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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>Bayesian forecasting of short-term crash risk with conditional extreme value models: A comparison between one-stage and two-stage approaches</title>
      <link>https://trid.trb.org/View/2633047</link>
      <description><![CDATA[Extreme Value Theory (EVT) has become a widely used approach for quantifying crash risk from traffic conflict data. Most existing applications, however, rely on unconditional models, which fail to adequately capture dependence in extreme traffic conflicts and do not reliably predict future crash risk. To demonstrate the potential of conditional EVT models for advancing short-term crash risk forecasting, this study compares two conditional EVT approaches within a Bayesian framework that address extremal dependence from distinct perspectives. The first approach is the two-stage GARCH-EVT framework, where conditional mean and variance are modeled using GARCH-type specifications before EVT is applied to the standardized residuals. Both traditional and covariate-augmented variants are examined. The second approach uses a one-stage conditional peak-over-threshold (POT) model, represented by the score-driven POT model, which directly captures dynamics in the conditional exceedance probability and the distribution of exceedance sizes. Crash risk is quantified using two conditional tail risk measures, Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR), with forecasting performance evaluated through traditional and comparative backtesting. An empirical study examines rear-end conflicts collected at two signalized intersections over four observation days to generate one-cycle-ahead crash risk forecasts during the out-of-sample period. Traditional backtesting indicates that both the covariate-augmented GARCH-EVT and the score-driven POT approaches produce valid and comparable forecasts, with the two-stage method yielding estimates with lower uncertainty. Comparative backtesting, however, shows that the score-driven POT model achieves slightly superior forecasting accuracy. The weaker performance of the two-stage framework can be attributed to partial removal of extremal dependence, sensitivity to substitute values in cycles without conflicts, and the limitations inherent in its two-stage structure.]]></description>
      <pubDate>Wed, 04 Mar 2026 09:16:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633047</guid>
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    <item>
      <title>A note on observed injury bias in police-reported pre-crash travel speed estimates</title>
      <link>https://trid.trb.org/View/2617181</link>
      <description><![CDATA[Vehicle pre-crash travel speed is one of the most important determinants of driver injury severity. However, pre-crash travel speed estimates made by police officers, especially those in crashes with less severe injuries (where there is less of a need for high levels of accuracy due to potential litigation), can be susceptible to biases because of the tendency to associate less severe driver injuries with lower pre-crash travel speeds. This potential bias makes the use of pre-crash travel speeds in injury-severity modeling highly problematic due to its endogeneity with injury severity. To detect the presence and extent of this problem, a bias correction term for pre-crash travel speed estimation equations is applied by treating injury-severity level (discrete) and pre-crash travel speed (continuous) as a discrete/continuous econometric model. The findings show that for severe injury crashes, the bias correction is statistically insignificant, reflecting the increased accuracy required of police officers in severe crashes. However, for crashes resulting in less severe occupant injuries, there is a significant bias resulting from observed injury levels, which distorts the effects of explanatory variables on pre-crash travel speed estimates. The results of this paper not only provide empirical evidence of potential endogeneity problems in models of crash injury severity but also underscore the need to more fully consider potential endogeneity issues and their associated consequences in statistical models and machine learning models.]]></description>
      <pubDate>Mon, 09 Feb 2026 08:53:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617181</guid>
    </item>
    <item>
      <title>Joint analysis on pedestrian injury severity across vehicle movements at intersections: Addressing temporal instability and spatial correlations</title>
      <link>https://trid.trb.org/View/2613636</link>
      <description><![CDATA[Intersection-related vehicle–pedestrian collisions present a significant challenge in transportation safety due to the complexity and hazards of intersections within urban road networks. This study introduces a spatially aggregated ordered logit model with a joint multivariate normal structure, which offers distinct advantages over conventional models by effectively capturing correlations among vehicle movement types (left-turn, straight, and right-turn) and accounting for residual aggregation at both intersection and county levels. Using a dataset of 4280 pedestrian-vehicle crashes in Florida from 2019 to 2023, incorporating pedestrian, driver, vehicle, intersection, environmental, crash, and temporal characteristics, the proposed model demonstrates superior performance in capturing interdependencies among vehicle maneuvers. Four temporally consistently significant variables are identified including pedestrians aged under 18 years old, urban areas, major roadway speed limits below 30 mph and lighted roadways during nighttime. In contrast, several other variables demonstrate significance only in specific years, reflecting notable temporal variation in their impact on pedestrian injury severity. A series of statistical tests, including normality distribution tests, spatial autocorrelation tests, and assessments of independence and homoscedasticity, were conducted to validate the model. The results confirm the model’s ability to satisfy critical statistical assumptions—normality, independence, homoscedasticity, and spatial autocorrelation—and its robustness in achieving a high degree of spatial independence. The findings underscore the need for targeted safety measures and intersection design strategies to mitigate collision risks. By offering enhanced accuracy, temporal flexibility, and spatial insights, the proposed modeling approach provides a robust framework for developing evidence-based safety interventions and optimizing intersection designs to reduce pedestrian injury severity.]]></description>
      <pubDate>Tue, 20 Jan 2026 10:17:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613636</guid>
    </item>
    <item>
      <title>A note on random parameters models of crash injury severities with 𝑘-means clustering for data preprocessing</title>
      <link>https://trid.trb.org/View/2613635</link>
      <description><![CDATA[Many recent studies have shown that data segmentation (seeking to segment the data into potentially homogeneous groups by factors such as data-collection year, driver age, driver gender, driver behaviors, etc.) can significantly improve crash injury-severity model estimation results. However, the choice of the segmentation criterion is often speculative and based on a predetermined expectation of homogeneity by the analyst. In an effort to improve model estimation results, a potential alternative to analyst-specified data segmentation is to preprocess the data using multivariate machine learning techniques. This paper demonstrates the potential of data preprocessing using 𝑘-means clustering as a means to improve the estimation of statistical models. Empirical results show that the combination of 𝑘-means clustering, in addition to data segmentation by year to account for temporal shifts in parameters, result in an improved statistical fit (a hybrid of analyst-specified and machine learning data segmentation). Furthermore, a comparison of the marginal effects generated by the clustered and non-clustered models suggests that the preprocessing of data by clustering techniques can result in more precise marginal effect estimates to guide safety policies. The findings show considerable potential for using machine learning algorithms, such as 𝑘-means clustering, to improve the estimation results of statistical models.]]></description>
      <pubDate>Tue, 20 Jan 2026 10:17:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613635</guid>
    </item>
    <item>
      <title>Analyzing crash injury severities with deep learning and advanced statistical models: An assessment of methodological challenges</title>
      <link>https://trid.trb.org/View/2604233</link>
      <description><![CDATA[In this research, statistical and deep learning models are applied to determine factors that affect motorcycle crash-injury severities. Four methodological challenges are considered: 1) imbalanced data (because fatal injuries are an exceedingly small portion of all resulting injury outcomes); 2) unobserved heterogeneity (because many unobserved factors will influence resulting injury severities); 3) quantification of variable effects; and 4) the possibility of temporally shifting relationships among variables. Convolutional neural networks and deep neural networks are the deep learning models considered, and random parameters logit models with heterogeneity in means and variances is the statistical model considered. Extensive experimentation indicated that data imbalance and unobserved heterogeneity could be best handled in deep learning models with a Bayesian deep neural network with a random generator and weighted loss function. With statistical modeling indicating significant shifts in model parameters over time, the data were segmented by year and both statistical and deep learning models were estimated. While techniques are available for deep learning to potentially handle data imbalance and unobserved heterogeneity, the quantification of variable effects and temporal shifts remains a challenge. For example, a comparison of variable effects show that the deep learning estimates of variable effects are generally inconsistent with the plausible values generated by the statistical models in terms of magnitudes and occasionally in terms of direction, indicating a need for improvements in deep-learning variable-effect extraction methods. The findings also show the need for future work to isolate the effect of complex temporal relationships which are currently imbedded in deep learning approaches, because the segmentation of data that has been used in statistical models to isolate temporal effects, and even the use of all data and defining new time-dependent variables, may not be a viable deep learning option due to the potential loss in predictive performance.]]></description>
      <pubDate>Mon, 22 Dec 2025 16:07:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604233</guid>
    </item>
    <item>
      <title>A unified framework for modeling traffic crashes from hierarchical spatial resolutions</title>
      <link>https://trid.trb.org/View/2577140</link>
      <description><![CDATA[Independent traffic crash modeling approaches do not account for the embedded relationships related to the multi-resolution data structure, leading to mis-specified estimations. The recently developed integrated frameworks demonstrate the capability of addressing this drawback. The current study proposes an integrated framework that accommodates information from multiple spatial units and observation resolutions. Specifically, the study develops an integrated model system that allows for the influence of independent variables from disaggregate crash record, micro-facility (segment and intersection) and macro (traffic analysis zone) level simultaneously within the macro level propensity estimation. The empirical analysis considers disaggregate crash records of 1,818 segments and 4,184 intersections from 300 traffic analysis zones in the City of Orlando, Florida. These crash records contain crash-specific factors, driver and vehicle factors, roadway, road environmental and weather information of each crash record. For micro-facility and macro levels, an exhaustive set of independent variables including roadway and traffic factors, land-use and built environment attributes, and sociodemographic characteristics are considered. The proposed model system can also accommodate for hierarchical correlations among the data across observation resolutions and parameter variability across the system. The empirical analysis is augmented by employing several goodness of fit and predictive measures. The results clearly demonstrate the improved performance offered by the proposed integrated model system relative to the non-integrated model. A validation exercise also highlights the superiority of the proposed framework. The application of the proposed integrated framework can allow transportation professionals to adopt policy-based, site-specific, and outcome-specific solutions simultaneously.]]></description>
      <pubDate>Mon, 08 Sep 2025 14:54:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2577140</guid>
    </item>
    <item>
      <title>Assessing the impact of COVID-19 on driver injury severities in fixed-object passenger car crashes: Insights from temporal and partially constrained modeling analysis</title>
      <link>https://trid.trb.org/View/2569704</link>
      <description><![CDATA[The COVID-19 pandemic reshaped the global transportation sector, including in the U.S., creating an unprecedented shift in traffic patterns. Despite a reduction in vehicle miles traveled (VMT), crash severity, particularly fatalities, increased significantly. Among all crash types, fixed-object collisions have consistently posed a critical safety concern due to their disproportionately high fatality rates, a trend further exacerbated during the pandemic. This study examines the impact of COVID-19 on driver injury severity in fixed-object passenger car crashes in Oregon. The authors estimated separate unconstrained models of driver injury severity in fixed-object passenger car crashes across three distinct time periods: before pandemic (March 2019–February 2020), during pandemic (March 2020–February 2021), and after pandemic (March 2021–February 2022), as well as a partially constrained model utilizing a random parameters multinomial logit model that incorporates heterogeneity in both means and variances of the random parameters. The analysis utilized 22,522 crash records for the state of Oregon obtained from the Oregon Department of Transportation. Likelihood ratio tests were performed to assess the temporal instability of model parameter estimates throughout the three time periods and to compare the partially constrained and unconstrained models. The findings indicated notable temporal variations in the determinants of injury severity, encompassing driver attributes, crash circumstances, roadway characteristics, and environmental elements. While alcohol consumption, improper driving, and collisions with trees consistently influenced injury severity across all periods, factors such as gender, airbag deployment, speeding, seasonal variations, and road surface conditions exhibited changing effects. Out-of-sample predictions indicate that severe injuries in fixed-object crashes were consistently underestimated, highlighting growing concerns about increasing crash severity, particularly in the post-pandemic period.]]></description>
      <pubDate>Tue, 15 Jul 2025 09:47:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2569704</guid>
    </item>
    <item>
      <title>Autonomous vehicle sensor data and the estimation of network-wide spatiotemporal generalized extreme value models of rear-end injury-severity crash frequencies</title>
      <link>https://trid.trb.org/View/2562273</link>
      <description><![CDATA[Existing traffic conflict-based extreme value modeling applications are primarily restricted to a few concentrated locations due to the scarcity of network-wide vehicular trajectory data and the constraints associated with traditional network-wide modeling techniques. As such, this study develops a network-wide bivariate spatiotemporal non-stationarity generalized extreme value model to estimate rear-end crash frequency by injury severity level using Argo AI autonomous vehicle sensor data. Fusing this dataset with road network data from the Florida Department of Transportation, this paper studies a road network of 57 intersections and mid-blocks in Miami, Florida. Modified time-to-collision and the expected post-collision velocity difference (Delta-V) are used to estimate severe and non-severe rear-end crashes. Road geometry, road classification, and traffic state variables are used as covariates to address spatiotemporal heterogeneity in the generalized extreme value model estimation. Results show the significant impact of spatiotemporal variables such as lane width, median width, dedicated street parking, dedicated bike lane, vehicle class, and road class on rear-end crash frequency by injury severity levels. It is found that the bivariate spatiotemporal generalized extreme value model outperforms the bivariate random intercept generalized extreme value model and the univariate generalized extreme value model with conditional severity probability when benchmarked against observed annual crash frequency using root mean square error and the coefficient of determination (R-squared). Additionally, the bivariate spatiotemporal generalized extreme value model provides the closest estimate of observed severe crashes by roadway segments in the study area. The findings of this study underscore the importance of proactive network-wide safety management using spatiotemporal heterogeneity and autonomous vehicle sensor data to estimate crash frequency by severity for real-time decision-making.]]></description>
      <pubDate>Mon, 30 Jun 2025 16:37:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562273</guid>
    </item>
    <item>
      <title>Autonomous vehicle lane-changing dynamics and impact on the immediate follower</title>
      <link>https://trid.trb.org/View/2556858</link>
      <description><![CDATA[Understanding and modelling lane-changing behaviour are critical aspects of microscopic traffic flow modelling, safety analyses, and microsimulation due to their significant impact on traffic flow characteristics and safety. Among the three aspects of lane-changing behaviour—decision-making, execution, and impact—the lane-changing impact has been comparatively underexplored in the literature, which is disproportionate to its importance. A lack of proper understanding of lane-changing impact may lead to inaccurate planning and interpretation of mixed traffic stream comprising both autonomous and human-driven vehicles. Motivated by this research gap, the current study investigates the lane-changing impact of autonomous vehicles on the immediate follower using the publicly available Waymo Open Dataset. Human-driven vehicle lane-changing data are also extracted from the same database and used for comparison. Lane-changing impact on traffic flow efficiency and safety is examined through the speed reduction of the follower in the target lane and deceleration rate to avoid a collision for the same follower, respectively. A correlated random parameters linear regression model is employed to assess the speed reduction of the follower as a function of lane-change duration, lag gap, lane-changer speed, and a dummy variable indicating whether the lane-changer is an autonomous vehicle or a human-driven vehicle. The results reveal that lane changes executed by autonomous vehicles may cause greater or lesser speed reductions for the follower compared to those executed by human-driven vehicles, which could be attributed to the heterogeneous behaviour of followers perceiving and responding differently to autonomous vehicle lane-changes compared to human-driven ones. Further, the block maxima and peak over threshold models are developed to estimate crash risk for the follower in the target lane using a deceleration rate to avoid a collision conflict measure. The results suggest that the risk of a collision increases substantially when the lane-changer is an autonomous vehicle. This elevated risk may be associated with drivers’ lack of trust in autonomous vehicles and traffic dynamics, reflecting self-inflicting hard deceleration to avoid potential collisions. Overall, this study highlights the heterogeneous impacts of lane-changing by autonomous vehicles on the immediate follower, emphasising the need for tailored models that accurately capture the dynamics of surrounding traffic behaviour. The findings will be helpful to road safety engineers and policymakers in planning mixed traffic with the safe integration of autonomous vehicles.]]></description>
      <pubDate>Thu, 26 Jun 2025 11:42:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2556858</guid>
    </item>
    <item>
      <title>Exploring the dynamic determinants of general aviation accidents across flight phases and time: A random parameter bivariate probit approach with heterogeneity in means</title>
      <link>https://trid.trb.org/View/2559591</link>
      <description><![CDATA[General aviation experiences significant variation in accident characteristics across flight phases. This study seeks to investigate the phase transferability and temporal stability of determinants influencing general aviation accidents, using the U.S. data (2008–2019) from the National Transportation Safety Board. To achieve this, a random parameter bivariate approach with heterogeneity in means was employed, focusing on two binary outcomes: injury severity (fatal/severe vs. minor/none) and aircraft damage (destroyed vs. non-destroyed). Four flight phases were analyzed: departure, enroute, maneuvering, and arrival. The data were divided into three time periods, 2008–2011, 2012–2015, and 2016–2019, to assess the determinants’ temporal stability. Likelihood ratio tests revealed that pilot injury and aircraft damage risks exhibit phase non-transferability and temporal instability. Out-of-sample predictions indicated a steady rise in fatal or severe injury risk, while aircraft damage risk initially increased before declining over time. A significant positive correlation between pilot injury and aircraft damage was observed through model estimation. Key factors, including pilot, aircraft, flight, and environmental conditions, significantly influenced both outcomes. Moreover, factors such as decision-making errors, adverse physiological conditions, fixed landing gear, and visual meteorological conditions showed both phase transferability and temporal stability. However, most factors were phase- and period-specific. Based on these findings, targeted measures, such as pilot escape and survival training, as well as phase-specific, scenario-based training, are proposed to mitigate general aviation risks.]]></description>
      <pubDate>Thu, 26 Jun 2025 11:42:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2559591</guid>
    </item>
    <item>
      <title>Grouped random parameters Poisson-Lindley model with spatial effects addressing crashes at intersections: Insights from visual environment features and spatiotemporal instability</title>
      <link>https://trid.trb.org/View/2556859</link>
      <description><![CDATA[This study investigates the unobserved heterogeneity and spatiotemporal variations in the effects of visual environment features on intersection crash frequency. A Grouped Random Parameters Poisson-Lindley model with Spatial Effects is developed to account for spatial variations at both the macro (county) and micro (intersection) levels. The analysis utilizes crash data from 2,044 intersections across 12 Florida counties, collected between 2020 and 2022, along with explanatory variables including traffic flow, geometric design characteristics, and visual environment features (extracted from Google Street View images). Comparing to existing methods (e.g., Fixed, Random Parameters, and Grouped Random Parameters Poisson-Lindley models), the proposed approach, which incorporates both macro- and micro-level spatial effects, demonstrates significantly improved model performance. Additionally, the temporal variations of explanatory variables over the three-year period are clearly identified through out-of-sample predictions and marginal effects analysis. Two visual environment features, Vegetation and Grass, result in the identification of grouped random parameters, highlighting the varying impact of these features on intersection crash frequency across the 12 counties. The findings also reveal a strengthening of micro-level spatial effects, indicating heightened spatial correlations between adjacent intersections following the COVID-19 pandemic. Key factors influencing crash frequency include traffic volume, four-legged intersections, major roads with more than four lanes, wider minor roads, and a higher proportion of vehicles in the drivers’ field of vision. These results provide valuable insights into the influence of drivers’ visual environment on intersection safety and offer policy recommendations for enhancing traffic safety.]]></description>
      <pubDate>Thu, 26 Jun 2025 11:42:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2556859</guid>
    </item>
    <item>
      <title>Short-term conflict-based crash risk forecasting: A Bayesian conditional peak-over-threshold approach</title>
      <link>https://trid.trb.org/View/2543470</link>
      <description><![CDATA[Forecasting short-term crash risks is crucial for real-time road safety management, yet this research area remains largely underexplored. Classical Extreme Value Theory (EVT) models assume independent observations, limiting their ability to capture the clustering behavior in occurrence times and magnitudes of extreme traffic conflicts. To overcome this limitation, the authors introduce conditional peak-over-threshold (POT) models that incorporate time-varying parameters to simultaneously capture the dynamics of extreme traffic conflicts and enable forecasting for crash risk. Within the framework of marked point process (MPP) and EVT, the authors develop the conditional POT models based on two observation-driven approaches (self-exciting and score-driven) through Bayesian inference. A dynamic risk measure, Value-at-Risk (VaR), is employed to assess the performance of these conditional POT models for crash risk forecasting. Empirical analysis of rear-end conflict data collected from a signalized intersection across two separate days demonstrates that both self-exciting and score-driven POT models effectively characterize the clustering behavior of extreme traffic conflicts. Furthermore, backtesting confirms that conditional POT models provide more accurate crash risk forecasts than classical POT models, which tend to underestimate crash risk by ignoring temporal dependence in extreme traffic conflicts. Among the examined model specifications, score-driven POT models demonstrate superior forecasting performance. The authors' proposed Bayesian conditional POT approach provides probabilistic forecasting that enables direct uncertainty quantification and dynamic monitoring of crash risk, thereby supporting informed safety decisions.]]></description>
      <pubDate>Wed, 28 May 2025 12:00:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2543470</guid>
    </item>
    <item>
      <title>Modeling economic loss associated with fishing vessel accidents: A Bayesian random-parameter generalized beta of the second kind model with heterogeneity in means</title>
      <link>https://trid.trb.org/View/2540030</link>
      <description><![CDATA[The distribution of economic loss associated with vessel accidents typically exhibits non-negative, continuous, positively skewed, and heavy-tailed characteristics. Another challenge in analyzing fishing vessel accidents is the absence of relevant factors. Ignoring such heterogeneity caused by unobserved factors potentially leads to inaccurate inferences. In the present study, a novel Bayesian random-parameter generalized beta of the second kind (GB2) model with possible heterogeneity in means and variances was developed. The flexible GB2 distribution was harnessed to model the skewed and heavy-tailed response variable, and the random parameters were specified to capture the unobserved heterogeneity. The proposed method was validated using an insurance claim dataset with 3,448 fishing vessel accidents within Ningbo waters during 2018–2022. The proposed model successfully identified significant influential factors, including fixed parameters, random parameters, and covariates influencing the means of the random parameters. Specifically, offshore and inevitable accidents, fishing transport vessels, double-trawl vessels with mechanical failures, wide-hulled vessels, and favorable sea conditions were associated with greater economic loss. Special attention should also be paid to nighttime accidents involving steel-hulled fishing transport vessels, as this accident type emerged to result in greater loss during the pandemic lockdown period. The authors' approach can accommodate the abnormality, skewness, and heavy-tail of vessel accident loss data, adjust for the bias introduced by unobserved factors, and uncover the interactive relationship among covariates. Targeted countermeasures were proposed to mitigate economic loss resulting from fishing vessel accidents.]]></description>
      <pubDate>Fri, 23 May 2025 15:35:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2540030</guid>
    </item>
    <item>
      <title>Assessment of vehicle age as a contributor to temporal shifts in single-vehicle driver injury severities</title>
      <link>https://trid.trb.org/View/2534078</link>
      <description><![CDATA[Vehicle age plays a crucial role in crash occurrence and occupant injury severity, with older vehicles historically associated with more severe injury outcomes compared to newer models. This study investigates the temporal instability of specific injury-contributing factors for single-vehicle, single-occupant crashes involving vehicles equal or less than 3 years old at the time of the crash, using data from Alabama’s Critical Analysis Reporting Environment (CARE) system. The analysis spans four time points: 2010, 2014, 2018, and 2022. Preliminary data analysis indicates a reduction in new vehicle severe injury crashes from 7.25 % in 2010 to 4.05 % in 2022. Random parameters multinomial logit models with heterogeneity in means were developed to identify crash factors significantly related to injury outcomes. Key findings highlight the consistent trend of higher severity crashes in which drivers fail to use a seatbelt and airbags are deployed. However, there was a notable decrease in severe injuries for 3-year-old vehicles involved in crashes in 2022 compared to previous years. Model results revealed that this benefit is particularly evident in the reduced likelihood of severe injury among drivers older than 65 years where airbags were deployed over the years, except for 2010. The study indicates the importance of advancements in vehicle technology in enhancing occupant safety. It also emphasizes the need for ongoing research into driver behavior, road conditions, and the evolution of safety standards to fully leverage these technological improvements. The findings suggest that continuous updates to driver education and awareness programs are essential to reflect new technologies and changing driving environments, ensuring drivers can effectively utilize advanced safety features.]]></description>
      <pubDate>Wed, 30 Apr 2025 16:59:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2534078</guid>
    </item>
    <item>
      <title>A physics-informed risk force theory for estimating pedestrian crash risk by severity using artificial intelligence-based video analytics</title>
      <link>https://trid.trb.org/View/2521974</link>
      <description><![CDATA[Pedestrians are a vulnerable road user group, and assessing their crash risk at critical locations, such as signalized intersections, is crucial for developing targeted countermeasures. While conflict-based safety assessments using traffic conflict measures effectively estimate crash risk, they often overlook the heterogeneity of different motorized and non-motorized road users. Conversely, field-based theories account for road user heterogeneity, yet their application in crash risk assessment, specifically evaluating pedestrian crash risk, and particularly by severity level using real-world data, remains underexplored. This study introduces a novel application of physics-informed risk force theory for assessing pedestrian crash risk by injury severity, utilizing facility-based video data at signalized intersections. The study derives risk forces that encompass pedestrian and vehicle heterogeneity as a nearness-to-collision component and vehicle impact speed as a severity component. Stationary and non-stationary extreme value models, incorporating exogenous traffic parameters at the signal cycle level, were applied to 72 h of video data collected from three signalized intersections in Queensland, Australia. The non-stationary univariate extreme value model with risk force as a measure of nearness-to-collision reliably estimated total crash frequency compared to historical crash records. In addition, the bivariate extreme value model with risk force and impact speed reasonably predicted pedestrian crashes by severity levels. The results also indicate that an increased volume of interacting pedestrians and left-turning vehicles elevates the likelihood of total and severe crashes. The proposed pedestrian crash risk assessment framework offers a unified and efficient proactive approach that can enhance automated safety analysis of traffic facilities, thereby assisting road authorities in real-time safety management.]]></description>
      <pubDate>Mon, 31 Mar 2025 08:54:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2521974</guid>
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