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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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      <title>The mechanism of continuous monitoring of compliance with environmental requirements imposed on vehicles in operation</title>
      <link>https://trid.trb.org/View/1577263</link>
      <description><![CDATA[The article discusses the possibility of continuous monitoring of the technical condition of vehicles, namely the composition of exhaust gases. It proposes a mechanism that provides information on the composition of exhaust gases without installation of additional sensors, based on the analysis of the engine operating parameters available over the standard OBDII protocol. Application of artificial neural networks, allowing comparison of the array of engine operating parameters stored in the control unit with the array of values of hazardous components’ concentrations in exhaust gases, is described for result analysis. The results of experimental studies are provided as confirmation of the suggested hypotheses. An algorithm for continuous monitoring of compliance with environmental requirements, allowing preventing their possible violation, is developed.]]></description>
      <pubDate>Wed, 06 Feb 2019 16:18:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/1577263</guid>
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      <title>Video Cameras in Access Link Paratransit Vehicles</title>
      <link>https://trid.trb.org/View/1522711</link>
      <description><![CDATA[New Jersey Transit (NJ TRANSIT), under purview of the Americans with Disabilities Act (ADA) paratransit requirement, operates a fleet of 360 Access Link vehicles. The current Access Link paratransit fleet uses a short-segment video monitoring system. This system captures 20-second video clips generated upon G-force or manual triggers. Since implementation in the Access Link fleet, there have been significant changes to the technologies available and to contract cost structures of vendors. In addition, newer technologies on the market—especially continuous video monitoring systems—provide a greater amount of information and features than the currently installed systems. The research team conducted a national survey of transportation providers and found that 86 percent of the survey respondents use a continuous video monitoring system. Nearly all operators with a continuous video monitoring system reported satisfaction with their respective system, regardless of vendor. Continuous video monitoring system users cited many occurrences of their system capturing events and sequences that would not be triggered with a short segment system, including actions that lead up to an event. This additional footage greatly aided event investigation, incident resolution, and employee training efforts. Many benefits are difficult to quantify in dollars saved due to their relationship to employee safety, oversight, customer relations, etc. Therefore the benefits quantified focused on the reduction in liability and insurance claims. Overall, a continuous recording system provides additional features and captures significantly more events. This creates a potential for additional savings, which may be greater than increased costs of the more expensive system. Based on the results of the research, NJ TRANSIT could expect that: 1) without a system they would incur a liability/claim cost of $706,644 per year, 2) with the current segmented recording installation they incur a liability/claim cost of $293,519 per year, and 3) replacing their segmented system with a continuous recording installation “could” further reduce their liability/claim cost to $145,335 per year. These data are speculative and are statistically sensitive to claim trends and extreme claims resulting in large one-time payouts that vary significantly from the average claim cost. However, there are many additional tangible benefits associated with the additional information collected by a continuous system.]]></description>
      <pubDate>Mon, 16 Jul 2018 12:52:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/1522711</guid>
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    <item>
      <title>Bridge Monitoring</title>
      <link>https://trid.trb.org/View/1508570</link>
      <description><![CDATA[The main objective of the present project is to develop an indirect structural health monitoring (SHM) approach, in which one makes use of vibration data collected from sensors installed on vehicles as they traverse the bridge or other infrastructure, rather than from sensors installed directly on the structure. The work has proceeded in several stages. An initial exploratory study (funded by Traffic21) based entirely on mathematical models and computational simulations allowed the demonstration of the feasibility of an indirect monitoring approach for these mathematical models and the desirability of testing this technique on physical models. Subsequent support from the National Science Foundation enabled the conducting of the  first experimental study, based on a simple laboratory model of a bridge and a vehicle. Almost at the same time funding was received from the UTC T-SET transportation center for the main purpose of exploring and developing different techniques for performing signal analysis and classification of the data collected during the course of the experiments. In this project, the authors have not inflicted actual damage to the bridge structure. Instead, they used small, added masses to the bridge deck, dampers, or varying support conditions as proxies. In addition, the authors considered temperature as a parameter since it is well known that bridge properties can vary significantly with temperature changes. Most of the work in the project, especially the feature extraction and classification aspects, focused on data recorded during the experiments with the laboratory models. This allowed the authors to develop their detection capabilities in a controlled environment. In order to deal with more realistic systems, the authors selected two additional applications: (i) the 3rd level of the East Garage at Carnegie Mellon University (CMU) with a small robot as the vehicle. This set-up has the same general characteristics as the laboratory model, i.e., the structure tested consists essentially of a double-T beam with a deck, and the vehicle moves longitudinally along the beam; (ii) With the help of the UTC T-SET and the collaboration of the Port Authority of Allegheny County, the authors have begun to collect indirect data from Pittsburgh’s Light Rail Line. This application offers the opportunity to continuously collect real data and to analyze and classify the data with the objective of detecting damage in an actual operational environment. The authors describe the various activities and results in the main body of the report. These are organized into tasks, starting with the laboratory model (Tasks 1, 2, 3) followed by the work on the more realistic structures (Task 4).]]></description>
      <pubDate>Wed, 09 May 2018 16:22:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/1508570</guid>
    </item>
    <item>
      <title>Continuous Road Surface Distress Detection</title>
      <link>https://trid.trb.org/View/1508607</link>
      <description><![CDATA[State and local maintenance departments are tasked with keeping roads in good repair. One part of this task is to monitor the degradation of road surfaces, which manifests itself with the presence of cracks, potholes, and other distress. Currently, this is done by: (i) inspectors who visually judge the road condition, (ii) specialized vehicles which measure the distress with cameras or laser devices, or (iii) citizens who call in their observations. The first method is tedious and often inconsistent if several inspectors do the inspections. The second method does not have these problems, but it is generally expensive. The cost can range between $40 and $230 per mile. Because of budget constraints one stretch of road is usually only traversed by the specialized vehicle once every two years. This method is effective for interstates and highways, but it is not practical for the inner roads in a city. As for the third method, reports by citizens are generally only about severe problems, like large potholes on main roads. Each of the three methods has its strength, but none of them is able to monitor the road on a continuous basis at low cost with minimal human intervention. The approach of the road inspection system developed in this project is to use images or videos collected by commodity devices such as smartphones. The devices are mounted in vehicles that travel the roads for other purposes and therefore no dedicated vehicles or drivers are needed. The captured images are analyzed automatically with machine vision algorithms and the resulting distress scores are passed to the asset management system.]]></description>
      <pubDate>Wed, 09 May 2018 16:22:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/1508607</guid>
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    <item>
      <title>Assessing Roadway Traffic Count Duration and Frequency Impacts on Annual Average Daily Traffic Estimation: Assessing Accuracy Issues Related to Short-Term Count Durations</title>
      <link>https://trid.trb.org/View/1442739</link>
      <description><![CDATA[Annual Average Daily Traffic (AADT) for many roadways is estimated through a temporary count obtained over anywhere from a few hours to one week, and subsequently expanded to a full year using factors derived from permanent count stations with similar characteristics. Historic research has focused on determining the optimum duration for the short-term count to balance resource restrictions, which favor shorter count durations, against statistical accuracy, which favors longer count durations. This task quantifies the relative accuracy and precision associated with different durations. It also evaluates the impact of day of week and month of year for AADT estimation. Results are evaluated for different factoring methods, and different factor grouping methods. The Federal Highway Administration (FHWA) Travel Monitoring Analysis System (TMAS) volume data from 14 years consisting of hourly counts by day from nearly 43,000 continuous permanent volume traffic data sites/years in the United States comprised the reference dataset for this research. A subset of 320 of these were utilized which include complete data for all 24 hours of every day of the year. These sites collectively represented a wide range of AADT volumes, nine functional classes, 32 states, and years 2000 through 2012. This report is a final task report that summarizes accuracy and precision of AADT estimation for various short-term count durations, by day of week and month of year in which count was obtained, and includes the analysis methodology and summary statistics findings.]]></description>
      <pubDate>Sat, 11 Feb 2017 17:01:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/1442739</guid>
    </item>
    <item>
      <title>Assessing Roadway Traffic Count Duration and Frequency Impacts on Annual Average Daily Traffic Estimation: Evaluating Special Event, Recreational Travel, and Holiday Traffic Variability</title>
      <link>https://trid.trb.org/View/1442741</link>
      <description><![CDATA[The Federal Highway Administration (FHWA) Travel Monitoring Analysis System (TMAS) volume data were utilized from 418 sites/years in the United States where data were available for all 24 hours of every day of the year. These sites collectively represented a wide range of annual average daily traffic (AADT) volumes, 9 functional classes, 35 states, and years 2000 through 2012. The TMAS hourly data were converted to daily ratios of volume to the overall AADT for the site. These daily volume ratios were fit to statistical analysis of variance models to estimate the mean changes in volume for national holidays and the days surrounding them. Further subsets of sites were utilized to model the traffic impacts of roadways near recreational areas and associated with special events. The report includes the analysis methodology and summary statistics findings.]]></description>
      <pubDate>Sat, 11 Feb 2017 17:01:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/1442741</guid>
    </item>
    <item>
      <title>Assessing Roadway Traffic Count Duration and Frequency Impacts on Annual Average Daily Traffic Estimation: Assessing Accuracy Issues Related to Annual Factoring</title>
      <link>https://trid.trb.org/View/1442740</link>
      <description><![CDATA[Annual average daily traffic (AADT) for many roadways is estimated through a temporary count obtained over anywhere from a few hours to one week, and subsequently expanded to a full year using factors derived from permanent count stations with similar characteristics. Many organizations perform these counts less than annually and therefore must further adjust counts from a prior year (typically 1, 2, or 5 years prior, depending on the roadway type) to the current year. This task quantifies the relative accuracy and precision associated with different annual count cycles for 24- and 48-hour count durations. Results are evaluated at the national level, as well as by functional classification of sites, and at the individual state level. The Federal Highway Administration (FHWA) Travel Monitoring Analysis System (TMAS) volume data from 14 years consisting of hourly counts by day from nearly 6,000 continuous permanent volume traffic data sites/years in the United States comprised the reference dataset for this research. A subset of 320 of these were utilized which include complete data for all 24 hours of every day of the year. These sites collectively represented a wide range of AADT volumes, 9 functional classes, 32 states, and years 2000 through 2012. This report is a final task report that summarizes accuracy and precision of expanding short-term counts’ Average Daily Traffic (ADT) to AADT, depending on the frequency with which the counts are obtained (yearly, every two years, every three years, or every six years), and includes the analysis methodology and summary statistics findings.]]></description>
      <pubDate>Sat, 11 Feb 2017 17:01:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/1442740</guid>
    </item>
    <item>
      <title>Assessing Roadway Traffic Count Duration and Frequency Impacts on Annual Average Daily Traffic Estimation: Assessing Accuracy Issues with Current Known Methods in AADT Estimation from Continuous Traffic Monitoring Data</title>
      <link>https://trid.trb.org/View/1442738</link>
      <description><![CDATA[Numerous factoring and baseline values are required to ensure annual average daily traffic (AADT) data are collected and reported correctly. The variability of numerous methods currently used are explored so that those in the traffic community will clearly know the limitations and the extent of each method used and how to properly utilize methods for their agency to obtain the necessary results. Federal Highway Administration (FHWA) Travel Monitoring Analysis System (TMAS) data from 14 years consisting of 24 hours of the day and 7 days of the week volume data from over 6000 continuous permanent volume traffic data sites in the United States comprised the reference dataset for this research. Randomly selected (with some constraints) sites each include one year of 100% complete daily reporting and the set of sites represent 12 functional classes, years 2000 through 2013, 43 states and DC, and various volume ranges. Four AADT estimation methods were examined for accuracy when data from various time periods were removed. This report is a final task report that summarizes identified inaccuracies with current methods that are used for AADT estimation, and includes the analysis methodology and summary statistics findings.]]></description>
      <pubDate>Sat, 11 Feb 2017 17:01:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/1442738</guid>
    </item>
    <item>
      <title>Long-term monitoring can support noise mitigation</title>
      <link>https://trid.trb.org/View/1406688</link>
      <description><![CDATA[Continuous acoustic monitoring of railways is important to accurately assess the success or failure of noise mitigation measures, as experience from Switzerland suggests. In refining both railroad vehicle and track maintenance methods, it can also play an important role. The Federal Office for Transport (BAV) in Switzerland has been monitoring railway noise since the early 1970s, and it has been using six permanent monitoring stations to assess the effectiveness of various noise reduction measures since 2003.]]></description>
      <pubDate>Fri, 20 May 2016 15:54:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/1406688</guid>
    </item>
    <item>
      <title>Keeping the goods on track</title>
      <link>https://trid.trb.org/View/1326525</link>
      <description><![CDATA[The availability and reliability of freight rolling stock is being improved by continuous monitoring, which is an essential input to condition-based maintenance (CBM) strategies. Condition-based strategies take into account the condition of critical components in order to predict their remaining usable life. The Cargo-CBM research project was launched in 2011 in order to develop such a condition-based maintenance system and is supported by Germany's Federal Ministry of Economics & Technology (BMWi) and is led by Technische Universitat Berlin, which has developed the diagnostic algorithms at the core of the system. Each vehicle is fitted with an onboard unit (OBU) which incorporates the signals from numerous sensors, preprocesses some data, and transmits the essential information back to the ground system.]]></description>
      <pubDate>Tue, 21 Oct 2014 14:11:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/1326525</guid>
    </item>
    <item>
      <title>JR East aims at condition-based maintenance</title>
      <link>https://trid.trb.org/View/1290707</link>
      <description><![CDATA[East Japan Railway is developing the 'smart maintenance' concept, which is based on continuous monitoring of track condition using trains in revenue service and involves a switch from time-based interventions to a condition-based maintenance regime. Comparisons between the two systems of maintenance are presented, as well as inspection devices that may be used as part of the monitoring systems.]]></description>
      <pubDate>Fri, 21 Feb 2014 15:18:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/1290707</guid>
    </item>
    <item>
      <title>Keeping an eye on fishplate strains</title>
      <link>https://trid.trb.org/View/1114650</link>
      <description><![CDATA[In the continuing battle to maintain railways that are increasingly busy, innovative monitoring techniques have a role to play. As growing traffic and demands for 24-hour operation place increased strain on rail and light rail networks, while simultaneously reducing time available for maintenance, continuous monitoring of infrastructure components is becoming more attractive. Testing on the Sheffield Supertram network is underway in order to investigate the use of optic fibre strain sensors, a multiplexed array of which are used to monitor stresses in rail joints and other track components. The results of these field trials are presented here.]]></description>
      <pubDate>Wed, 14 Sep 2011 11:13:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/1114650</guid>
    </item>
    <item>
      <title>Towards Sensor-Based Health Monitoring Systems for Bridge Decks: A Full-Depth Precast Deck Panels Case Study</title>
      <link>https://trid.trb.org/View/1107391</link>
      <description><![CDATA[Traffic and variable loading conditions greatly influence the performance, durability, and safety of a bridge structure throughout its service life.  Continuous monitoring can provide the basis for determining the deterioration rate and for estimating the remaining service life, thus assisting in making important decisions regarding bridge maintenance. This paper presents the design and implementation of a health monitoring system for condition assessment of full-depth precast concrete bridge deck, which was developed for the Parkview Bridge in Kalamazoo, Michigan. This system is composed of a remotely accessible on-site data acquisition system and a sensor network of vibrating wire strain gauges to monitor strain and temperature over given time increments.  The system relies on the sensor network embedded in the bridge deck to gather static performance data under different loading conditions to provide condition assessment by collecting, storing, analyzing, and reporting relevant performance data over time. The paper presents a practical case study that mainly focuses on describing the initial steps in the development of the sensor network system, namely, the design (system architecture and data structures) and construction along with examples of how the data is acquired, organized, presented, and analyzed, keeping in mind that the bridge is still early in its life-cycle and has not yet experienced any structural problems.]]></description>
      <pubDate>Tue, 26 Jul 2011 08:51:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/1107391</guid>
    </item>
    <item>
      <title>High-Precision GPS for Continuous Monitoring of Rail</title>
      <link>https://trid.trb.org/View/839597</link>
      <description><![CDATA[Estimation of rail position and geometry using a high-precision differential Global Positioning System (GPS) can be used to monitor the movement of rail.  Because rail naturally expands and contracts during temperature changes, lack of movement during severe temperature change would indicate significant rail stress.  Point-specific monitoring can detect slippage of rail down a mountainside or other longitudinal rail movement.  High-accuracy measurements might be useful for a variety of future rail applications, including identifying buckle precursors and predicting rail failure.  Measurements collected from a moving platform would be most practical for mapping long sections of rail.  In this Innovations Deserving Exploratory Analysis (IDEA) project, the author compared raw and post-processed GPS trajectory measurements, collected from a high-railer, to stationary benchmark points.  The GPS system had an average error distance of 5-10 cm to the benchmark points.  However, there was some calibration error and position variation between the GPS antenna and the rail; this error could be reduced with a more advanced system setup.  The bulk of the error came from problems with GPS kinematic convergence.  Because of integer ambiguities in the carrier signal, the GPS receiver does not always reliably estimate the correct number of carrier frequency cycles between the receiver and satellites.  For short track segments, the integer ambiguity is observed as a constant bias in the data.  A least-squares offset was calculated to remove this bias.  Signal processing smoothing methods removed some of the inherent jaggedness of the trajectory data.  After smoothing and bias removal, many of the trajectory measurements had an average error distance of about 1-2 cm to the stationary benchmark points.  This reduced error is what might be achieved if the integer cycle ambiguity problem can be well solved.  GPS technology could potentially be used to detect geometric precursors to rail buckles, if such precursors exist.  This report presents a buckle detection model that takes as input a rail trajectory corrupted by measurement noise and computes the probability that a low amplitude sinusoidal mode can be identified in the measurement data.  The author finds that a GPS system operating at 3 cm accuracy could reliably find a low amplitude sinusoidal mode having amplitude 5-10 cm across a 50 m section.]]></description>
      <pubDate>Wed, 31 Oct 2007 14:38:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/839597</guid>
    </item>
    <item>
      <title>A Bridge Structural Health Monitoring and Data Mining System</title>
      <link>https://trid.trb.org/View/835439</link>
      <description><![CDATA[Structural health monitoring (SHM) is becoming a more widely accepted way to improve bridge management. In 2005, a fiber optic SHM system was developed and deployed by the Iowa State University Bridge Engineering Center to continuously monitor bridge performance under ambient traffic loads and to detect potential gradual deterioration or sudden damage, specifically for Iowa’s fracture critical bridges. Strain time history data collected by this system were utilized to construct a baseline model that is based upon extreme-matching distribution. Structural responses deviating from the baseline distribution are considered as indication of damage or degradation. As a means to improve the damage/deterioration prediction capabilities of the above mentioned system, it is postulated that the dispersion of the extreme-matching data could be minimized using truck (position, type, etc.) information. Thus, techniques for the determination of detailed truck information are being investigated. Finite element analysis was carried out to verify the proposed truck detection and damage detection algorithms. In this paper, the SHM system, relevant autonomous data mining results, and numerical verification are presented. Moreover, ongoing efforts in estimating the truck geometry/type, weight, and velocity are described.]]></description>
      <pubDate>Fri, 21 Sep 2007 13:54:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/835439</guid>
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