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    <title>Transport Research International Documentation (TRID)</title>
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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>Estimates of Variances: Report 10, 1977 NPTS</title>
      <link>https://trid.trb.org/View/1896779</link>
      <description><![CDATA[This report is part of a series that presents data relating to the 1977 Nationwide Personal Transportation Study (NPTS). This report presents in tabular form estimates of variability referred to as standard errors based on information obtained from a probability sample of households interviewed for the NPTS. The tables contain standard errors for estimates of vehicle trips, person trips, and person miles, and household demographic characteristics. Estimates of standard errors of percentages have been prepared for vehicle trips, vehicle miles, person trips, and person miles for different trip purposes. In addition, standard errors of means are presented for average annual vehicle trips, vehicle miles, person trips, trip duration and trip distance.]]></description>
      <pubDate>Sat, 18 Dec 2021 19:48:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/1896779</guid>
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    <item>
      <title>Bootstrap standard error estimations of nonlinear transport models based on linearly projected data</title>
      <link>https://trid.trb.org/View/1667883</link>
      <description><![CDATA[Linear data projection is a commonly leveraged data scaling method for unbiased traffic data estimation. However, recent studies have shown that model estimations based on linearly projected data would certainly result in biased standard errors. Although methods have been developed to remove such biases for linear regression models, many transport models are nonlinear regression models. This study outlines the practical difficulties of the traditional approach to standard error estimation for generic nonlinear transport models, and proposes a bootstrapping mean value restoration method to accurately estimate the parameter standard errors of all nonlinear transport models based on linearly projected data. Comprehensive simulations with different settings using the most commonly adopted nonlinear functions in modeling traffic flow demonstrate that the proposed method outperforms the conventional method and accurately recovers the true standard errors. A case study of estimating a macroscopic fundamental diagram that illustrates situations necessitating the proposed method is presented.]]></description>
      <pubDate>Fri, 06 Dec 2019 14:04:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/1667883</guid>
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    <item>
      <title>Technological progress and other factors behind the adoption of electric vehicles: Empirical evidence for EU countries</title>
      <link>https://trid.trb.org/View/1576182</link>
      <description><![CDATA[This paper aims to analyse the factors supporting the transition to new forms of mobility, namely Electric Vehicles (EV). For a deep understanding of these effects, the authors analysed EV, by dividing them into individual Battery Electric Vehicles (BEV), which are 100% electric, and Plug-in Hybrid Electric Vehicles (PHEV). The factors examined include: policy, social, economic, environmental, and technical. This paper uses data from 2010 to 2016 for a panel of the 24 EU countries. A Panel-Corrected Standard Errors (PCSE) estimator is used. When comparing the results of analysing BEV and PHEV individually, and all EV together, they prove to be quite different. This finding indicates that policies should be tailored to each individual technology, rather than a single one for all EV. The proxy used for technological progress is the increased use of both BEV and PHEV. The evidence is also provided that charging stations are drivers of electric mobility.]]></description>
      <pubDate>Fri, 25 Jan 2019 10:34:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/1576182</guid>
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    <item>
      <title>Biased standard error estimations in transport model calibration due to heteroscedasticity arising from the variability of linear data projection</title>
      <link>https://trid.trb.org/View/1406719</link>
      <description><![CDATA[Reliable transport models calibrated from accurate traffic data are crucial for predicating transportation system performance and ensuring better traffic planning. However, due to the impracticability of collecting data from an entire population, methods of data inference such as the linear data projection are commonly adopted. A recent study has shown that systematic bias may be embedded in the parameters calibrated due to linearly projected data that do not account for scaling factor variability. Adjustment factors for reducing such biases in the calibrated parameters have been proposed for a generalized multivariate polynomial model. However, the effects of linear data projection on the dispersion of and confidence in the adjusted parameters have not been explored. Without appropriate statistics examining the statistical significance of the adjusted model, their validity in applications remains unknown and dubious. This study reveals that heteroscedasticity is inherently introduced by data projection with a varying scaling factor. Parameter standard errors that are estimated by linearly projected data without any appropriate treatments for non-homoscedasticity are definitely biased, and possibly above or below their true values. To ensure valid statistical tests of significance and prevent exposure to uninformed and unnecessary risk in applications, a generic analytical distribution-free (ADF) method and an equivalent scaling factor (ESF) method are proposed to adjust the parameter standard errors for a generalized multivariate polynomial model, based on the reported residual sum of squares. The ESF method transforms a transport model into a linear function of the scaling factor before calibration, which provides an alternative solution path for achieving unbiased parameter estimations. Simulation results demonstrate the robustness of the ESF method compared with the ADF method at high model nonlinearity. Case studies are conducted to illustrate the applicability of the ESF method for the parameter standard error estimations of six Macroscopic Bureau of Public Road functions, which are calibrated using real-world global positioning system data obtained from Hong Kong.]]></description>
      <pubDate>Wed, 25 May 2016 13:35:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/1406719</guid>
    </item>
    <item>
      <title>On finite sample performance of confidence intervals methods for willingness to pay measures</title>
      <link>https://trid.trb.org/View/1376297</link>
      <description><![CDATA[This paper systematically compares finite sample performances of methods to build confidence intervals for willingness to pay measures in a choice modeling context. It contributes to the field by also considering methods developed in other research fields. Various scenarios are evaluated under an extensive Monte Carlo study. Results show that the commonly used Delta method, producing symmetric intervals around the point estimate, often fails to account for skewness in the estimated willingness to pay distribution. Both the Fieller method and the likelihood ratio test inversion method produce more realistic confidence intervals for small samples. Some bootstrap methods also perform reasonably well, in terms of effective coverage. Finally, empirical data are used to illustrate an application of the methods considered.]]></description>
      <pubDate>Wed, 23 Dec 2015 08:12:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/1376297</guid>
    </item>
    <item>
      <title>Lessons Learned in Local Calibration of MEPDG for Louisiana Flexible Pavement Design</title>
      <link>https://trid.trb.org/View/1337030</link>
      <description><![CDATA[The latest survey on state highway agencies’ implementation efforts of the Mechanistic Empirical Pavement Design Guide (MEPDG) shows that the majority of states have on-going efforts at different stages, among which local calibration is a critical step. Although the overall procedure of local calibration as documented in the "Guide for the Local Calibration of the MEPDG" is well known, most literature has only reported how the overall procedure was tailored and applied to local conditions; very little has focused on details. The objective of this paper is to discuss several detailed obstacles, lessons learned and pitfalls during the process of local calibration in Louisiana. In particular, the procedure and impact of calibrating standard error are presented. Additionally, case studies on two major inputs, dynamic modulus of hot mix asphalt and traffic load spectra, are discussed. Results show that the calibration of standard error is an integral part with the calibration of fatigue cracking model. Due to the existence of a sensitive zone in the standard error curve, the calibration of standard error is more important than the calibration of cracking model in many cases. In addition, case studies show that the calibrated Pavement ME software is sensitive to new asphalt materials and load spectra. Laboratory tested dynamic modulus could reduce at least 0.5’’ of asphalt concrete from the corresponding Level 3 default input case.]]></description>
      <pubDate>Fri, 16 Jan 2015 08:28:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/1337030</guid>
    </item>
    <item>
      <title>The determinants of credit spreads changes in global shipping bonds</title>
      <link>https://trid.trb.org/View/1327052</link>
      <description><![CDATA[This paper investigates whether bond, issuer, industry and macro-specific variables account for the observed variation of credit spreads’ changes of global shipping bond issues before and after the onset of the subprime financial crisis. Results show that conclusions as to the significant variables of spreads depend significantly on whether two-way cluster-adjusted standard errors are utilized, thus rendering results in the extant literature ambigious. The main determinants of global cargo-carrying companies’ shipping bond spreads are found in this paper to be: the liquidity of the bond issue, the stock market’s volatility, the bond market’s cyclicality, freight earnings and the credit rating of the bond issue.]]></description>
      <pubDate>Mon, 24 Nov 2014 15:49:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/1327052</guid>
    </item>
    <item>
      <title>Interpreting Discrete Choice Models Based on Best-Worst Data: A Matter of Framing</title>
      <link>https://trid.trb.org/View/1289746</link>
      <description><![CDATA[Best worst choice response tasks have become increasingly popular as a means of increasing the amount of information captured from respondents undertaking stated preference experiments. In analysis, best worst data is often exploded to provide additional pseudo observations which may aid in model estimation. Recent studies however have questioned many of the underlying assumptions which typically accompany best worst studies, such as the symmetry of preferences across the best and worst responses as well as assumptions about equal error variances across the two response types. This paper first provides a detailed description of the various best worst tasks that have appeared within the literature before arguing that violations of preference symmetry and homogeneity of error variance should be the norm. This is because in asking respondent to choose their most and least preferred option out of a set of alternatives reflects different response frames, one positive and one negative, and behaviourally there exists no reason why one would assume that the preferences (and error variances) obtained from one type of question should precisely mirror that of the other. Using an empirical case study, the impact of the framing of these questions is examined. Finally, the argument put forward is that best-worst data should be treated in a similar to data fusion methods, where one combines two different sources of discrete choice data.]]></description>
      <pubDate>Wed, 26 Mar 2014 14:00:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/1289746</guid>
    </item>
    <item>
      <title>Traffic signal controllers: functional safety requirements</title>
      <link>https://trid.trb.org/View/1153182</link>
      <description><![CDATA[This European standard specifies the functional safety requirements for traffic signal controllers.  It is applicable to traffic signal equipment permanently and temporarily installed, but excludes portable traffic control equipment.]]></description>
      <pubDate>Wed, 22 Aug 2012 18:52:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/1153182</guid>
    </item>
    <item>
      <title>A framework for a critical assessment of the quality of epidemiological studies of driver health and accident risk</title>
      <link>https://trid.trb.org/View/1116561</link>
      <description><![CDATA[A large number of studies have tried to assess how various aspects of driver health influence driver involvement in accidents. The objective of this paper is to provide a framework for a critical assessment of the quality these studies from a methodological point of view. Examples are given of how various sources of bias and confounding can produce study findings that are highly misleading. Ten potential sources of error and bias in epidemiological studies of the contribution of driver health impairments to road accidents are discussed.  Examples are given of how all these items may influence the findings of a single study or make synthesising findings from multiple studies difficult. A checklist for assessing study quality is provided.]]></description>
      <pubDate>Fri, 23 Sep 2011 07:26:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/1116561</guid>
    </item>
    <item>
      <title>Traffic Simulation Runs: How Many Needed?</title>
      <link>https://trid.trb.org/View/1098936</link>
      <description><![CDATA[A microscopic simulation model is run multiple times for the roadway segment and traffic period being analyzed in order to determine the mean values that are needed to analyze transportation design alternatives.  To save money and time, engineers often elect to reduce the number of runs in microsimulations. However, this can lead to unreliable results. This article describes a Federal Highway Administration study that investigated the relationship between the number of traffic simulation runs and the aggregate results under various levels of error. The goal was to determine the level of simulation required for a calibrated network -- a model that reproduces field-measured traffic conditions -- to be considered statistically significant at a predefined confidence level for various measures of effectiveness (MOEs). FHWA's Corridor Simulation (CORSIM) software was used in a case study of computer model runs. The study used a calibrated model of six freeway links in the Minneapolis/St. Paul metropolitan area. Researchers analyzed the six links for the afternoon peak period. They calculated three link-level MOEs -- lane density, link discharge, and link speed -- for each link by analyzing the measured data collected during the CORSIM simulation runs. They also calculated the sampling error for the mean value at 95% confidence for each MOE. A sampling error under 10% represented stability in the mean value. For each of the six freeway links, the researchers carried out six independent run sets (5, 10, 15, 20, 25, and 30). Many of the numerical and graphical representations of the run sets revealed recognizable improvements to the results between 5 and 10 runs. Overall evidence indicated that at least 10 to 15 simulation runs were needed to stabilize the results. After the researchers achieved a stabilized run set, minimal benefit was obtained from additional runs. The sampling error peaked at over 70% for the 5-run set during the queue formation but was reduced to 20% for the 30-run set. The improvements to the mean values did vary between run sets. The researchers also documented the level of effort required to run the sets for the six scenarios, noting the time required for additional runs, the total time to complete all runs and analysis, the average time per run, and the range of times per run for each set. Results showed that the level of effort for additional runs was not significant.]]></description>
      <pubDate>Mon, 18 Apr 2011 12:25:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/1098936</guid>
    </item>
    <item>
      <title>Using a reliability process to reduce uncertainty in predicting crashes at unsignalized intersections</title>
      <link>https://trid.trb.org/View/914192</link>
      <description><![CDATA[The negative binomial (NB) model has been used extensively by traffic safety analysts as a crash prediction model, because it can accommodate the over-dispersion criterion usually exhibited in crash count data. However, the NB model is still a probabilistic model that may benefit from updating the parameters of the covariates to better predict crash frequencies at intersections. The objective of this paper is to examine the effect of updating the parameters of the covariates in the fitted NB model using a Bayesian updating reliability method to more accurately predict crash frequencies at 3-legged and 4-legged unsignalized intersections. For this purpose, data from 433 unsignalized intersections in Orange County, Florida were collected and used in the analysis. Four Bayesian-structure models were examined: (1) a non-informative prior with a log-gamma likelihood function, (2) a non-informative prior with an NB likelihood function, (3) an informative prior with an NB likelihood function, and (4) an informative prior with a log-gamma likelihood function. Standard measures of model effectiveness, such as the Akaike information criterion (AIC), mean absolute deviance (MAD), mean square prediction error (MSPE) and overall prediction accuracy, were used to compare the NB and Bayesian model predictions. Considering only the best estimates of the model parameters (ignoring uncertainty), both the NB and Bayesian models yielded favorable results. However, when considering the standard errors for the fitted parameters as a surrogate measure for measuring uncertainty, the Bayesian methods yielded more promising results. The full Bayesian updating framework using the log-gamma likelihood function for updating parameter estimates of the NB probabilistic models resulted in the least standard error values.]]></description>
      <pubDate>Mon, 22 Mar 2010 06:03:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/914192</guid>
    </item>
    <item>
      <title>Alert eyes and DWIs: An indirect evaluation of a DWI witness reward program in Stockton, CA</title>
      <link>https://trid.trb.org/View/889762</link>
      <description><![CDATA[We evaluate a "grassroots" anonymous reward program targeting drunken driving in Stockton, CA. The time-series cross-sectional data covers 19 years for Stockton and six other California cities. Exploiting interrupted time-series regression, Zellner's seemingly unrelated regression (SUR) framework, and bootstrapped standard errors, we test for an impact of this program on alcohol-related injury or fatality accidents, the proportion of all accidents involving alcohol, and the number of DWI arrests. In its first decade, the citizen reward program appears to have averted some 275 alcohol-related accidents for social cost savings of between $21,000 and $5.6 million. Further, possibly 4495 arrests were precluded, saving some $1-3 million in arrest-related costs. Incentivized public monitoring of driving-after-drinking may be an effective drunken driving abatement program though our exploratory findings need further confirmation.]]></description>
      <pubDate>Wed, 17 Jun 2009 12:08:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/889762</guid>
    </item>
    <item>
      <title>Calculating Errors for Measures Derived from Choice Modeling Estimates</title>
      <link>https://trid.trb.org/View/881298</link>
      <description><![CDATA[The calibration of choice models produces a set of parameter estimates and an associated covariance matrix, usually based on maximum likelihood estimation. However, in many cases, the values of interest to analysts are in fact functions of these parameters rather than the parameters themselves. It is thus also crucial to have a measure of variance for these derived quantities and it is preferable that these should also have the maximum likelihood properties of minimum variance et cetera. While the calculation of standard errors has been described for a number of such measures in the literature, including the ratio of two parameters, these results are often based on rather ad hoc calculations. In this paper, the authors present a general way of obtaining standard errors for such derived functions, describe the properties for these errors, give formulae for the standard errors for a number of commonly used quantities and develop a freeware software tool for the calculation of standard errors.]]></description>
      <pubDate>Tue, 19 May 2009 07:48:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/881298</guid>
    </item>
    <item>
      <title>Model Uncertainty in Normalized Shear Modulus and Damping Relationships</title>
      <link>https://trid.trb.org/View/849631</link>
      <description><![CDATA[Model uncertainty associated with normalized shear modulus and material damping ratio relationships developed in a previous study is discussed in terms of standard deviations in this paper. The relationships were originally developed by separating resonant column and torsional shear test results of 122 specimens into three general geologic groups (Quaternary-age soils, Tertiary-age and older soils, and residual/saprolite soils) and determining mean regressional model parameters (i.e., alpha, kappa, gamma  and subscript r1, and D and subscript min1) for each group. Statistical variability of the regressional parameters is quantified and expressed by standard errors of prediction. The determined uncertainty of the regressional parameters is then propagated through the recommended relationships using the point-estimate method to estimate the model uncertainty of the relationships. The point-estimate method is a simple and effective method of uncertainty propagation. For uncertainty estimation, the regressional parameters are assumed to be uncorrelated for the sake of simplicity. The influence of this assumption on the model uncertainty of normalized shear modulus and material damping ratio values, as well as the sensitivity of the model uncertainty to the uncertainty of various regressional parameters, are also discussed.]]></description>
      <pubDate>Wed, 27 Feb 2008 08:58:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/849631</guid>
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