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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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    <language>en-us</language>
    <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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Modeling real-world diesel car tailpipe emissions using regression-based approaches</title>
      <link>https://trid.trb.org/View/2342252</link>
      <description><![CDATA[The development of precise vehicle emission models is crucial for estimating vehicular exhaust emissions. Though measuring emissions using an on-board emissions measurement system can be promising, it is essential to improve the precision of emission rates (ERs) prediction through effective statistical methods. A novel framework of simple linear regression (SLR), support vector regression (SVR), and piecewise linear regression (PLR) approaches was employed to develop a speed-based emission model. In total, 30 trips data from six professional drivers were collected to understand the variability of tailpipe emissions. The developed SLR, SVR, and PLR models demonstrated high accuracy, as indicated by mean absolute percentage error (MAPE), root-mean-square error (RMSE), and coefficient of determination (R²) values. PLR outperformed SLR, and SVR in predicting CO, CO₂, HC and NOₓ ERs. These models can be useful tools for policymakers to understand emissions in heterogeneous traffic conditions and develop appropriate solutions to improve air quality.]]></description>
      <pubDate>Mon, 26 Feb 2024 08:52:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2342252</guid>
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    <item>
      <title>EGR Reference Allocation for Diesel Engine Air System Control</title>
      <link>https://trid.trb.org/View/1825911</link>
      <description><![CDATA[The control of the engine air system is an essential part for meeting the emission levels of current and upcoming legislation. Up to now different strategies were presented in the literature and also applied on real systems. Starting from simple single-input-single-output structures in combination with feedforward parts leading to advanced multi-input-multi-output approaches. Nevertheless, independent of the used control approach for each of them suitable references are necessary. Although it seems adequate to directly use the emission target quantities in a closed loop air system control, a fast and accurate measurement is seldom available. An alternative is to use intermediate quantities as references, like fresh air mass flow or oxygen concentrations, which represent the state of the air system. However, for control purposes each of these quantities has to be determined, i.e., measured or calculated. Moreover, it has to be considered that each sensor has different dynamics and accuracy in dependency of the given range and furthermore also system dynamics can influence the sensor readouts. In this work a method for controlling the exhaust gas recirculation valve of a diesel engine is proposed, where in contrary to standard approaches not only one but a combination of different reference quantities is used, with the aim to maintain predefined tailpipe nitric oxides emissions. The idea is to use allocation techniques and to combine different measurements with respect to their accuracy and dynamic properties, thus ensuring that in each operating point and time instant the most accurate quantity with respect to NOx is used. The determination of the static accuracy of each quantity was carried out by a NOx sensitivity analysis and the use of Gaussian error propagation. Considering the dynamic properties, response and rise times of the different sensors and physical quantities were taken into account. Finally, the control strategy was implemented and tested on simulation models and on an EU5 passenger car diesel engine on an engine testbed, showing satisfactory results.]]></description>
      <pubDate>Tue, 24 May 2022 10:05:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/1825911</guid>
    </item>
    <item>
      <title>Emissions Measurement System for Hybrid and Plug-in Hybrid Electric Vehicles Using Intermittent Sampling Strategy</title>
      <link>https://trid.trb.org/View/1828276</link>
      <description><![CDATA[Conventional constant volume sampling (CVS) is well known as a precision emissions measurement method, even though the concentrations of THC, NOX, CO and CH₄ emitted from vehicles are getting lower by improvement of emissions control devices.  Recently, fuel economy requirements have increased in many regions.  Hybrid electric vehicle (HEV), or plug-in hybrid electric vehicle (PHEV), is one of the solutions for fuel economy improvement.  HEVs and PHEVs have an all-electric range in which the internal combustion engines (ICEs) are completely shut down.  This operation results in a high dilution factor (DF) and low concentrations of gaseous components, including CO₂, in the CVS system.  Such dilution conditions directly cause an increase of numerical error for DF and an analysis error for gaseous components.  Furthermore, a small amount of air flow across exhaust catalysts, drawn by slightly negative tailpipe pressure generated by the CVS during ICE shutdown may influence emission results.  A new emissions measurement method which provides intermittent sampling synchronized with ICE operating modes has been introduced for HEVs and PHEVs.  Batch sampling is activated by this system only when the ICE is operating.  A fast response tailpipe shut-off valve which stops tailpipe flow during ICE shutdown has also been applied to this system in order to prevent catalysts from cooling by inadvertent air flow.  The proposed and conventional emissions measurement systems have been compared by PHEV emissions tests.  The results suggest the proposed system can improve emissions measurement accuracy by decreasing DF and increasing gaseous concentrations in the CVS system.]]></description>
      <pubDate>Thu, 09 Dec 2021 10:35:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/1828276</guid>
    </item>
    <item>
      <title>Numerical Analysis of Mass Emission Measurement Systems for Low Emission Vehicles</title>
      <link>https://trid.trb.org/View/1783136</link>
      <description><![CDATA[Numerical models of three kinds of mass emission measurement systems, i.e. the constant volume sampler (CVS) system, the mini-diluter system and the direct modal-mass measurement system have been built on PC using a software called Mathematica®. The models are capable of simulating gas compounds concentration in the CVS bags and mass emitted during a test, using the time trend exhaust emission patterns, the exhaust gas flow rate pattern, and initial setting values like dilution ratio. Major error factors in the measurement systems, such as H2O condensation, gas compounds present in ambient air, delay and smoothing of the gas stream, and performance of the analyzers, can also be introduced to the calculation. Using the models, various techniques to optimize the sampling system are quantitatively compared.]]></description>
      <pubDate>Thu, 09 Dec 2021 10:16:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/1783136</guid>
    </item>
    <item>
      <title>A Study of a Gasoline-Fueled Near-Zero-Emission Vehicle Using an Improved Emission Measurement System</title>
      <link>https://trid.trb.org/View/1782146</link>
      <description><![CDATA[This paper concerns research on an emission control system aimed at reducing emission levels to well below the ULEV standards. As emission levels are further reduced in the coming years, it is projected that measurement error will increase substantially. Therefore, an analysis was made of the conventional measurement system, which revealed the following major problems.Improvements were made to the conventional measurement system with the aim of resolving these problems. As a result, a system has been established that can measure emission levels of around one-tenth of the ULEV standards with much greater accuracy than the previous system.This improved measurement system is now being used to develop an exhaust gas aftertreatment system that integrates an electrically heated catalyst (EHC), a three-way catalyst (TWC) and an HC adsorber. In addition, the individual performance of the HC adsorber and the TWC has also been improved. Although this aftertreatment system is still at the research stage, preliminary test results indicate that it has the potential to reduce emission levels to one-tenth of the ULEV limits.]]></description>
      <pubDate>Thu, 09 Dec 2021 10:15:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/1782146</guid>
    </item>
    <item>
      <title>Estimating Light-Duty Vehicle Emission Factors using Random Forest Regression Model with Pavement Roughness</title>
      <link>https://trid.trb.org/View/1715523</link>
      <description><![CDATA[Pavement roughness would affect the running of vehicle movement, and thus possibly impact fuel consumption and vehicle emissions, the numerical relationships and analytical steps of which are, however, not yet well studied. The major objective of this paper is to quantify vehicular emission factors—hydrocarbons (HC), carbon monoxide (CO), oxides of nitrogen (NOx), and carbon dioxide (CO2)—and fuel consumption as a function of pavement roughness (the International Roughness Index [IRI]) and other factors. Within each operating mode identification (OMID) bins of vehicle operational status, a random forest regression model (RFRM) was identified to estimate emission factors and fuel consumption. The field test data, with a total length of 1,067.41?mi driving and 323,075 data pairs from one test vehicle, were used to train and validate models. The portable emissions measurement system (PEMS) and a smartphone application for IRI were employed for the tests in Texas, U.S., roadways. Results show that the optimum roughness conditions for lower emissions and fuel consumption are in categories B and C with moderate roughness. The root-mean-square error (RMSE) during training, testing, and validation processes of the RFRM are within 6.4%, implying a good fit of resulted models. IRI has the most OMID bins as number one predictor, followed by vehicle specific power (VSP) and speed. Through separated modeling for each OMID, the impacts of IRI are successfully grasped. It is recommended conducting more field measurements with more vehicle types. This would help with possible incorporation of vehicle emissions, fuel consumption, and other environmental factors into the pavement design, maintenance, and retrofitting process.]]></description>
      <pubDate>Thu, 25 Jun 2020 17:24:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/1715523</guid>
    </item>
    <item>
      <title>Creating an emission model based on portable emission measurement system for the purpose of a roundabout</title>
      <link>https://trid.trb.org/View/1638375</link>
      <description><![CDATA[Road transport is the main source of pollution to the environment in urban areas; therefore, there is a need to accurately estimate the amount of exhaust gases emitted by motor vehicles. The development of systems for measuring emissions of exhaust gases caused the exit from stationary chassis dynamometers to real road test. This paper presents an analysis of emission data from the PEMS system for real driving cycles of various types of vehicles, complying with EURO2-EURO6 standards, fueled with petrol, LPG, and diesel in urban, rural, and motorway areas as well as detailing roundabouts. The results show that in the range of roundabouts, there is an increased emission of harmful exhaust components, such as CO₂, THC, CO, and NOₓ. Due to the specific traffic conditions that prevail at the roundabout (acceleration, braking, acceleration to a certain speed), the methodology for creating an exhaust emission model for this type of objects has been proposed. Statistical analysis of the received boosted regression tree models based on the coefficient of regression, root mean square error, and mean absolute error and based on the visual assessment of the results show that the obtained models are well represented by real data. The obtained results of emission calculations on roundabouts may be used to identify areas of increased emission of harmful exhaust components, as well as an introduction to prepare new roundabout design guidelines concerning emission data.]]></description>
      <pubDate>Tue, 22 Oct 2019 14:42:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/1638375</guid>
    </item>
    <item>
      <title>Determination of PEMS Measurement Allowances for Gaseous Emissions Regulated Under the Heavy-Duty Diesel Engine In-Use Testing Program: Part 1 – Project Overview and PEMS Evaluation Procedures</title>
      <link>https://trid.trb.org/View/1430589</link>
      <description><![CDATA[Under the U.S. Environmental Protection Agency's (EPA's) Heavy-Duty In-Use Testing (HDIUT) program, emission of non-methane hydrocarbons (NMHC), carbon monoxide (CO), and oxides of nitrogen (NOx) have been regulated using Portable Emissions Measurement Systems (PEMS) during in-use field operation for heavy-duty on-highway diesel engines with 2007 or later model year designations. As directed by the EPA, the Engine Manufacturers Association (EMA), and the California Air Resources Board (CARB), additive emission measurement accuracy margins (measurement allowances) were experimentally determined for HDIUT to account for the measurement differences between laboratory testing with laboratory grade equipment and in-use testing with PEMS.         As part of a three-paper series, this paper summarizes the HDIUT measurement allowance program while focusing on the laboratory evaluations of the Sensors Inc. SEMTECH-DS PEMS. The objective of the project was to quantify PEMS measurement errors as compared to concurrent measurements from a 40 CFR Part 1065 compliant dynamometer test cell. PEMS measurement errors were also evaluated when subjected to various environmental disturbances. The error data recorded during laboratory testing were used to populate a Monte Carlo statistical model. The model was iterated to randomly combine the various sources of PEMS measurement errors, the results of which were used to determine the additive measurement allowances for in-use testing. Model validation exercises were conducted by comparing PEMS emission results with the Center for Environmental Research & Technology (CE-CERT) Mobile Emissions Laboratory during on-road testing.       ]]></description>
      <pubDate>Fri, 26 May 2017 11:31:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/1430589</guid>
    </item>
    <item>
      <title>Recovery of Tail Pipe Species Concentrations and Its Effect on Emissions Calculations from Raw Exhaust Gas Streams during Chassis Dynamometer Tests</title>
      <link>https://trid.trb.org/View/1434615</link>
      <description><![CDATA[This paper proposes a method to recover species concentrations at the tail pipe exit of heavy-duty vehicles during chassis dynamometer tests, and investigates its effect in the calculation of emissions from their raw exhaust streams. It was found that the method shown in this paper recovered the sharp peaks of the gas species. The effect on calculations was significant, as time-variant raw exhaust flow rate and emissions concentrations data are acquired continuously during a test (at 10 Hz), and their product is integrated during calculations. The response of the analyzer is delayed due to the time taken for transport of the sample gases from the probe tip to the analyzer, and deformed due to mixing and diffusion during this transport. This ‘convolution’ of the concentration data stream introduces an error in the final result, calculated in g/mile.         The convolution of the concentration data is corrected by the following method: the analyzer response (output) to a step change in the concentration of the species at the probe tip (input) is recorded to determine the ‘convolution function’. The inverse of the convolution function is applied to the output to recover the input concentrations at the probe tip. The signal processing is carried out in the frequency domain and appropriate filters and windowing are applied to improve the quality of the reconstructed signal. This deconvolution algorithm is then applied to the analyzer data collected during a test to recover the concentrations at the probe tip, which is thus corrected for time delay and signal deformation. The deconvoluted concentration data, exhaust flow rate data, and dynamometer data are used to calculate the emission of a gas species (g/mile).         Results for tests conducted in the laboratory for CO2, CO, NOx, NO, THC, and CH4 are presented. The results obtained from the ‘asrecorded’ raw exhaust data and reconstructed data are compared with those from bag samples for the same test. It is shown that the emissions calculations using deconvoluted concentration data from the raw exhaust stream match well with the results from the bag samples for the same test in a full-scale dilution emissions measurement system.       ]]></description>
      <pubDate>Thu, 05 Jan 2017 16:22:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/1434615</guid>
    </item>
    <item>
      <title>Effects of Errors on Vehicle Emission Rates from Portable Emissions Measurement Systems</title>
      <link>https://trid.trb.org/View/1262238</link>
      <description><![CDATA[Portable emissions measurement systems (PEMS) are useful for quantification of real-world vehicle activity, energy use, and emissions. However, there is no standard methodology for processing PEMS data; this can lead to errors in reported results. PEMS typically include tail-pipe exhaust gas and particle analyzers, Global Positioning System (GPS) receivers, engine sensors, and electronic control unit (ECU) data loggers. The sensitivity of estimated emission rates to random errors in measurements is quantified. Methods are evaluated for identification and correction of improper synchronization of PEMS, ECU, and GPS data streams and for road grade estimation. Estimated fuel use and emission rates for light- and heavy-duty vehicles are sensitive to errors in intake manifold absolute pressure and engine revolutions per minute values and in indicators of air-to-fuel ratio including carbon dioxide and oxygen concentrations. Synchronization can be aided by maximizing the Pearson correlation coefficient between two indicator variables and confirming the result by matching concurrent increases in indicator variables. The effect of improper synchronization on estimated modal emission rates is quantified. Modal average emission rates based on vehicle-specific power (VSP) are more sensitive to improperly synchronized engine versus GPS data. Improperly synchronized data streams result in decreased variability between the lowest and highest modal average emission rates. Estimation of road grade from a linear least squares slope of elevation over a specified distance is demonstrated. VSP-based modal fuel use and pollutant emission rates are less sensitive to differences in road grade than to errors in synchronization.]]></description>
      <pubDate>Fri, 13 Sep 2013 09:48:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/1262238</guid>
    </item>
    <item>
      <title>EXHAUST NOISE MODELLING FOR MUFFLER DESIGN. MOBILITY: THE TECHNICAL CHALLENGE; PROCEEDINGS OF THE FOURTH INTERNATIONAL PACIFIC CONFERENCE ON AUTOMOTIVE ENGINEERING, MELBOURNE, AUSTRALIA, NOVEMBER 8-14, 1987. VOLUMES 1 TO 3</title>
      <link>https://trid.trb.org/View/284974</link>
      <description><![CDATA[The design of mufflers for motor vehicles has traditionally been trial and error based.  This is still true of much of the vehicle exhaust industry.  In an attempt to improve upon trial and error methods, a computer model of the exhaust system was developed and used to assist in the acoustical aspects of exhaust system design.  The computer model is based on a linear acoustic analysis of the pulsating gas flow in the exhaust system and sound radiation at the tailpipe outlet.  In using the computer model the insertion loss or muffling effect caused by making a particular change to any part of the complete exhaust system may be predicted. As comparisons between calculations and measurements from an actual vehicle exhaust design project show, accuracy is very good.  SAE paper no 871198.  (Author/TRRL)]]></description>
      <pubDate>Wed, 31 Aug 1988 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/284974</guid>
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