Analytic Methods in Accident Research最新文献

{"title":"A Bayesian deep learning-driven framework integrating non-stationary generalized extreme value parameterization for coneflict-based real-time crash risk prediction","authors":"Wei Wei ,&nbsp;Lai Zheng ,&nbsp;Feng Zhu ,&nbsp;Wenchen Yang ,&nbsp;Fred Mannering","doi":"10.1016/j.amar.2026.100429","DOIUrl":"10.1016/j.amar.2026.100429","url":null,"abstract":"<div><div>Estimating crash risks by assessing traffic conflicts is in important advance in traffic safety and a significant departure from the traditional approach of waiting for crashes to occur to identify problematic highway segments and intersections. This has led to the development of various non-stationary (using time-varying covariates) Extreme Value Theory (EVT) approaches where traffic conflict data are used to estimate crash risks. However, many of these EVT applications have been limited by fixed functional relationships, reliance on prior assumptions, limited sets of covariates, and the inability to capture temporal dependencies among covariates. To overcome these limitations, this study proposes a Bayesian Deep Learning - Generalized Extreme Value (BDL-GEV) framework for real-time crash risk prediction. In this approach, a Long Short-Term Memory (LSTM)-based architecture is used to dynamically generate GEV distribution parameters from historical traffic states using tailored mapping functions to better quantify crash risks. In addition, to account for unobserved heterogeneity and predictive uncertainty, a random generator is embedded in the input layer, and a Bayesian deep learning formulation with a Monte Carlo dropout is used to provide principled, data-driven uncertainty quantification. Using rear-end traffic conflict data from a signalized intersection shows that the proposed framework produces stable and plausible GEV parameter estimates with appropriate uncertainty quantification. Negative log-likelihood value and Probability Integral Transform (PIT)-based diagnostics indicate that the model achieves a good distributional fit, and sensitivity analyses confirm the efficacy of the Bayesian component, random generator, parameter mapping strategy, sliding-window length and penalty term in the loss function. Finally, compared to the traditional Bayesian hierarchical GEV model, the proposed BDL-GEV model improves both distributional fitting and one-signal-cycle-ahead crash risk estimation.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"50 ","pages":"Article 100429"},"PeriodicalIF":12.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148231161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A heteroscedastic error components random parameters logit with heterogeneity in means and variances for nighttime crash injury severity with partially time-constrained parameters","authors":"Chamroeun Se ,&nbsp;Thanapong Champahom ,&nbsp;Sajjakaj Jomnonkwao ,&nbsp;Ampol Karoonsoontawong ,&nbsp;Vatanavongs Ratanavaraha","doi":"10.1016/j.amar.2026.100430","DOIUrl":"10.1016/j.amar.2026.100430","url":null,"abstract":"<div><div>Reliable inference in crash injury severity analysis requires a unified framework that captures the depth of unobserved heterogeneity while maintaining computational efficiency. In this paper, a heteroscedastic error-components random parameters logit with heterogeneity in means and variances, embedded in a partially time-constrained structure, is employed to capture temporal instability using 2018–2020 nighttime crash data on lit and unlit roads in Thailand. Transferability tests confirmed that lit and unlit road crash severity models are statistically different, and temporal stability tests revealed significant parameter instability across all year-pairs, justifying both the separation by lighting condition and the temporally flexible modeling framework. Model fit tests against a fully time-unconstrained benchmark supported the partial-constraint framework, and the proposed specification outperformed multiple random parameters extensions. Notably, the most substantial model fit gains emerged not from random parameters or heterogeneity in means alone, but from incorporating error components that capture shared unobserved correlations between property-damage-only and severe-injury outcomes, with heteroscedasticity in the error component variances providing further significant improvement, demonstrating that the magnitude of these correlations varies with crash characteristics. On lit roads, collisions with stationary objects increased dispersion in injury severity, whereas guardrails in run-off-road crashes reduced both expected severity and variability. On unlit roads, run-off-road crashes on straight segments without guardrails and crashes involving pickup trucks heightened unobserved heterogeneity, while motorcycle-involved crashes showed reduced variability reflecting predictable rider vulnerability. Pandemic conditions in 2020 shifted severity patterns, yielding more severe crashes in high-energy collision types on lit roads, while partially mitigating severity on unlit roads where lower traffic volumes afforded greater margins for vehicle control. Infrastructure features such as guardrails, traffic signals, and speed limit signs maintained their protective effects throughout the pandemic period. Findings underscore a unified, temporally adaptive, and heterogeneity-inclusive framework and highlight guardrail effectiveness alongside persistent motorcyclist vulnerability. Collectively, these findings provide valuable knowledge for practitioners, researchers, institutions, and decision-makers seeking to enhance highway safety under nighttime conditions.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"50 ","pages":"Article 100430"},"PeriodicalIF":12.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148231162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revisiting traffic conflict modelling: comparing generalized Pareto and Lomax models for failure-induced and proximity-based conflicts","authors":"Harpreet Singh ,&nbsp;Shimul Md Mazharul Haque","doi":"10.1016/j.amar.2026.100418","DOIUrl":"10.1016/j.amar.2026.100418","url":null,"abstract":"<div><div>Over the past few decades, traffic conflict modelling with proximity-based conflicts has emerged as a key approach for estimating crash risk from traffic conflicts, with extreme value models providing a rigorous framework for extrapolating rare-event probabilities. However, proximity-based definitions of conflicts may lead to biased estimation, as they often include interactions arising from deliberate and controlled driving behaviours that may not correspond to actual crash likelihood. In contrast, failure-induced conflicts that take into account evasive action and response delays can potentially overcome this limitation. Despite these advances, a comprehensive comparison of proximity-based and failure-induced conflicts within crash risk modelling is still lacking. This study addresses this gap by comparing and evaluating the performance of different threshold-exceedance modelling approaches for crash frequency estimation. Three threshold-exceedance models are evaluated in the study, including (i) a Lomax model applied to response delays during failure-induced conflicts, (ii) a Generalized Pareto Distribution model for proximity-based conflicts, and (iii) a Generalized Pareto Distribution model for failure-induced conflicts. Empirical analysis is conducted using high-resolution trajectory data from four signalized intersections in Brisbane, Australia. The results indicate that both the Generalized Pareto Distribution model for failure-induced conflicts and the Lomax model, representing response delays within failure-induced conflicts, provided reasonable estimates of historical rear-end crashes, with predicted crash counts contained within the 95 % confidence interval of the observed crash data. In contrast, the Generalized Pareto Distribution model for the proximity-based conflicts overestimated the crash frequency. Notably, within the failure-induced conflicts, the Generalized Pareto Distribution model demonstrated greater accuracy than the Lomax model, yielding estimates closer to the observed mean and with narrower confidence bounds, thereby indicating higher predictive precision. Overall, the findings underscore the value of incorporating failure-induced conflicts into traffic conflict modelling, revealing that the Generalized Pareto Distribution model with failure-induced conflicts provides more accurate and reliable crash risk estimates.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"49 ","pages":"Article 100418"},"PeriodicalIF":12.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accounting for temporal instability in macro-level crash frequency modeling: A framework integrating high-resolution traffic dynamic patterns and spatiotemporal approaches","authors":"Jin Liu ,&nbsp;Hao Yu ,&nbsp;Guoyao Yang ,&nbsp;Zhenning Li ,&nbsp;Pan Liu","doi":"10.1016/j.amar.2025.100417","DOIUrl":"10.1016/j.amar.2025.100417","url":null,"abstract":"<div><div>Small-scale temporal factors have substantial effects on zonal crash risk, yet their influence has long been overlooked due to data limitations. This omission may introduce confounding and bias the estimation of observed annually aggregated variables. This study aims to combine high-resolution traffic dynamic patterns derived from taxi trajectory data and advanced spatiotemporal models to account for hourly-scale temporal instability in year-level crash frequency modeling. Two sets of hourly-scale spatiotemporal traffic dynamic patterns were extracted, enabling the development of small-scale models. Spatiotemporal model with adaptive smoothing spatial specification was employed to further capture temporal effects. Model specification results revealed strong temporal autocorrelation at the hourly scale, with a magnitude comparable to the spatial autocorrelation. The results showed that the model effectively captured the varying safety impacts of unobserved temporal factors across hourly intervals, and that the extracted high-resolution patterns successfully internalized previously unobserved time-relevant information into the model’s linear component. Comparative analyses demonstrated that both incorporating traffic dynamic patterns and accounting for hourly-scale spatiotemporal autocorrelation in crash frequency modeling significantly improved the model fit and predictive performance. The proposed framework detected a broader set of risk factors than purely spatial models, and yielded less biased posterior means and more rigorous intervals through disentangling small-scale noise from fixed-effect signals and preventing pseudo-replication of observations. The extracted traffic flow dynamics also represent a fundamental yet traditionally inaccessible set of factors in regional crash analysis. This study established their macro-level associations with crash risk, revealing that higher mean speeds and lower speed fluctuations are linked to reduced crash risk. Additionally, these patterns can be regarded as “high-frequency” features, and those extracted from just one month of data were proved sufficient to construct models comparable to those based on the full-year dataset. This finding enables a practical framework that leverages real-time updated high-frequency variables as model inputs for rolling crash risk prediction. The methods and findings of this study offer practitioners in-depth macro-level insights into crash causation and valuable guidance on regional traffic safety interventions.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"49 ","pages":"Article 100417"},"PeriodicalIF":12.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145842329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An evasive action-based bivariate extreme value model for estimating pedestrian crash frequency using traffic conflicts","authors":"Saransh Sahu ,&nbsp;Yasir Ali ,&nbsp;Sebastien Glaser ,&nbsp;Shimul Md Mazharul Haque","doi":"10.1016/j.amar.2026.100420","DOIUrl":"10.1016/j.amar.2026.100420","url":null,"abstract":"<div><div>Traditional models, employing extreme value theory for estimating pedestrian crashes from traffic conflicts, commonly utilise popular conflict measures, such as post encroachment time and gap time. Whilst these measures have proven useful, they are limited in identifying a vehicle–pedestrian conflict based on a fixed threshold value and depend on subjective graphical-based extreme identification methods, which neither fully capture the dynamic interactions between vehicles and pedestrians nor account for road user behaviour to identify conflicting events. This study proposes a bivariate extreme value modelling framework that analyses evasive action-based traffic conflicts by integrating risk force theory and artificial intelligence-based video analytics to estimate pedestrian crash frequency by severity. The methodological framework quantifies crash risk dynamically during vehicle–pedestrian interactions and identifies traffic conflict events based on evasive behaviours. Traffic conflicts are modelled using a Generalised Pareto distribution to capture the tail behaviour of high-risk conflicts. The proposed econometric modelling framework was validated using 72 h of traffic movement data from three signalised intersections in Queensland, Australia. Results demonstrate that the Generalised Pareto distributions effectively fit evasive action-based vehicle–pedestrian conflicts, with estimated total pedestrian frequency and severe crash frequency aligning closely with historical crash records, thereby supporting the validity of the proposed model. This study presents a scalable, behaviourally grounded methodology as an alternative to a subjective conflict identification approach, enabling continuous risk assessment for proactive pedestrian safety management and real-time safety analysis.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"49 ","pages":"Article 100420"},"PeriodicalIF":12.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of regional economic conditions in active traveler injury severity: Accounting for COVID-19 pandemic disruptions","authors":"Zehao Wang,&nbsp;Wei (David) Fan","doi":"10.1016/j.amar.2026.100419","DOIUrl":"10.1016/j.amar.2026.100419","url":null,"abstract":"<div><div>Regional economic disparities contribute to a disproportionate number of fatal and severe crashes among active travelers (pedestrians and bicyclists) in economically disadvantaged areas. Such road safety inequalities may be further exacerbated by external shocks such as the COVID-19 pandemic, due to regional variations in safety resilience. However, few studies have examined how the determinants of injury severity vary across regions with differing economic conditions, while accounting for COVID-contributing temporal shifts. This study uses North Carolina as a case study, classifying counties into three groups (i.e., highly, moderately, and least distressed counties) based on four economic indicators, and defining three pandemic periods (i.e., before, during, and after the pandemic). A partially constrained random parameter multinomial logit model with heterogeneity in the means and variances is estimated for crashes in each county group. Results show that the effects of factors are more stable in the least distressed counties, suggesting stronger safety resilience under external shocks. Additionally, during the pandemic, alcohol-impaired driving significantly affected injury severity only in highly and moderately distressed counties. Out-of-sample predictions further suggest that the probability of severe injuries among active travelers increases with rising regional economic distress and after the pandemic. Moreover, compared to the least distressed counties, the reduced safety resilience in highly and moderately distressed counties is attributed to weaker recovery and resistance capacities, respectively. These findings provide valuable insights for formulating region-specific policies, detecting system vulnerabilities, and promoting equitable and sustainable active transportation systems.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"49 ","pages":"Article 100419"},"PeriodicalIF":12.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A note on observed injury bias in police-reported pre-crash travel speed estimates","authors":"Mouyid Islam ,&nbsp;Fred Mannering","doi":"10.1016/j.amar.2025.100407","DOIUrl":"10.1016/j.amar.2025.100407","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"48 ","pages":"Article 100407"},"PeriodicalIF":12.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145424812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian forecasting of short-term crash risk with conditional extreme value models: A comparison between one-stage and two-stage approaches","authors":"Depeng Niu,&nbsp;Tarek Sayed","doi":"10.1016/j.amar.2025.100409","DOIUrl":"10.1016/j.amar.2025.100409","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"48 ","pages":"Article 100409"},"PeriodicalIF":12.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analyzing crash injury severities with deep learning and advanced statistical models: An assessment of methodological challenges","authors":"MohammadAli Seyfi ,&nbsp;Amir Mohammad Karimi Mamaghan ,&nbsp;Ali Behnood ,&nbsp;Fred Mannering","doi":"10.1016/j.amar.2025.100405","DOIUrl":"10.1016/j.amar.2025.100405","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"48 ","pages":"Article 100405"},"PeriodicalIF":12.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint analysis on pedestrian injury severity across vehicle movements at intersections: Addressing temporal instability and spatial correlations","authors":"Chenzhu Wang,&nbsp;Mohamed Abdel-Aty,&nbsp;Natalia Barbour","doi":"10.1016/j.amar.2025.100406","DOIUrl":"10.1016/j.amar.2025.100406","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"48 ","pages":"Article 100406"},"PeriodicalIF":12.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145364125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}