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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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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <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>Evaluating the effects of data transformation in computing road accident frequency from surrogate measures of safety using extreme value theory</title>
      <link>https://trid.trb.org/View/2647792</link>
      <description><![CDATA[Extreme Value Theory (EVT) is a state-of-the-art method for proactively evaluating traffic safety at a microscopic scale using interaction data. These interactions are typically quantified through Surrogate Measures of Safety (SMoS), also known as conflict indicators, which assess the severity of each interaction. Severe interactions are treated as extreme events and serve as input for EVT modelling. The key advantage of EVT lies in its ability to predict unobserved severe interactions, such as crashes, based on one or more SMoS indicators through mathematical extrapolation. However, the accuracy of these predictions depends heavily on the underlying statistical distribution of the chosen indicator. This is because EVT is an asymptotic theory, and its application requires the sample to meet a set of mathematical assumptions known as the extreme value conditions. In practice, these conditions are rarely, if ever, formally verified when EVT is applied to SMoS data. In this study, we implemented a formal procedure to test whether SMoS indicators satisfy the extreme value conditions. The approach was applied to a Swedish dataset. A key contribution of this work is the mathematical interpretation of one of the most fundamental concepts in traffic conflict theory: the relationship between conflicts and crashes. We provide an explanation for why certain indicators are more suitable than others for predicting unobserved severe events under certain scenarios. Additionally, we demonstrate that nonlinear transformations of SMoS indicators can enhance their suitability for EVT modelling. To conclude, the results of this study can be further extended to become a standard procedure in modelling traffic conflicts using EVT.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:25 GMT</pubDate>
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      <title>Laser Scanning Systems in Highway and Safety Assessment: Analysis of Highway Geometry and Safety Using LiDAR</title>
      <link>https://trid.trb.org/View/1974995</link>
      <description><![CDATA[This book aims to promote the core understanding of a proper modelling of road traffic accidents by deep learning methods using traffic information and road geometry delineated from laser scanning data. The first two chapters of the book introduce the reader to laser scanning technology with creative explanation and graphical illustrations, review and recent methods of extracting geometric road parameters. The next three chapters present different machine learning and statistical techniques applied to extract road geometry information from laser scanning data. Chapters 6 and 7 present methods for modelling roadside features and automatic road geometry identification in vector data. After that, this book goes on reviewing methods used for road traffic accident modelling including accident frequency and injury severity of the traffic accident (Chapter 8). Then, the next chapter explores the details of neural networks and their performance in predicting the traffic accidents along with a comparison with common data mining models. Chapter 10 presents a novel hybrid model combining extreme gradient boosting and deep neural networks for predicting injury severity of road traffic accidents. This chapter is followed by deep learning applications in modelling accident data using feed-forward, convolutional, recurrent neural network models (Chapter 11). The final chapter (Chapter 12) presents a procedure for modelling traffic accident with little data based on the concept of transfer learning. This book aims to help graduate students, professionals, decision makers, and road planners in developing better traffic accident prediction models using advanced neural networks.]]></description>
      <pubDate>Mon, 11 Jul 2022 09:21:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/1974995</guid>
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    <item>
      <title>Structural anatomy and temporal trends of road accident research: Full-scope analyses of the field</title>
      <link>https://trid.trb.org/View/1882005</link>
      <description><![CDATA[Scholarly research on road accidents over the past 50 years has generated substantial literature. The authors propose a robust search strategy to retrieve and analyze this literature.  Analyses was focused on estimating the size of this literature and examining its intellectual anatomy and temporal trends using bibliometric indicators of its articles.  The size of the literature is estimated to have exceeded N = 25,000 items as of 2020. At the highest level of aggregation, patterns of term co-occurrence in road accident articles point to the presence of six major divisions: (i) law, legislation & road trauma statistics; (ii) vehicular safety technology; (iii) statistical modelling; (iv) driving simulator experiments of driving behavior; (v) driver style and personality (social psychology); and (vi) vehicle crashworthiness and occupant protection division. Analyses identify the emergence of various research clusters and their progress over time along with their respective influential entities. For example, driver injury severity and crash frequency show distinct characteristics of trending topics, with research activities in those areas notably intensified since 2015 Also, two developing clusters labelled autonomous vehicle and automated vehicle show distinct signs of becoming emerging streams of road accident literature.  By objectively documenting temporal patterns in the development of the field, these analyses could offer new levels of insight into the intellectual composition of this field, its future directions, and knowledge gaps.  The proposed search strategy can be modified to generate specific subsets of this literature and assist future conventional reviews. The findings of temporal analyses could also be instrumental in informing and enriching literature review sections of original research articles. Analyses of authorships can facilitate collaborations, particularly across various divisions of accident research field.]]></description>
      <pubDate>Fri, 15 Oct 2021 09:22:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/1882005</guid>
    </item>
    <item>
      <title>Modeling heavy vehicle crash and injury severity</title>
      <link>https://trid.trb.org/View/1489326</link>
      <description><![CDATA[To reduce the trauma of heavy vehicle crashes, more research is needed to provide a better understanding of the factors influencing the frequency and severity of these crashes. The aim of this research is to identify the factors influencing heavy vehicle crashes and injury severity in Victoria, Australia. Therefore, in this research project, three studies were carried out to provide evidence-based recommendations to enhance the safety of heavy vehicles and save lives on Australian roads. In the first study, a crash severity model is developed to determine the variables influencing single-vehicle crashes involving heavy vehicles at intersections and mid-blocks. In the second study, a crash injury severity model is developed to determine the neighbourhood socioeconomic variables that influence injury severity in heavy vehicle collisions. Finally, in the third study, a crash injury severity model is developed to determine the causes contributing to injury severity in heavy vehicle angle collisions. In the first study, the objective was to identify the factors differentiating between single heavy vehicle collisions at intersections and mid-blocks using a binary logit model. The primary objective of the second study was to identify the neighbourhood socioeconomic characteristics affecting injury severity in heavy vehicle collisions. Finally, the main objective of the third study was to identify the factors contributing to injury severity in angle crashes involving heavy vehicles, in order to provide insights into improving traffic safety. The output of this research will provide evidence-based recommendations to improve the safety of all road users, including heavy vehicle drivers on Australian roads. This study will also contribute to advancing knowledge in the field and will provide road safety professionals with more information and knowledge on the advantages of using statistical models, especially the Scobit model, in traffic safety studies.]]></description>
      <pubDate>Mon, 20 Nov 2017 10:45:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/1489326</guid>
    </item>
    <item>
      <title>Re-visiting crash–speed relationships: A new perspective in crash modelling</title>
      <link>https://trid.trb.org/View/1376907</link>
      <description><![CDATA[Although speed is considered to be one of the main crash contributory factors, research findings are inconsistent. Independent of the robustness of their statistical approaches, crash frequency models typically employ crash data that are aggregated using spatial criteria (e.g., crash counts by link termed as a link-based approach). In this approach, the variability in crashes between links is explained by highly aggregated average measures that may be inappropriate, especially for time-varying variables such as speed and volume. This paper re-examines crash–speed relationships by creating a new crash data aggregation approach that enables improved representation of the road conditions just before crash occurrences. Crashes are aggregated according to the similarity of their pre-crash traffic and geometric conditions, forming an alternative crash count dataset termed as a condition-based approach. Crash–speed relationships are separately developed and compared for both approaches by employing the annual crashes that occurred on the Strategic Road Network of England in 2012. The datasets are modelled by injury severity using multivariate Poisson lognormal regression, with multivariate spatial effects for the link-based model, using a full Bayesian inference approach. The results of the condition-based approach show that high speeds trigger crash frequency. The outcome of the link-based model is the opposite; suggesting that the speed–crash relationship is negative regardless of crash severity. The differences between the results imply that data aggregation is a crucial, yet so far overlooked, methodological element of crash data analyses that may have direct impact on the modelling outcomes.]]></description>
      <pubDate>Wed, 23 Dec 2015 08:09:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/1376907</guid>
    </item>
    <item>
      <title>Using statistical modelling to predict crash risks, injury outcomes and compensation costs in Victoria</title>
      <link>https://trid.trb.org/View/1357951</link>
      <description><![CDATA[In 2011, Victoria&rsquo;s Transport Accident Commission (TAC) built a rich linked crash database to explore the research question: &ldquo;What are the significant variables in predicting crash risk, injury outcomes and compensation costs when controlling for all other variables&rdquo;? The core aims of the TAC Road Safety Risk Models project were to conduct sophisticated analyses of available data to identify key drivers of road trauma, injury severity and compensation costs, as well as to identify key target markets. The project began with an intense data build involving the sourcing, linking and cleansing of road safety and related data. This included crash and compensation data, as well as exposure data on Victorian licence holders and registered vehicles. Detailed injury data was also obtained. A series of statistical models were then developed to examine the relationship between person, vehicle and crash variables, along with injury severity and compensation costs. A number of pre-crash variables were found to be significant predictors of crash risk and severity including vehicle, person and geo-demographic variables. Injury severity was found to be the most significant variable atpredicting compensation costs. The established database provides a benchmark for future Road Safety policy analysis, particularly with consideration given to the cost of injury to society. With the prospect of new and improved data availability for key input datasets, the TAC has begun to update the linked dataset and refresh the models to identify new relationships.]]></description>
      <pubDate>Thu, 18 Jun 2015 11:04:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/1357951</guid>
    </item>
    <item>
      <title>Using statistical modelling to predict crash risks, injury outcomes and compensation costs in Victoria</title>
      <link>https://trid.trb.org/View/1354145</link>
      <description><![CDATA[In 2011, Victoria&rsquo;s Transport Accident Commission (TAC) built a rich linked crash database to explore the research question: &ldquo;What are the significant variables in predicting crash risk, injury outcomes and compensation costs when controlling for all other variables&rdquo;? The core aims of the TAC Road Safety Risk Models project were to conduct sophisticated analyses of available data to identify key drivers of road trauma, injury severity and compensation costs, as well as identify key target markets. The project began with an intense data build involving the sourcing, linking and cleansing of road safety and related data. This included crash and compensation data, as well as exposure data on Victorian licence holders and registered vehicles. Detailed injury data was also obtained. A series of statistical models were then developed to examine the relationship between person, vehicle and crash variables, along with injury severity and compensation costs. A number of pre-crash variables were found to be significant predictors of crash risk and severity including vehicle, person and geo-demographic variables. Injury severity was found to be the most significant variable at predicting compensation costs. The established database provides a benchmark for future Road Safety policy analysis, particularly with consideration given to the cost of injury to society. With the prospect of new and improved data availability for key input datasets, the TAC has begun to update the linked dataset and refresh the models to identify new relationships.]]></description>
      <pubDate>Fri, 15 May 2015 12:20:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/1354145</guid>
    </item>
    <item>
      <title>Modelling trends in road accident frequency: Bayesian inference for rates with uncertain exposure</title>
      <link>https://trid.trb.org/View/1302365</link>
      <description><![CDATA[Several thousand people die as a result of a road accident each year in Great Britain and the trend in the number of fatal accidents is monitored closely to understand increases and reductions in the number of deaths. Results from analysis of these data directly influence Government road safety policy and ensure the introduction of effective safety interventions across the country. Overall accident numbers are important, but when disaggregating into various characteristics, accident risk (defined as the number of accidents relative to an exposure measure) is a better comparator. The exposure measure used most commonly for accident rate analysis is traffic flow which can be disaggregated into vehicle types, road type, and year. Here we want to assess the accident risk across different car types and car ages, and therefore alternative exposure sources are required. We disaggregate exposure to a further extent than possible with currently available data in order to take the increased variability within these new factors into account. Exposure data sources are mainly based on sample surveys and therefore have some associated uncertainty, however previous accident risk analysis has not, in general, taken this into account. For an explicit way to include this uncertainty we use a Bayesian analysis to combine three sources of exposure using a log-Normal model with model priors representing our uncertainty in each data source. Using further Bayesian models, we propagate this uncertainty through to accident rates and accident severity, determining important factors and inter-relationships between factors to identify key features affecting accident trends, and we make the first exploration of the effect of the recent recession on road accidents.]]></description>
      <pubDate>Tue, 18 Mar 2014 11:17:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/1302365</guid>
    </item>
    <item>
      <title>Development of Hierarchical Safety Performance Functions for Urban Mid-blocks</title>
      <link>https://trid.trb.org/View/1280069</link>
      <description><![CDATA[Crash frequency and severity are influenced by a variety of variables that represent regional, site, crash, and driver-vehicle unit characteristics. In the traditional methods of crash prediction, all the variables are considered at a single level and the multilevel structure inherent in the crash data is ignored. Hierarchical modelling is a statistical technique that allows multilevel data structure to be properly specified and estimated. In the present study, a hierarchical modelling approach was used to estimate the crash frequency and severity of single and dual carriageway roads. Since the crash patterns of single and dual carriageways were found to be different, separate models were developed for these facilities. A two-level design was adopted for crash frequency prediction and four level design for crash severity prediction. The two levels in the crash frequency prediction are geographic region level and traffic site level. The additional levels in severity prediction are crash level and driver-vehicle unit level. The study indicates that hierarchical models performed better for crash frequency and severity prediction. Hierarchical models are strongly advocated for crash data that has correlated observations within groups.]]></description>
      <pubDate>Mon, 27 Jan 2014 09:40:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/1280069</guid>
    </item>
    <item>
      <title>Highway design standards and operational characteristics in relation to levels of safety on interurban roads in Israel</title>
      <link>https://trid.trb.org/View/1168720</link>
      <description><![CDATA[This study deals primarily with the levels of safety of the main interurban road network in Israel and covers 70% of the total interurban roads, with a total of 8910 police-reported accidents in the years 1990 to 1992.  It presents a macro approach to the relation between road safety and various road categories as reflected in the overall geometric and traffic operational features and explores the effect of other individual geometric features on safety.  The safety performance of the various road categories are investigated on an comparative basis.  After an extensive literature review, advanced statistical tools are applied to handle the randomness in the occurrences of accidents and to explore the relations between safety and other explanatory variables.  The Classification and Regression Tree (CART) analysis, which generates a binary tree structure from the data showing the criteria (variables) for each split and giving pictorial representation of the data, is used to illuminate the relation between the variables and road accidents as a measure of safety.  The CART results portray the importance of ADT and the need for stratification of ADT in accident modelling.  It also indicates probable power function models and highlights the effects of shoulder type among single carriageways.  The occurrence and number of road accidents are discrete random events which are probabilistic in nature and have non-negative integer values; thus after an extensive literature review multi-variate multiplicative Poisson regression models with log links are fitted to the data set using the Generalized Linear Interactive Modeling (GLIM) package. The results emphasize the relative safer nature of freeways as compared to the conventional single and dual carriageway roads and show the relation between   between average daily traffic (ADT) and safety is curvilinear meaning that as ADT increases and congestion sets in, the incidence of accidents decreases.  For the fitted models the effects of ADT were found to be most important.  In the case of models for single carriage roads, other explanatory variables, namely free flow speed, type of shoulders and junction frequency showed some significance.  The Poisson models developed can be used as a basis for the identification of accident blackspots and in the evaluation of safety treatments.]]></description>
      <pubDate>Thu, 23 Aug 2012 19:53:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/1168720</guid>
    </item>
    <item>
      <title>Highway safety: modeling, analysis, management, statistical methods, and crash location</title>
      <link>https://trid.trb.org/View/1160541</link>
      <description><![CDATA[]]></description>
      <pubDate>Thu, 23 Aug 2012 07:26:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/1160541</guid>
    </item>
    <item>
      <title>Modelling crash risk on the New Zealand state highway network</title>
      <link>https://trid.trb.org/View/1141769</link>
      <description><![CDATA[This report presents an updated statistical analysis of data relating to crash rates on New Zealand roads.  The research was carried out during 2007-2009 and it precedes the changes in 2010 to the New Zealand T10 specification.  The refinements presented are associated with accounting for differences between the local and the general (ie design) speed environment, crash severity and interactions between curvature and roughness.  The addition of these refinements will extend the present model's usefulness for guiding safety initiatives and providing economic justifications.  The regression model used in the analysis assumes that crashes are statistically independent and the number of crashes in each 10m segment of road follows a Poisson distribution.  Inputs to the model include the average daily traffic (per side) and is a linear combination of the road characteristics, being transformations of terms that include factors such as gradient, curvature, out-of-context-curve effect, skid site classification, skid resistance, region and an urban/rural classification.  There is still more variability in the data than the Poisson model would predict.  However, the results indicate the availability of a robust crash prediction model that takes into account both road condition and road geometry, allowing proactive identification of existing engineering-related road safety deficiencies and more importantly, the ability to quantify the potential for improvement.]]></description>
      <pubDate>Wed, 27 Jun 2012 09:56:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/1141769</guid>
    </item>
    <item>
      <title>Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model</title>
      <link>https://trid.trb.org/View/1116558</link>
      <description><![CDATA[Accident prediction models have been extensively used in site ranking with the objective of identifying accident hotspots. Previously this has been achieved by using a univariate count data or a multivariate count data model (e.g. multivariate Poisson-lognormal) for modelling the number of accidents at different severity levels simultaneously. This paper proposes an alternative method to estimate accident frequency at different severity levels, namely the two-stage mixed multivariate model which combines both accident frequency and severity models. The accident, traffic and road characteristics data from the M25 motorway and surrounding major roads in England have been collected to demonstrate the use of the two-stage model. A Bayesian spatial model and a mixed logit model have been employed at each stage for accident frequency and severity analysis respectively, and the results combined to produce estimation of the number of accidents at different severity levels. Based on the results from the two-stage model, the accident hotspots on the M25 and surround have been identified. The ranking result using the two-stage model has also been compared with other ranking methods, such as the naive ranking method, multivariate Poisson-lognormal and fixed proportion method. Compared to the traditional frequency based analysis, the two-stage model has the advantage in that it utilises more detailed individual accident level data and is able to predict low frequency accidents (such as fatal accidents). Therefore, the two-stage mixed multivariate model is a promising tool in predicting accident frequency according to their severity levels and site ranking.]]></description>
      <pubDate>Wed, 21 Sep 2011 07:13:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/1116558</guid>
    </item>
    <item>
      <title>Benchmarking road safety in Northern Ireland</title>
      <link>https://trid.trb.org/View/1084502</link>
      <description><![CDATA[The latest figures in Northern Ireland show that the current road safety target reductions, for the ten years up to 2012, had already been achieved by 2008 and so are likely to be maintained or exceeded in 2012. Despite this success, the proportion of Northern Ireland’s population killed or seriously injured as a result of road traffic collisions remains higher than for Great Britain and other high-performing European countries. The project described in this report benchmarked Northern Ireland’s road safety performance against appropriate comparator data. An examination of the similarities and differences in road safety exposure factors in Northern Ireland and Great Britain concluded that no single country or region of Great Britain was appropriate as a comparator for Northern Ireland. Therefore, it was necessary to use statistical modelling to ‘build’ a hypothetical comparator. The comparator was created using data from Great Britain, adjusting for variables such as traffic flow and road type. It showed that safety on motorways and urban roads is substantially better in Northern Ireland than on the same road types in Great Britain once traffic flow and road length have been taken into account. However, even allowing for the exposure factors, safety on rural roads is worse in Northern Ireland. Nonetheless, the net effect across all road types is that there were fewer road casualties in Northern Ireland in 2008 than there would have been had road safety performance been comparable with that in Great Britain. (A)]]></description>
      <pubDate>Wed, 22 Dec 2010 08:37:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/1084502</guid>
    </item>
    <item>
      <title>Effect of side raised entry treatments on road safety in London</title>
      <link>https://trid.trb.org/View/871464</link>
      <description><![CDATA[One of the measures intended to improve conditions for pedestrians in London has been the installation of Side Raised Entry Treatments (SRET) across side roads at their junctions with major roads. This report concerns a large scale statistical analysis of the collision record of SRETs which was undertaken together with an in-depth study of users' behaviour at a small selection of junctions with and without SRETs. The statistical model estimated that there was no overall change in the total number of collions due to SRETs on the TLRN. However, on Borough roads an overall reduction in the number of collisions after installation of a SRET was modelled. It is not clear from the study of users' behaviour whether pedestrians expect drivers to give way at SRETs. At two sites, one control and on with a SRET, a significant minority of pedestrians appeared to assert priority and force drivers to give way to them, but overall there was no clear difference in pedestrians' expectation of priority between SRET and control sites. Pedestrians appeared to like the convenience of crossing the road at a SRET, where the SRET provided a continous level place to cross between footways either side of the road. Significantly fewer people diverted from the natural crossing line to walk behind stationary vehicle, and avoid delay, at sites with a SRET than at the controls. The benefit of the convenient informal crossing appeared to exceed the disbenefit of the extra delay of waiting for the vehicle to clear. Neither the observational study nor the collision modelling raised particular issues for children or older pedestrians. (A)]]></description>
      <pubDate>Tue, 30 Sep 2008 10:15:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/871464</guid>
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