalable Simulation of Participatory Sensing Data
Emergence of smartphone and the participatory sensing (PS) paradigm have
paved the way for a new variant of pervasive computing. In PS, human user
performs sensing tasks and generates notifications, typically in lieu of
incentives. These notifications are real-time, large-volume, and multi-modal,
which are eventually fused by the PS platform to generate a summary. One major
limitation with PS is the sparsity of notifications owing to lack of active
participation, thus inhibiting large scale real-life experiments for the
research community. On the flip side, research community always needs ground
truth to validate the efficacy of the proposed models and algorithms. Most of
the PS applications involve human mobility and report generation following
sensing of any event of interest in the adjacent environment. This work is an
attempt to study and empirically model human participation behavior and event
occurrence distributions through development of a location-sensitive data
simulation framework, called PS-Sim. From extensive experiments it has been
observed that the synthetic data generated by PS-Sim replicates real
participation and event occurrence behaviors in PS applications, which may be
considered for validation purpose in absence of the groundtruth. As a
proof-of-concept, we have used real-life dataset from a vehicular traffic
management application to train the models in PS-Sim and cross-validated the
simulated data with other parts of the same dataset.Comment: Published and Appeared in Proceedings of IEEE International
Conference on Smart Computing (SMARTCOMP-2018
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
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