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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>When locations (almost) tell the story: Reconstructing road accidents from iPhone location-related traces</title>
      <link>https://trid.trb.org/View/2744950</link>
      <description><![CDATA[This study originates from a road traffic accident in which two vehicles collided in a curve. Alongside the physical evidence, location-related traces recovered from the Cache.sqlite database of an iPhone belonging to one of the drivers constituted the principal source of digital evidence. Beyond this specific case, analysis of these traces raised key forensic questions regarding their contribution to accident reconstruction. Based on these considerations, four investigative questions were defined, concerning the timing of the crash, phone manipulation before impact, speed estimation, and crash location. These questions were examined through a controlled experimental design involving 16 iPhone devices and high-precision Global Navigation Satellite System (GNSS) reference data, complemented by real casework observations. The results indicate that the moment of the crash can be approximated through speed analysis or spatial mapping of the recorded traces. Course values reflect the direction of motion rather than device orientation, thereby precluding their use for detecting phone handling. Speed values are reliable under steady-state conditions, but degrade substantially during braking. Regarding location, the analysis reveals three potential post-processing phenomena referred to as projection, correction, and anticipation. They can introduce significant positional errors, particularly around the moment of impact. These findings underline both the value and the limitations of iPhone location-related traces for forensic purposes. They further emphasize the need for a deeper understanding of their underlying creation mechanisms to ensure reliable interpretation in accident reconstruction.]]></description>
      <pubDate>Thu, 20 Aug 2026 09:54:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2744950</guid>
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    <item>
      <title>Modeling the Point of Derailment and Derailment Severity of Freight Trains Using Markovian Dependence</title>
      <link>https://trid.trb.org/View/2673475</link>
      <description><![CDATA[Train derailments are a major safety concern in rail transportation, often leading to substantial economic losses and risks to human life. Traditional derailment analyses typically treat derailments as independent events with uniform probabilities along a train, which fails to capture the sequential dependencies seen in real derailment cascades. This paper introduces a car-level Markov modeling framework that explicitly represents dependencies between adjacent cars to predict both the point of derailment (POD) and the subsequent propagation along the train consist. Using U.S. Federal Railroad Administration freight-train derailment data from 2011 to 2023, model parameters are estimated through a likelihood-based approach with LASSO regularization to identify the most influential operational and positional factors while preventing overfitting. The proposed model outperforms independence-based benchmarks and reveals distinct mechanisms for derailment initiation and propagation; in particular, the derailment state of the preceding car is the dominant determinant of conditional derailment risk. The framework yields interpretable, car-specific risk estimates that can support risk-based inspection scheduling, consist-design optimization and preventive maintenance planning.]]></description>
      <pubDate>Mon, 01 Jun 2026 09:13:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673475</guid>
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    <item>
      <title>Prediction of expressway accident locations using contribution information from Grad-CAM</title>
      <link>https://trid.trb.org/View/2688682</link>
      <description><![CDATA[This study proposes a method for predicting accident locations on expressways using convolutional neural networks (CNNs) and Grad-CAM-based interpretability. A CNN model is trained using vehicle trajectory images generated from ETC 2.0 probe data. Grad-CAM is applied to extract spatiotemporal contribution information associated with accident risk, enabling the identification of expressway sections where risk-prone traffic patterns are spatially concentrated for the subsequent 30-min period. Based on the spatial aggregation of these contribution scores, a framework is developed to estimate the most likely accident section within a target route. The model outputs are intended to support short-term, location-specific prioritization of safety interventions by traffic operators, rather than forecasting individual crash events. Validation on a 15.34 km section of the Tomei Expressway in Japan demonstrates that the proposed method reduces location prediction error by approximately 19% compared to a naïve prediction, and further outperforms representative tree-based ensemble methods, including Random Forest, XGBoost, and LightGBM. The method shows higher accuracy for multi-vehicle accidents and for high-traffic conditions. Furthermore, the model exhibits superior performance over a statistical baseline across various risk thresholds, indicating its robustness and practical applicability in real-world implementation scenarios where resource allocation must be prioritized. These results highlight the utility of interpretable deep learning in supporting targeted and effective traffic safety interventions.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688682</guid>
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    <item>
      <title>Safety Analysis of Highway Curves Where Crashes Occur in South Carolina</title>
      <link>https://trid.trb.org/View/2670678</link>
      <description><![CDATA[The design of highway curves has significant impact on traffic safety. More than 25% of fatal crashes in the United States occur on horizontal curves, and the crash rate for curves is approximately three times higher than other highway sections. Furthermore, crashes are over 4 times more likely to be fatal on horizontal curve sections compared to similar highway tangent sections. These statistics underscore the critical need to identify and mitigate the specific risks associated with driving on curved sections, particularly in the context of Vision Zero goals for zero roadway fatalities. This research focuses on a detailed analysis of curves where vehicle crashes occur in South Carolina. Eight years of crash data and an inventory of circular curves including Rieker CARS data are analyzed to identify trends and the effectiveness of countermeasures. The research achieved several objectives including identifying potential locations of compliance issues related to curve signage and also identifying curve locations of highest crash frequency that can benefit from potential countermeasures. The research required innovative data development methods vital to achieve the objectives.]]></description>
      <pubDate>Wed, 25 Feb 2026 14:28:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2670678</guid>
    </item>
    <item>
      <title>Investigating the Influencing Factors of Crash Frequency Considering Spatial Heterogeneity</title>
      <link>https://trid.trb.org/View/2613269</link>
      <description><![CDATA[To clarify the influencing factors of crash frequency, this paper collects the traffic crashes and some relevant spatial data in a certain area and constructs a Geographically Weighted Regression (GWR) model and a Multiscale Geographically and Temporally Weighted Regression (MGWR) model based on that. Through the models, the degree of impact of various spatial influencing factors and the spatial heterogeneity are examined. The results show that the MGWR model is superior to the GWR model in terms of fitting effect and spatial scale; road density and the number of hospitals have positive promoting effects, meaning that the areas with higher road density or more hospitals tend to possess more traffic crashes, while grid GDP exhibits an inverse inhibitory effect, meaning that the economically prosperous areas possess fewer traffic crashes; besides that, the proportion of crashes during the morning rush hours and the proportion of crashes with motorcycle involvements have significant impacts and spatial heterogeneity on traffic crashes.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613269</guid>
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    <item>
      <title>Study of Techno-Legal Aspects of Accident Site Investigation—A Case Study from Bengaluru</title>
      <link>https://trid.trb.org/View/2113508</link>
      <description><![CDATA[After a major road crash, conducting crash investigation is generally the next step. But the road accident spot analysis is not carried out as per the procedure in most of the road crashes in India. Although crash spot studies are conducted by associated officers, the procedure, implementation, and post-investigation studies are charge framed, nontechnical, and not available in the public domain. The study aims to investigate the techno-legal aspect of the crash site investigation of a typical crash from Bengaluru which occurred between the motorbike and the car. Crash spot was studied, analysis of accident spot parameters, and vehicles inspection observations are correlated and used to infer the reason for the crash. This article aims to highlight the importance of scientific investigation of the road accident site and the sequence of activities happening after the occurrence of the road accident with a case study.]]></description>
      <pubDate>Wed, 18 Feb 2026 08:24:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2113508</guid>
    </item>
    <item>
      <title>Assessing Commercial Driveways Safety in the Vicinity of Interchanges: A Study in Florida</title>
      <link>https://trid.trb.org/View/2562020</link>
      <description><![CDATA[This research investigated how the proximity of commercial driveways to interchanges affects traffic safety by applying crash frequency and severity analysis models to examine 69 interchanges, 832 commercial driveways, and 853 related crashes in Florida. Key factors influencing crash occurrences included the number of lanes, types of bike lanes and right-turn lanes, driveway designs, and traffic control measures. The exclusive right-turn lanes at driveways reduced crashes by 49.7% compared to shared lanes and 64.0% compared to driveways not connected to right-turn lanes. Additionally, crash severity was linked to the proximity of ramp tapers, instances of alcohol or drug use, lighting conditions, speed limits, and the types of bike lanes present. Specifically, driveways within 500 ft of ramp tapers increased the risk of severe crashes by 261%. Conventional bike lanes raised the risks of minor injuries. These findings provide valuable insights for enhancing safety and access management guidelines around interchanges.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562020</guid>
    </item>
    <item>
      <title>Fleeing the Scene: Hit and Run Collisions in Los Angeles</title>
      <link>https://trid.trb.org/View/2613667</link>
      <description><![CDATA[To investigate the phenomenon of hit and run collisions in Los Angeles, this study aims to understand: 1) collision factors which make it more likely that a driver flees the scene, 2) where hit and run collisions are occurring at a high rate, 3) challenges to addressing this issue, and 4) strategies which could help reduce the rate of hit and run collisions. Like Vision Zero, this investigation focuses on collisions that result in serious injury or death. This analysis revealed that hit and run collisions were more likely to happen after dark. Drivers were much more likely to flee if they hit a pedestrian or bicyclist than another vehicle. Race also plays a significant role; Black and Hispanic individuals were much more likely to be the victims of hit and run collisions than their White counterparts. Spatial analysis of collision data revealed pockets of high hit and run activity in the Pacoima, Sun Valley, and Sunland-Tujunga neighborhoods in the San Fernando Valley; on Sunset Boulevard in Pacific Palisades; in Highland Park, northeast of Downtown, and a large portion of Southeast Los Angeles. Hit and run activity has been persistently high in the area surrounding Downtown, and has been increasing in some areas of the San Fernando Valley. Interviews with expert parties highlighted the challenges of addressing hit and run collisions. There is very little information regarding drivers who flee the scene and the factors that motivate their behavior. The difficulty identifying drivers and successfully prosecuting them, combined with the tendency for leniency in the judicial system, may contribute to an atmosphere of impunity for drivers. One strategy for gaining insight on why drivers flee is to increase the linkages between collision reports and any information gathered after the fact. This would allow researchers to explore whether certain characteristics of drivers make them more likely to flee. Efforts should be made to interview drivers charged with leaving the scene to better understand the context and motivation of hit and run behavior. Increasing awareness of the penalties for leaving the scene, particularly in groups at high risk of fleeing, may help with deterrence. Overall reduction of severe traffic collisions will also help reduce the rate of hit and runs.]]></description>
      <pubDate>Tue, 16 Dec 2025 11:19:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613667</guid>
    </item>
    <item>
      <title>Identifying disorder and abnormal incidents in traffic flow: A thermodynamics approach based on differential velocity entropy</title>
      <link>https://trid.trb.org/View/2604804</link>
      <description><![CDATA[Macroscopic traffic safety situation estimation is an important prerequisite for identifying abnormal incidents and ensuring the safe operation of road network. Existing traffic flow metrics fail to comprehensively quantify disorder associated with crash risks and abnormal events. Inspired by thermodynamics, this research proposes a differential velocity entropy based on the distribution of velocity vectors and the maximum entropy (ME) approach. This entropy models the disorder in traffic flow, capturing additionally information typically overlooked in traditional fundamental diagrams. As a non-parametric measure of disorder, it eliminates heterogeneity arising from bin selection and sample size effects in quantitative analysis. Numerical simulations demonstrate its sensitivity to traffic phase transitions, revealing the spatial and temporal locations of bottlenecks. Subsequently, the authors conducted an empirical study with the I-24 MOTION dataset to assess the metric’s ability to identify crash impacts. The results show that entropy peaks correlate spatially and temporally with accident locations, and its evolution captures the propagation range of accident’s impact. Additionally, differential velocity entropy shows a strong association with conflict frequency and can serve as a key traffic-flow indicator for identifying the macroscopic traffic safety situation.]]></description>
      <pubDate>Mon, 27 Oct 2025 09:34:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604804</guid>
    </item>
    <item>
      <title>Joint Analysis of E-Bike Crash Types and Injury Severity at Different Locations Using a Copula-Based Approach</title>
      <link>https://trid.trb.org/View/2611123</link>
      <description><![CDATA[E-bikes stand out as one of the most energy-efficient forms of motorized transport. However, a significant increase in e-bike traffic crashes has raised concerns about the factors influencing crash severity. Moreover, the specific impacts of different locations on e-bike crash injury severity remain elusive. This study investigated the factors influencing e-bike crash severity with a focus on the diverse effects of crash location and crash type. A copula-based joint framework was applied to capture the dependency structure between crash type and severity. The exogenous variables with location effects were captured by the interaction variables. This structure allowed exploration of how these variables and their location influenced crash severity in the pooled dataset for each crash type. We utilized data from the Meituan Shared E-bike Platform, comprising 1,150 crash records. An exhaustive set of independent variables, including environment conditions, locations, e-bike cyclist characteristics, and actions was considered. These copula parameters suggested the completely distinct nature of the relationship between crash types and their corresponding severities. Overall, the determinants of injury severities were found to vary between different crash types. Key common indicators such as heavy rain affected the severity of the rollover and motor vehicle crash in opposite ways owing to different stopping distances, mass, and kinetic energy. Younger riders tended to experience less severe outcomes in rollover crashes but were more likely to be involved in motor vehicle crashes. This study contributes to guiding transportation agencies in developing effective countermeasures to promote the safety of e-bikes.]]></description>
      <pubDate>Mon, 20 Oct 2025 08:42:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611123</guid>
    </item>
    <item>
      <title>Using GIS for Crash Location and Analysis at State DOTs: Case Studies of Select Transportation Agencies</title>
      <link>https://trid.trb.org/View/2592195</link>
      <description><![CDATA[This report highlighted State DOTs that have been able to use GIS and geospatial tools to allow safety analysts to produce better crash analyses. The DOTs of Alabama, Oregon, New York, North Carolina, and Connecticut have taken advantage of better crash location data and the improved processes involved, but this work is an ongoing process. Crash location and crash analysis are essential elements toward creating solutions that improve roadway safety. While the technology exists to produce more effective crash analyses, strategies for implementing new policies that make it easier to find or share crash location data and updating the way DOTs perform crash analyses are still needed. Effective examples of these strategies or efforts highlighted through webinars, peer exchanges, or case study reports can help more State DOTs continue to reduce fatal crashes.]]></description>
      <pubDate>Mon, 25 Aug 2025 09:04:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2592195</guid>
    </item>
    <item>
      <title>Local Infrastructure and Economy Predicts Traffic Related Fatalities in Children</title>
      <link>https://trid.trb.org/View/2563822</link>
      <description><![CDATA[Previous research on pediatric motor vehicle collisions (MVC) and fatalities has primarily focused on patient demographics and crash specific information. This study evaluates whether various measures of local infrastructure, including the National Walk Index (NWI), population density, and public school density, or macroeconomic forces, encapsulated in Social Vulnerability Index (SVI) and food area deprivation (PFA) can predict which counties are most at risk for pediatric traffic fatalities. Counties with more than 100,000 children in the most recent US census and >=1 pediatric traffic fatality as identified in the Fatality Analysis Reporting System (FARS) between 2017 and 2021 were included in the study. Poisson regression modeling was used to identify county level infrastructure and macroeconomic forces that predicted increasing MVC related average annual mortality rate per 100,000 children.  There were 158 counties that met inclusion criteria. Univariate Poisson regression demonstrated that NWI, SVI, PFA, population density, and school density each individually correlated with MVC related mortality rate (p < 0.001 for all predictors). When controlling for SVI and population density, multivariable Poisson regression demonstrated that each decile increase in walkability was associated with a 7 % decrease in MVC related mortality rate (IRR 0.93, 95 % CI 0.91-0.96). Areas with poor walkability predict the likelihood of pediatric traffic fatality. These findings highlight tangible local and state policy changes that could be implemented to decrease the likelihood of traffic-related child fatality rates in specific counties.]]></description>
      <pubDate>Fri, 11 Jul 2025 10:00:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2563822</guid>
    </item>
    <item>
      <title>GIS-based analysis of spatio-temporal clustering of road traffic accidents in Bangkok Metropolitan Region (BMR), Thailand from 2012 to 2021</title>
      <link>https://trid.trb.org/View/2564749</link>
      <description><![CDATA[Most fatalities and injuries resulting from Road Traffic Accidents (RTAs) exhibit distinct spatial and temporal patterns which have adverse implications for both regional economies and demographic structures. This study aims to investigate the spatio-temporal distribution of RTA hotspots along the highway network. Using Global Moran’s I spatial autocorrelation, the authors examined the temporal dynamics of 31,687 RTA records from 2012 to 2021 across four temporal scales: time of day, day of the week, month, and year. The analysis of RTAs by severity levels (fatalities, serious injuries, and minor injuries) revealed clear spatial clustering of hotspot locations. The results demonstrated statistically significant clustering at the 95 % confidence level, indicating that RTAs are not randomly distributed but are concentrated in specific high-risk areas, particularly at highway junctions and in zones of high activity density. In contrast, other areas exhibited more dispersed or varied cluster patterns. Temporal analysis further revealed that accident patterns differ markedly by time period, with notable variations between daytime, nighttime, and festive seasons. These findings offer valuable insights for traffic safety authorities and policymakers in formulating targeted interventions and evidence-based strategies to mitigate road traffic accidents.]]></description>
      <pubDate>Fri, 11 Jul 2025 10:00:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2564749</guid>
    </item>
    <item>
      <title>Reducing EMS Response Time to Crash Sites under Adverse Pavement Surface Conditions</title>
      <link>https://trid.trb.org/View/2553126</link>
      <description><![CDATA[Emergency medical services (EMS), also known as ambulance or paramedics services, play a critical role in saving lives by providing patients with the earliest urgent medical care and transportation to medical facilities. It is crucial that the EMS response time (EMS-RT), defined as the time required for EMS to reach the site after notification of the crash, be kept at a minimum to the chances of fatalities. This study aims to identify the factors that affect the EMS-RT during adverse weather conditions using descriptive and statistical modeling. Logistic and multiple linear regression techniques were utilized to develop the models. More than 58,000 EMS-RTs from 2016 to 2020 in an urban area were investigated. Results revealed that EMS-RT is significantly higher (p-value<0.05) in snowy pavement surface conditions and identified the factors that may reduce the EMS-RT. Results also showed that the resources are sufficient in the study area to keep the EMS-RT the same on a day with higher crashes compared to typical days.]]></description>
      <pubDate>Tue, 17 Jun 2025 09:58:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553126</guid>
    </item>
    <item>
      <title>Deciphering spatial heterogeneity of maritime accidents considering impact scale variations</title>
      <link>https://trid.trb.org/View/2511208</link>
      <description><![CDATA[Ensuring maritime safety has ascended as a preeminent concern within the global maritime sector. Understanding how factors affect maritime accidents’ consequences in different water areas would be of great benefit to preventing the occurrence or reducing the consequences. This study thus employed a multi-scale geographically weighted regression (MGWR) model on the accident dataset from Fujian waters in the East China Sea, to quantify the influences of different factors as well as the spatial heterogeneity in the effects of key factors on maritime accident consequence. The performances of MGWR are compared with multiple linear regression (MLR) and GWR. As expected, MGWR outperforms the other two models in terms of its ability to clearly capture the unobserved spatial heterogeneity in the effects of factors. Results reveal notably distinct influences of some factors on maritime accident consequences in different locations. An intuitive indication by MGWR is that approximately 50% of the accidents present positive coefficients of good visibility while other locations are negative, which are failed to recognize by MLR. The outcomes provide insights for making appropriate safety countermeasures and policies customized for different water areas.]]></description>
      <pubDate>Thu, 27 Feb 2025 10:44:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2511208</guid>
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