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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Parallel Traffic Micro-Simulation by Cellular Automata and Application for Large Scale Transportation Modeling</title>
      <link>https://trid.trb.org/View/1875083</link>
      <description><![CDATA[A set of very simple driving rules for single lane roadway traffic gives surprisingly realistic results. This observation can be backed up by theory, showing relations both to fluid-dynamical models for traffic, and to control system approaches to driving. Extensions to multi-lane traffic are straightforward and work well. Because of its simplicity, it is easy to implement the model on supercomputers (vectorizing and parallel), where real time limits of more than 4 million kilometers (or more than 53 million vehicle sec/sec) have been achieved. The model can be used for applications where both high simulation speed and individual vehicle resolution are needed. The authors describe results of freeway network simulations of the German land Northrhine-Westfalia, where drivers have individual route plans which they adapt according to the delays they experience. Finally, the authors discuss the TRANSIMS microsimulation design, where such a simple high-speed microsimulation will be used as one microsimulation option.]]></description>
      <pubDate>Mon, 20 Sep 2021 16:58:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/1875083</guid>
    </item>
    <item>
      <title>Fast Low Fidelity Microsimulation of Vehicle Traffic on Supercomputers</title>
      <link>https://trid.trb.org/View/1875152</link>
      <description><![CDATA[A set of very simple rules for driving behavior used to simulate roadway traffic gives realistic results. Because of its simplicity, it is easy to implement the model on supercomputers (vectorizing and parallel), where the author has achieved real time limits of more than 4~million~kilometers (or more than 53~million vehicle sec/sec). The model can be used for applications where both high simulation speed and individual vehicle resolution are needed. The author uses the model for extended statistical analysis to gain insight into traffic phenomena near capacity, and the author discusses that this model is a good candidate for network routing applications.]]></description>
      <pubDate>Mon, 20 Sep 2021 16:58:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/1875152</guid>
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    <item>
      <title>Parallel computing in railway research</title>
      <link>https://trid.trb.org/View/1705589</link>
      <description><![CDATA[Available computing power for researchers has been increasing exponentially over the last decade. Parallel computing is possibly the best way to harness computing power provided by multiple computing units. This paper reviews parallel computing applications in railway research as well as the enabling techniques used for the purpose. Nine enabling techniques were reviewed and Message Passing Interface, Domain Decomposition and Hadoop & Apache are the top three most widely used enabling techniques. Seven major application topics were reviewed and iterative optimisations, continuous dynamics and data & signal analysis are the most widely reported applications. The reasons why these applications are suitable for parallel computing were discussed as well as the suitability of various enabling techniques for different applications. Computing time speed-ups that were reported from these applications were summarised. The challenges for applying parallel computing for railway research are discussed.]]></description>
      <pubDate>Thu, 18 Jun 2020 09:43:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/1705589</guid>
    </item>
    <item>
      <title>A Large-Scale Noise Map Realizing Method on a Supercomputer</title>
      <link>https://trid.trb.org/View/1495291</link>
      <description><![CDATA[In this paper, with the goal of controlling noise pollution, a systematic methodology for computing large-scale 3D traffic noise maps on supercomputer is presented. The supercomputer used to compute noise map in this paper is Tianhe-2. The noise of the receiver point is predicted by using an efficient model based on spatial geometry. Because of the computing complexity of a large-scale noise map in three dimensions, a parallel algorithm focused on controlling the compute nodes of the supercomputer to compute the noise map is designed and includes the tasks of map tiling and coordinating the compute nodes. Moreover, a method for rendering the computing noise data is provided to visualize the noise map. In addition, a strategy for obtaining a dynamic noise map is elaborated to support the real-time noise map. In the end, two efficiency experiments are implemented. One experiment involves comparing the expansibility of the parallel algorithm with various numbers of compute nodes and various densities of receiver points in a map to determine the expansibility. With an increase in the number of compute nodes, the computing time increases linearly when the computing scale is fixed. With an increase in the computing scale, the computing efficiency increases when the number of compute nodes is fixed. The other experiment is a comparison of the computing speed between a supercomputer and a normal computer to evaluate the computing performance of the supercomputer; the computing node of Tianhe-2 is found to be six times faster than that of a normal computer.]]></description>
      <pubDate>Mon, 26 Feb 2018 13:45:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/1495291</guid>
    </item>
    <item>
      <title>Real-Time Frequency-Domain Decomposition for Structural Health Monitoring Using General-Purpose Graphic Processing Unit</title>
      <link>https://trid.trb.org/View/1325122</link>
      <description><![CDATA[In order to analyze the data output of structural health monitoring (SHM) systems, civil engineers use frequency-domain decomposition (FDD) to identify the modal properties of structures. FDD is computationally expensive and it prevents central processing units (CPUs) from achieving real-time performance. The article discusses how SHM systems are becoming larger and shows how a CPU takes seconds to perform FDD of 16 input signals but minutes to perform FDD of hundreds of input signals. A supercomputer can achieve real-time performance but it cannot be installed near a civil structure because it is bulky, expensive, and requires constant maintenance. FDD is performed using a general-purpose graphic processor unit (GPGPU) because a graphic processor unit (GPU) is capable of massive parallel computing. A GPU is energy efficient and does not require the maintenance of a supercomputer and can be installed inside a base station at a structure site. The developed parallel FDD algorithm is up to hundreds of times faster than its serial version on CPU. For SHM of civil structures, where natural frequencies are less than 20 Hz parallel FDD a single GPU achieves real-time performance and the use of GPGPU offers many advantages because the modal properties are tracked in real time.]]></description>
      <pubDate>Wed, 29 Oct 2014 11:26:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/1325122</guid>
    </item>
    <item>
      <title>Accelerating Large-Scale Distributed Traffic Simulation with Adaptive Synchronization Method</title>
      <link>https://trid.trb.org/View/1321101</link>
      <description><![CDATA[In an era where traffic problems are critical for realizing smarter cities, large-scale and real-time traffic simulations are becoming important. To enable such a simulation in highly distributed environment such as supercomputers, we have built a microscopic traffic simulator called Megaffic on top of an X10-based distributed agent-based simulation framework. In previous work, the authors have found out that microscopic approach, by representing each vehicle as one agent, makes the synchronization serious bottleneck to realize a nearly scalability in distributed environment. In this paper, the authors propose a new approach that accelerates large-scale agent-based simulations by adaptively adjusting synchronization granularity. The tradeoff exists in that the precision of the simulation result might be lost to some extent, however the authors design our method in a way of not losing the precision as much as possible. In their experiment, the authors have used 192 central processing unit (CPU) cores and the Tokyo road network data in a supercomputer and validated that their proposed method achieves at least 2.5 times speed-ups without sacrificing much precision with the comparison of the regular synchronization method.]]></description>
      <pubDate>Thu, 28 Aug 2014 10:18:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/1321101</guid>
    </item>
    <item>
      <title>Using Supercomputers to Determine Bridge Loads</title>
      <link>https://trid.trb.org/View/873927</link>
      <description><![CDATA[Researchers at the Federal Highway Administration and the new Transportation Research and Analysis Computing Center (a partnership of the U.S. Department of Transportation and the Argonne National Laboratory) are using supercomputers with multidimensional hydraulics programs to study hydrodynamic forces on flooded bridge decks.  These supercomputer programs can closely mimic real-life conditions. A k-(epsilon) turbulence model and large eddy simulation together with the volume of fluid method simulates the flow past the bridge deck in an open channel. The predictions of drag, lift, and moment coefficients through the numerical modeling show a trend similar to flume experimental results. Bridge designers will be able to use the numerical computational fluid dynamics (CFD) model to obtain the coefficients on a bridge deck in an open channel for various flow conditions encountered in practice. Researchers are undertaking additional simulations for other shapes of bridge decks to predict the drag, lift, and moments with the help of CFD.  It is hoped that improved hydraulic and scour estimation using these computer programs will benefit both the planning and design of bridges.]]></description>
      <pubDate>Tue, 25 Nov 2008 07:30:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/873927</guid>
    </item>
    <item>
      <title>A MASSIVELY PARALLEL TIME-DEPENDENT LEAST-TIME-PATH ALGORITHM FOR INTELLIGENT TRANSPORTATION SYSTEMS APPLICATIONS</title>
      <link>https://trid.trb.org/View/694077</link>
      <description><![CDATA[ pending]]></description>
      <pubDate>Wed, 31 Oct 2001 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/694077</guid>
    </item>
    <item>
      <title>ADVANCED ANALYSIS OF STEEL FRAMES USING PARALLEL PROCESSING AND VECTORIZATION</title>
      <link>https://trid.trb.org/View/693095</link>
      <description><![CDATA[The two analytical methods commonly used by researchers for the inelastic analysis of structural steel frameworks are plastic hinge models and distributed plasticity (plastic zone) models. The paper presents an application of plastic zone analysis using advanced frame analysis algorithm with which parallel processing and vector computation can be implemented in the advanced analysis of large-scale structural steel frames.  Applicable measures for evaluating program speedup and efficiency on a Cray Y-MP C90 multiprocessor supercomputer are described.  Program performance (speedup and efficiency) for parallel and vector processing is evaluated.  Nonlinear response including postcritical branches of three large-scale fully restrained and partially restrained steel frameworks is computed using the proposed method.  The results of the study indicate that advanced analysis of practical steel frames can be accomplished using plastic zone analysis methods and alternate computational strategies.]]></description>
      <pubDate>Mon, 17 Sep 2001 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/693095</guid>
    </item>
    <item>
      <title>A MASSIVELY PARALLEL TIME-DEPENDENT LEAST-TIME-PATH ALGORITHM FOR INTELLIGENT TRANSPORTATION APPLICATIONS</title>
      <link>https://trid.trb.org/View/693097</link>
      <description><![CDATA[The development of intelligent transportation systems (ITS) and the resulting need for real-time traffic management and route guidance models have renewed interest in shortest-path algorithms.  One of the premises of ITS is to reduce a traveler's trip time by using current information about prevailing traffic conditions in the network; raw data are collected from the network, processed by a central controller or an onboard computer, and presented to the user in an easily understood form, usually a route.  The authors have developed a means of computing in parallel time-dependent least-time paths that can be used in real-time ITS applications.  A message-passing scheme is presented, and its correctness is proved.  The algorithm is implemented, coded, and computationally tested on actual and random networks with promising results.  The algorithm is implemented on a CRAY-T3D supercomputer using a Parallel Virtual Machine environment that allows portability to lower-end multiprocessor machines.]]></description>
      <pubDate>Mon, 17 Sep 2001 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/693097</guid>
    </item>
    <item>
      <title>GO SLOWER, GET THERE FASTER</title>
      <link>https://trid.trb.org/View/688015</link>
      <description><![CDATA[Traffic in the U.S. has worsened markedly over the last several years. In a recent study of 68 urban areas, the Texas Transportation Institute found that drivers now spend 350% more time stopped in traffic than they did just 15 years ago. This special technology report contains a series of articles on various national efforts to help reduce traffic congestion and negative environmental impacts such as air pollution from motor vehicle emissions. The technologies and planning solutions discussed include: defense supercomputer systems retasked to combat traffic problems; greater use of traffic circles to reduce delays and fatalities; advanced battery-powered bicycle vehicles; urban design efforts to encourage harmony between cars and pedestrians; and alternative-fuel engine vehicles powered by methanol, hydrogen, and electricity.]]></description>
      <pubDate>Thu, 05 Jul 2001 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/688015</guid>
    </item>
    <item>
      <title>MODELLING VEHICLE BEHAVIOUR IN CONGESTED NETWORKS USING HIGHLY PARALLEL SYSTEMS</title>
      <link>https://trid.trb.org/View/649380</link>
      <description><![CDATA[This paper describes a model which adopts a parallel, rather than a sequential approach, that allows the detailed behavior of traffic moving along roads and through junctions to be simulated as a set of processes. The aim of MOSTFLOW (MOdelling of Saturated Traffic FLOW) is to design a highly parallel microscopic simulation of an urban traffic network.  The paper describes the basic structure of MOSTFLOW queuing dynamics, and the design of the MOSTFLOW prototype.]]></description>
      <pubDate>Fri, 17 Nov 2000 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/649380</guid>
    </item>
    <item>
      <title>FUNDAMENTAL ISSUES IN INTELLIGENT TRANSPORTATION SYSTEMS</title>
      <link>https://trid.trb.org/View/650355</link>
      <description><![CDATA[In this paper, the author reviews the principles and issues associated with Intelligent Transportation Systems (ITS).  A discussion is also presented regarding motivations for further research into the modelling, simulation, and visualization of ITS.]]></description>
      <pubDate>Thu, 23 Mar 2000 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/650355</guid>
    </item>
    <item>
      <title>UPDATED PROBE MONITORS WATER CONTINUOUSLY</title>
      <link>https://trid.trb.org/View/650575</link>
      <description><![CDATA[Scientists at Oregon State University have refined the zero-angle photon spectrometer (ZAPS), developed in the early 1990s, to provide continuous realtime monitoring of water quality in rivers and water treatment plants.  The probe sits in the water and uses fiber optics and ultraviolet light to detect trace metals, organic matter, and nitrates.  A computer housed within the probe then sends the data to the university's supercomputers for storage and analysis.  The ZAPS probe can also measure organic compounds that are the natural byproduct of decaying vegetation and that can attract and increase the toxicity of such manmade materials as dioxins.  Currently, the probe cannot identify dioxins itself, but indicators will eventually be developed to detect their presence.]]></description>
      <pubDate>Sun, 12 Mar 2000 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/650575</guid>
    </item>
    <item>
      <title>STRATEGIES FOR REAL-TIME SPATIAL ANALYSIS IN A GIS FRAMEWORK : AN APPLICATION TO REAL-TIME TRAFFIC FLOW MODELING ON MASSIVELY PARALLEL COMPUTERS</title>
      <link>https://trid.trb.org/View/636674</link>
      <description><![CDATA[In this dissertation, the author presents strategies for real time spatial analysis using geographic information systems (GIS) and parallel processing.  Real time data visualization approaches including tracking, steering, and post processing are studied. Methods for visualization design are proposed using spatial, temporal and attribute domains.  Data from synthetic and real networks are used to show that at real time traffic flow analysis is achievable]]></description>
      <pubDate>Wed, 16 Feb 2000 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/636674</guid>
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