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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Vehicle deformation depth based injury risk function for safety benefit evaluation of crash avoidance and mitigation systems</title>
      <link>https://trid.trb.org/View/1510377</link>
      <description><![CDATA[As the delta-v-based injury models used to evaluate intelligent driving systems are always fitted with European or American crash database, they cannot achieve wide application in those countries where limited in-depth analysed crash data are recorded. An injury model which is based on easily accessible information, is urgently needed. In this study, a deformation depth based injury risk model is proposed to overcome the limitation of delta-v. First, a correlation between the vehicle deformation depth and occupant injury risk is verified from the aspects of retrospective safety assessment and stiffness cluster analysis using German in-depth accident study and national automotive sampling system–crashworthiness data system. Furthermore, injury risk-deformation functions are regressed for different stiffness clusters using the crash data. The fitting accuracy reaches 97%, higher than the existing literature. A novel safety benefit assessment simulation platform is built with the regressed injury risk model. Based on this platform, an autonomous emergency braking system is evaluated. Only 1% error of the safety benefit exists between the proposed model and the delta-v based one.]]></description>
      <pubDate>Tue, 19 Jun 2018 09:34:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/1510377</guid>
    </item>
    <item>
      <title>Development of Accident Reconstruction Using In‐Depth Accident Investigation Data in India</title>
      <link>https://trid.trb.org/View/1370177</link>
      <description><![CDATA[This paper describes the development of delta‐V and pre‐crash speeds by crash type and vehicle type for crashes on Indian roads as part of Road Accident Sampling System – India (RASSI), an in‐depth database of India road accident data. In addition, it highlights some of the difficulties faced in reconstructing accidents in a developing country. The paper includes analyses of approximately 1,000 accident cases from 2011 to 2014 in RASSI. The RASSI project is modeled on well‐known crash data studies and databases from countries such as the United States of American (USA), United Kingdom (UK), and Germany, but uses India‐centric collection methodologies. Reconstruction was done for approximately 700 vehicles, and analysis was made of impact speeds of cars, trucks and motorized two‐wheelers, including comparison with available data for US counterparts. Results show that 19% of crash‐ involved vehicles in India have a delta‐V less than 5 mph (8 km/h), while in the US, 5% of crashes have a delta‐V less than 5 mph. Nearly 30% of vehicles involved in a crash in India have a delta‐V greater than 25 mph (40 km/h) whereas only 4% of the crashes in the US have a delta‐V greater than 25 mph (40 km/h). These differences could be due to factors including the size/weight incompatibility seen on Indian roads between colliding vehicles, travel speed and higher percentage of rear‐end crashes]]></description>
      <pubDate>Mon, 28 Sep 2015 09:11:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/1370177</guid>
    </item>
    <item>
      <title>An Overview of NHTSA’s Crash Reconstruction Software WinSMASH</title>
      <link>https://trid.trb.org/View/1367870</link>
      <description><![CDATA[The National Highway Traffic Safety Administration (NHTSA) uses WinSMASH computer software to estimate the change in velocity, delta-V, of the vehicles involved in crashes. The software uses detailed measurements from the crash scene, vehicle damage and vehicle stiffness characteristics to compute energy absorbed by the vehicle and estimate the delta-V and Barrier Equivalent Speed (BES). The WinSMASH is a Microsoft Windows based, enhanced and updated version of the accident reconstruction software CRASH3 previously used by NHTSA. The purpose of this paper is to describe the new enhancements in the program. The damage algorithm used in CRASH3 has been reformulated in WinSMASH. The new damage algorithm in WinSMASH is based on an assumed linear relationship between crash energy and crush and uses intercept d₀ and slope d₁ to describe vehicle stiffness. The software uses generic vehicle size and stiffness categories based on the vehicle’s wheelbase. However, the program also allows the users to enter the vehicle specific stiffness coefficients. The stiffness coefficients for a large number of vehicles have been calculated from crash test results and integrated into WinSMASH. An automated procedure to select the vehicle specific stiffness coefficients is currently under development. A statistical model is also being developed for estimating the stiffness coefficients of a vehicle that is not crash tested. The paper provides an overview of these procedures. The WinSMASH estimated delta-V of the vehicles is compared with the corresponding delta-V obtained from the Event Data Recorder (EDR) installed in the crashed vehicles to assess the accuracy of the software. The staged crash tests used to validate the software are also discussed in the paper.]]></description>
      <pubDate>Fri, 25 Sep 2015 16:19:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/1367870</guid>
    </item>
    <item>
      <title>Accuracy of Vehicle Frontal Stiffness Estimates for Crash Reconstruction</title>
      <link>https://trid.trb.org/View/1364732</link>
      <description><![CDATA[The National Highway Traffic Safety Administration (NHTSA) estimates delta-V from detailed measurements of vehicle deformation using the WinSMASH crash reconstruction code. Previous research has shown that WinSMASH delta-V estimates underpredict true delta-V by 25% on average. One possible explanation for this error is inaccuracies in the stiffness values used in the deltaV reconstruction calculation. The accuracy of codes, such as WinSMASH, is dependent upon vehicle stiffness values computed from post-impact crush measurements in crash tests. Any error in these crush measurements will be reflected as inaccuracies in the stiffness coefficients, and ultimately as errors in WinSMASH delta-V estimates. This paper investigates the accuracy of post-impact crush measurements in 93 frontal New Car Assessment Program (NCAP) tests of model year 2005-2007 vehicles.]]></description>
      <pubDate>Fri, 28 Aug 2015 13:56:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/1364732</guid>
    </item>
    <item>
      <title>Time Development Of Delta-V Recording And PDOF During A Collision</title>
      <link>https://trid.trb.org/View/1094173</link>
      <description><![CDATA[The relationships between principle direction of force (PDOF), change in velocity (∆V), and time developments of longitudinal and lateral acceleration/∆V values obtained from event data recorders are often misunderstood by accident reconstructionists.  This paper discusses these related, yet different, phenomena. The PDOF vector and ∆V vector of a vehicle must point in the same direction, according to Newton’s second law of motion.  However, the two vectors usually will be offset from each other since the PDOF passes through the approximate centroid of the damage and ∆V describes the motion of the vehicle center of gravity.  This offset affects the vehicle’s rotation due to the collision, but not ∆V.  Although these two vectors have the same direction, the numerical description of their directions can differ substantially. Examining the detailed time development of the acceleration values recorded by an EDR as well as the final values of the ∆V components can be useful.  Two examples using actual EDR data are provided to illustrate time development of these ∆V components and calculations of ∆V and PDOF.]]></description>
      <pubDate>Mon, 25 Apr 2011 07:03:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/1094173</guid>
    </item>
    <item>
      <title>A kinetic energy model of two-vehicle crash injury severity</title>
      <link>https://trid.trb.org/View/1097730</link>
      <description><![CDATA[An important part of any model of vehicle crashes is the development of a procedure to estimate crash injury severity. After reviewing existing models of crash severity, this paper outlines the development of a modelling approach aimed at measuring the injury severity of people in two-vehicle road crashes. This model can be incorporated into a discrete event traffic simulation model, using simulation model outputs as its input. The model can then serve as an integral part of a simulation model estimating the crash potential of components of the traffic system. The model is developed using Newtonian mechanics and generalised linear regression. The factors contributing to the speed change (∆Vs) of a subject vehicle are identified using the law of conservation of momentum. A log-gamma regression model is fitted to measure speed change (∆Vs) of the subject vehicle based on the identified crash characteristics. The kinetic energy applied to the subject vehicle is calculated by the model, which in turn uses a log-gamma regression model to estimate the injury severity score of the crash from the calculated kinetic energy, crash impact type, presence of airbag and/or seat belt and occupant age.]]></description>
      <pubDate>Wed, 20 Apr 2011 14:03:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/1097730</guid>
    </item>
    <item>
      <title>Evaluation of the Accuracy of NASS/CDS Delta-V Estimates from the Enhanced Winsmash Algorithm</title>
      <link>https://trid.trb.org/View/1084357</link>
      <description><![CDATA[This paper describes how the National Automotive Sampling System/Crashworthiness Data System (NASS/CDS) uses the WinSmash program to reconstruct changes in vehicle velocity for real world crashes. Vehicle change in velocity, or delta-V, is a measure of crash severity and a predictor of injury risk. Earlier studies have demonstrated that WinSmash 2.42 underestimated the delta-V by 23% on average with the use of categorical stiffness values for vehicles identified as a source of error. An enhanced version of WinSmash, WinSmash 2008, was developed to employ vehicle specific stiffness values whenever possible. A total of 478 General Motors vehicles equipped with event data recorders (EDRs) and involved in real-world crashes were collected from years 2000 – 2008 of the NASS/CDS database and the delta-V was computed using the enhanced WinSmash. All vehicles were involved in frontal impacts. The enhanced reconstruction algorithm reduced the underestimation of delta-V from 23% to 13% on average for all vehicles. Delta-V estimates for cars only were greatly improved but still understated by 16% on average. Less than 5% error in delta-V was observed for pickup trucks and utility vehicles. The amount of structural overlap for the vehicle and investigator confidence in the reconstruction continued to have an effect on accuracy. No difference in average delta-V was observed when using either updated categorical stiffness values or vehicle specific stiffness values. The changes in WinSmash delta-Vs have important policy implications for the National Highway Traffic Safety Administration (NHTSA) as the NASS/CDS delta-Vs are the basis for traffic and safety regulations as well as the speeds for vehicular crash testing and costs/benefits analyses.]]></description>
      <pubDate>Wed, 22 Dec 2010 08:31:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/1084357</guid>
    </item>
    <item>
      <title>SPECIAL TRANSPORTS (N.12)</title>
      <link>https://trid.trb.org/View/1057599</link>
      <description><![CDATA[CE DOSSIER "SPECIAL TRANSPORT" COMPREND TROIS FICHES TECHNIQUES CONSACREES SUCCESSIVEMENT : AU SYSTEME DE TRANSPORT SEMI-CONTINU; LE DELTA V; A L'USAGE DU DEUX ROUES LEGER DANS DES VILLES DES PAYS BAS, AVEC DES EXEMPLES DANS LA RANDSTADT HOLLAND; ENFIN, AU TRAMWAY DE SAINT-ETIENNE.]]></description>
      <pubDate>Sun, 21 Nov 2010 05:55:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/1057599</guid>
    </item>
    <item>
      <title>Effect of Delta-V Errors in NASS on Frontal Crash Risk Calculations</title>
      <link>https://trid.trb.org/View/914904</link>
      <description><![CDATA[This paper describes how the most important factor in predicting the risk of injury or death in a frontal crash is crash severity, which is expressed as the velocity change, or delta-V, experienced by the vehicle during the crash. The National Automotive Sampling System (NASS) is the largest database in the world linking injury outcomes with delta-Vs, which are obtained from field reconstructions. The accuracy of these reconstructions was assessed by analyzing 228 NASS cases involving single event frontal crashes in which the vehicle's frontal delta-V was also measured directly by an onboard event data recorder (EDR). Compared to the EDR measurements, the delta-V values in NASS averaged 19% lower with a standard deviation of 8.6 kph. The effect of this error on injury and fatality risk calculations was investigated using NASS data from 1997 - 2006 for frontal crashes with a known delta-V. Injury and fatality risk functions were calculated by curve fitting the distributions of the delta-V values associated with injury and fatality incidence normalized by the fitted crash exposure distribution. Individual delta-V values were linearly scaled to correct for the bias error, and the delta-V distributions were corrected for scatter error using a numerical deconvolution technique. Correcting for delta-V bias error shifted the calculated risk curves to the right and correcting for delta-V scatter error shifted the curves back to the left, but to a lesser extent. The effects of occupant age, gender, and belt use on injury and fatality risk were substantial.]]></description>
      <pubDate>Wed, 31 Mar 2010 07:46:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/914904</guid>
    </item>
    <item>
      <title>Can Delta-V Be Adjusted with Structural and Occupant Restraint Performance to Improve Prediction of Chest Acceleration?</title>
      <link>https://trid.trb.org/View/914906</link>
      <description><![CDATA[This paper investigated whether delta-V can be modified with a measure of vehicle structure performance and occupant restraint performance to better predict occupant peak chest acceleration during a frontal crash. A total of 619 full-scale frontal crash tests, with impact speeds ranging from 14 to 42 mph, were analyzed. Multiple linear regression was used to correlate combinations of crash severity, vehicle structure performance, and occupant restraint performance descriptors to the maximum measured crash test dummy chest acceleration. Using an adjusted R(2) selection method, the best combination of metrics were selected and then compared to a baseline model that used only delta-V to predict occupant chest kinematics. The combination of delta-V, ridedown efficiency, and the kinetic energy factor was found to provide the best prediction of the occupant chest acceleration. This combination accounted for approximately 4 times the variation in the maximum chest acceleration when compared to a model based solely on vehicle delta-V.]]></description>
      <pubDate>Wed, 31 Mar 2010 07:45:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/914906</guid>
    </item>
    <item>
      <title>NASS/CDS Delta-V Estimates: The Influence of Enhancements to the WinSmash Crash Reconstruction Code</title>
      <link>https://trid.trb.org/View/914944</link>
      <description><![CDATA[This paper will discuss the WINSmash crash reconstruction code. The change in velocity (delta-V) crash severity metric in the National Automotive Sampling System/Crashworthiness Data System (NASS/CDS) is computed using the WinSmash crash reconstruction code. Beginning in 2008, NASS/CDS investigators have started to use an enhanced version of WinSmash, WinSmash 2008, which features a comprehensive vehicle specific library for over 5000 vehicle make-model-year combinations and updated categorical stiffness values. The use of WinSmash 2008 is expected to greatly improve delta-V estimates. However, there is concern that this may result in a step change in the NASS/CDS delta-V estimates, making it difficult to compare NASS/CDS 2008 with earlier years. A total of 1,808 collisions were recomputed using data from NASS/CDS 2007. The new version of WinSmash shows improved accuracy, but still underpredicts delta-V. The use of WinSmash 2008 increased the delta-V by 7.9% or 1.9 kph on average. The changes in delta-V were not evenly distributed. Delta-V increases were larger for side impacts (8.3%) than for back impacts (5.3%). The calculation type had little effect on the delta-V changes. For vehicles, pickup trucks showed a small increase (3.3%) and utility vehicles increased the most (9.6%). This jump in delta-V may prevent the data from NASS/CDS 2008 and later from being readily aggregated with previous years.]]></description>
      <pubDate>Wed, 31 Mar 2010 07:45:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/914944</guid>
    </item>
    <item>
      <title>Evaluation of Advanced Air Bag Deployment Algorithm Performance Using Event Data Recorders</title>
      <link>https://trid.trb.org/View/914907</link>
      <description><![CDATA[The objective of this paper is to characterizes the field performance of occupant restraint systems that are designed with advanced air bag features including those specified in the U.S. Federal Motor Vehicle Safety Standard (FMVSS) No. 208 for advanced air bags, through the use of Event Data Recorders (EDRs). Although advanced restraint systems have been extensively tested in the laboratory, the authors are only beginning to understand the performance of these systems in the field. Because EDRs record many of the inputs to the advanced air bag control module, these devices can provide unique insights into the characteristics of field performance of air bags. The study was based on 164 advanced air bag cases extracted from NASS/CDS 2002-2006 with associated EDR data. In this dataset, advanced driver air bags were observed to deploy with a 50% probability at a longitudinal delta-V of 9 mph for the first stage, and at 26 mph for both inflator stages. In general, advanced air bag performance was as expected, however, the study identified cases of air bag deployments at delta-Vs as low as 3-4 mph, non-deployments at delta-Vs over 26 mph, and possible delayed air bag deployments.]]></description>
      <pubDate>Wed, 31 Mar 2010 07:45:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/914907</guid>
    </item>
    <item>
      <title>Basic Integral Calculus for Crash Reconstruction</title>
      <link>https://trid.trb.org/View/887905</link>
      <description><![CDATA[Fundamental calculus techniques can help define accident reconstruction variables, specifically velocity change (delta-V).  This article provides a review of calculus and the definite integral in the context of accident analysis.  By using a set of acceleration data from a crash test or crash pulse information from a vehicle data recorder, an investigator can calculate the area under the curve and find the delta-V.  The delta-V can be used with other calculations to find closing velocity and, in some cases, impact velocity.]]></description>
      <pubDate>Tue, 28 Apr 2009 08:10:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/887905</guid>
    </item>
    <item>
      <title>Comparing Different Sources of CDR Data in "Real World" Crashes</title>
      <link>https://trid.trb.org/View/872840</link>
      <description><![CDATA[Accident reconstructionists have the ability to look at the stored crash data and use what is defined as internal data to calculate and compare against stored crash data that is defined as external data.  Internal data is that which is sensed and recorded internally to the airbag control module (ACM) while external data is that which is recorded by the ACM but comes from an external source.  This paper compares different sources of crash data retrieval data from two real-world crashes.  In the first case, a stationary Pontiac Grand Am was struck from behind by a Chevrolet pickup truck on a highway with a posted speed limit of 60 mph.  In the second case, a Ford police vehicle was struck from behind by another Ford police vehicle in a parking lot.  When comparing the Ford powertrain control module (PCM) recorded speeds and apparent speed change, the sampling window must be considered when establishing an impact speed.  Given that there can be some delay between the PCM sending and the Ford restraint control module receiving the restraint deployment signal (RDS), it would appear that this collision occurred after the -.2 second sample was recorded but before the RDS was received.  There was very good agreement between the recorded pre-RDS speed and the closing speed analysis.  A clearer view of the events leading up to the collision and the speeds involved can be obtained using the higher resolution of the PCM pre-RDS data when analyzed with the delta-v.]]></description>
      <pubDate>Wed, 29 Oct 2008 07:21:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/872840</guid>
    </item>
    <item>
      <title>Comparison of roadside crash injury metrics using event data recorders</title>
      <link>https://trid.trb.org/View/851616</link>
      <description><![CDATA[The occupant impact velocity (OIV) and acceleration severity index (ASI) are competing measures of crash severity used to assess occupant injury risk in full-scale crash tests involving roadside safety hardware, e.g. guardrail. Delta-V, or the maximum change in vehicle velocity, is the traditional metric of crash severity for real world crashes. This study compares the ability of the OIV, ASI, and delta-V to discriminate between serious and non-serious occupant injury in real world frontal collisions. Vehicle kinematics data from event data recorders (EDRs) were matched with detailed occupant injury information for 180 real world crashes. Cumulative probability of injury risk curves were generated using binary logistic regression for belted and unbelted data subsets. By comparing the available fit statistics and performing a separate ROC curve analysis, the more computationally intensive OIV and ASI were found to offer no significant predictive advantage over the simpler delta-V.]]></description>
      <pubDate>Wed, 23 Apr 2008 09:22:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/851616</guid>
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