Analytic Methods in Accident Research最新文献

{"title":"A physics-informed risk force theory for estimating pedestrian crash risk by severity using artificial intelligence-based video analytics","authors":"Saransh Sahu ,&nbsp;Yasir Ali ,&nbsp;Sebastien Glaser ,&nbsp;Md Mazharul Haque","doi":"10.1016/j.amar.2025.100382","DOIUrl":"10.1016/j.amar.2025.100382","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"46 ","pages":"Article 100382"},"PeriodicalIF":12.5,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143591645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Is there an emotional dimension to road safety? A spatial analysis for traffic crashes considering streetscape perception and built environment","authors":"Yiping Liu ,&nbsp;Tiantian Chen ,&nbsp;Hyungchul Chung ,&nbsp;Kitae Jang ,&nbsp;Pengpeng Xu","doi":"10.1016/j.amar.2025.100374","DOIUrl":"10.1016/j.amar.2025.100374","url":null,"abstract":"<div><div>Modern streetview image data provide two types of valuable information: the objective built environment and humans’ subjective perception of the streetscape. In the road safety domain, the built environment has been identified as playing a significant role while indicators of human perception are commonly used to evaluate street quality in urban planning. However, studies examining the association between humans’ perceptions of the streetscape and traffic crashes remain limited. This study aims to address this question and to inform safety considerations at the micro level in the planning process for the targeted streets. To answer the question, this study integrates databases on motor vehicle crashes, points of interest, street view images, and road networks for the urban area of Daejeon city in South Korea in 2019. A deep learning model was employed to calculate six perceptual indicators–wealthy, lively, boring, depressing, safety, and beautiful–based on a crowdsourcing dataset. Furthermore, a Bayesian multivariate Poisson-lognormal model with spatial-varying coefficients was introduced to simultaneously account for spatial random effect and the shared unobserved effect across crash severity levels. Results indicate that four of the six perceptual variables significantly affect the number of slight injury crashes, showing spatially heterogeneous effects. Based on the values of human perception indicators and their impacts on traffic crashes, we identified road segments which need special attention to objective safety performance when considering street renovation. Additionally, built environment factors such as the proportion of vegetation, the presence of sidewalks and fences, and points of interest (including educational, health service, and commercial establishments) were found to reduce the number of motor vehicle crashes. Overall, the findings are expected to facilitate the safety-enhanced street planning project, and contribute to the development of human-centric cities.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"46 ","pages":"Article 100374"},"PeriodicalIF":12.5,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A note on data segmentation, sample size, and model specification for crash injury severity modeling","authors":"Qinzhong Hou ,&nbsp;Jinglun Zhuang ,&nbsp;Chenrui Zhai ,&nbsp;Xiaoyan Huo ,&nbsp;Fred Mannering","doi":"10.1016/j.amar.2025.100373","DOIUrl":"10.1016/j.amar.2025.100373","url":null,"abstract":"<div><div>In recent years, the statistical assessment of crash injury severity data has increasingly begun to segment the available crash data into observational groups to explore the possibility that such groups may share the same estimated parameters. This method is commonly used to account for parameters that may shift over time, where the data is often segmented into groups based on observational year. Unfortunately, such data segmentation can lead to small samples within each group, which has caused some concern about decreasing sample size. However, concerns about diminishing sample size are often misplaced and not well understood. In this paper, the impact of data segmentation is assessed by estimating models that address the possibility of temporally shifting parameters. Starting with a large 80,000 observation sample, the process involves randomly segmenting the data into groups with sample sizes varying from 1000 to 40,000, and then assessing the difference between the estimated data-segmented models and the overall model (using all available data) using likelihood ratio tests. The results indicate that: 1) model specification is extremely important, regardless of sample size, 2) statistical tests should be used to determine the suitability of simple versus complex models, not sample size, and 3) the variance/covariance structure of the data being considered determines model specification and sample size effects, which means sample-size requirements are data-specific, and that general statements regarding minimum sample size requirements for specific model types cannot be made.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"45 ","pages":"Article 100373"},"PeriodicalIF":12.5,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430171","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":"How do drivers manage speed at tunnel entrances? Insights from uncorrelated grouped random parameters duration models for model invalidation and performance recovery times","authors":"Yunjie Ju ,&nbsp;Shi Ye ,&nbsp;Tiantian Chen ,&nbsp;Guanyang Xing ,&nbsp;Feng Chen","doi":"10.1016/j.amar.2025.100371","DOIUrl":"10.1016/j.amar.2025.100371","url":null,"abstract":"<div><div>Human drivers must quickly adjust to perturbations at tunnel entrances (i.e., the rapid switching of cross-sections, abrupt longitudinal changes in the driving environment, and changes in visual illumination, denoted “tunnel transition perturbations”) to regain control of their vehicles, especially when managing speed to prevent motor overshoot. Previous research has assessed drivers’ visual adaptation rather than variations in vehicle control under tunnel transition perturbations. In this study, a sample entropy method was used to measure the safety–critical duration of speed control events at tunnel entrances and thereby reveal the participants’ speed adaptation and recovery performance under tunnel transition perturbations. Two key metrics—model invalidation time and performance recovery time—were introduced, and an uncorrelated grouped random parameters hazard-based duration model was developed. Road grade, road curvature, income, and time having held a license were positively associated with model invalidation time, while a history of accidents in the past 12 months was negatively associated with model invalidation time. In addition, road grade, road curvature, and income had heterogeneous effects on model invalidation time. Moreover, a history of accidents in the past 12 months moderated the relationship between road grade and model invalidation time. Furthermore, road curvature, average annual mileage, and sleep deprivation significantly influenced performance recovery time, while road grade and non-fatigue condition had heterogeneous effects on performance recovery time. Overall, this study demonstrated that the participants’ personal characteristics and experiences significantly shaped the development of their internal models, and that their current status and perception had a substantial influence on their performance recovery under tunnel transition perturbations. These insights enhance understanding of the mechanisms of drivers’ motor control under tunnel transition perturbations and will therefore enable improvement of road traffic design and safety management at tunnel entrances.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"45 ","pages":"Article 100371"},"PeriodicalIF":12.5,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103716","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":"Understanding the effects of underreporting on injury severity estimation of single-vehicle motorcycle crashes: A hybrid approach incorporating majority class oversampling and random parameters with heterogeneity-in-means","authors":"Nawaf Alnawmasi ,&nbsp;Apostolos Ziakopoulos ,&nbsp;Athanasios Theofilatos ,&nbsp;Yasir Ali","doi":"10.1016/j.amar.2025.100372","DOIUrl":"10.1016/j.amar.2025.100372","url":null,"abstract":"<div><div>The underreporting of crash data is a well-documented issue in road safety literature, but few studies have focused on addressing this problem in the context of analyzing crash injury severities. This paper aims to provide an empirical assessment of the impact of underreporting issue using a hybrid approach in estimating injury severity for single-vehicle motorcycle crashes. Unlike traditional machine learning methods that oversample the minority class (the category with the fewer observations such as fatal and severe injuries), the present study oversamples the majority class (i.e. minor injuries), which are often underreported in crash datasets, thus providing a fresh perspective on this issue. Afterwards, random parameter models with heterogeneity in means and variances were applied. The results of this study, as supported by the likelihood ratio tests, indicate that the key variables influencing motorcyclists’ injury severities remain consistent across both original and oversampled data models. Specifically, crashes occurring during slowing down or stopping are associated with lower injury severity, whereas negotiating a right turn increases the probability of severe injuries. Interestingly, crashes that occur on dry pavements are associated with higher injury severity when compared to wet pavements, likely due to rider behavior adjustments in adverse weather conditions to compensate for the risk. Overall, the oversampled models have a significantly lower marginal effects values compared to the original model’s marginal effects. This study provides a foundation for further examination of underreporting issue in crash injury severity modelling and also highlights the need to capture the dynamics of crash injuries suggesting that alternative approaches could improve the understanding and hence road safety management. Future studies are encouraged to replicate this methodology to validate the findings as well as utilize other advanced machine learning algorithms, like tree-based models to assess underreporting mitigation.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"45 ","pages":"Article 100372"},"PeriodicalIF":12.5,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103711","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":"Time-dependent effect of advanced driver assistance systems on driver behavior based on connected vehicle data","authors":"Yuzhi Chen ,&nbsp;Yuanchang Xie ,&nbsp;Chen Wang ,&nbsp;Liguo Yang ,&nbsp;Nan Zheng ,&nbsp;Lan Wu","doi":"10.1016/j.amar.2025.100370","DOIUrl":"10.1016/j.amar.2025.100370","url":null,"abstract":"<div><div>This paper proposes a novel functional data analysis approach to investigate the time-dependent effect of advanced driver assistance systems (ADAS), specifically forward collision warnings, on driver speed reduction behavior. Existing aggregate measures compress temporal information within driver behavior profiles and fail to explicitly reveal the temporal dependency of such effect. With the proposed approach, the functional representation method is adopted to capture the underlying driver behavior in response to warning messages and address issues of irregularly spaced observations and measurement errors; the results of the functional principal component analysis with the bootstrap-enhanced Kaiser-Guttman method reveal important patterns in driver response behaviors; and a nonparametric functional varying coefficient regression model, considering vehicle initial motions and drivers’ acceleration styles, is established. This regression model utilizes coefficient functions to estimate the time-dependent effect of ADAS. The proposed approach is evaluated based on the New York City connected vehicle dataset using forward collision warning event records. The results suggest that the treatment effect of the warning messages is time-dependent, initially increasing before progressively decreasing over time. Driver responses can be decomposed into several phases at the 95 % confidence level, including reaction time (1.3 s), brake adjustment time (1.3 s), progressive braking duration (2.7 s), and effective treatment duration (4.0 s). The time-dependent bootstrap confidence interval confirms driver heterogeneity in these distinct phases. The proposed functional data analysis approach can serve as a paradigm for quantifying the treatment effect of other ADAS applications. The findings can support the improvements of ADAS design and the development and calibration of driver behavior models accounting for ADAS.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"45 ","pages":"Article 100370"},"PeriodicalIF":12.5,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103715","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 unified probabilistic approach to traffic conflict detection","authors":"Yiru Jiao ,&nbsp;Simeon C. Calvert ,&nbsp;Sander van Cranenburgh ,&nbsp;Hans van Lint","doi":"10.1016/j.amar.2024.100369","DOIUrl":"10.1016/j.amar.2024.100369","url":null,"abstract":"<div><div>Traffic conflict detection is essential for proactive road safety by identifying potential collisions before they occur. Existing methods rely on surrogate safety measures tailored to specific interactions (e.g., car-following, side-swiping, or path-crossing) and require varying thresholds in different traffic conditions. This variation leads to inconsistencies and limited adaptability of conflict detection in evolving traffic environments, particularly as the integration of autonomous driving systems adds complexity. Consequently, there is an increasing need for consistent detection of traffic conflicts across interaction contexts. To address this need, we propose a unified probabilistic approach in this study. The proposed approach establishes a unified framework of traffic conflict detection, where traffic conflicts are formulated as context-dependent extreme events of road user interactions. The detection of conflicts is then decomposed into a series of statistical learning tasks: representing interaction contexts, inferring proximity distributions, and assessing extreme collision risk. The unified formulation accommodates diverse hypotheses of traffic conflicts and the learning tasks enable data-driven analysis of factors such as motion states of road users, environment conditions, and participant characteristics. Jointly, this approach supports consistent and comprehensive evaluation of the collision risk emerging in road user interactions. We demonstrate the proposed approach by experiments using real-world trajectory data. A unified metric for indicating conflicts is first trained with lane-change interactions on German highways, and then compared with existing metrics using near-crash events from the U.S. 100-Car Naturalistic Driving Study. Our results show that the unified metric provides effective collision warnings, generalises across distinct datasets and traffic environments, covers a broad range of conflict types, and captures a long-tailed distribution of conflict intensity. In summary, this study provides an explainable and generalisable approach that enables traffic conflict detection across varying interaction contexts. The findings highlight its potential to enhance the safety assessment of traffic infrastructures and policies, improve collision warning systems for autonomous driving, and deepen the understanding of road user behaviour in safety–critical interactions.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"45 ","pages":"Article 100369"},"PeriodicalIF":12.5,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Econometric approaches to examine the onset and duration of temporal variations in pedestrian and bicyclist injury severity analysis","authors":"Natakorn Phuksuksakul ,&nbsp;Naveen Eluru ,&nbsp;Md. Mazharul Haque ,&nbsp;Shamsunnahar Yasmin","doi":"10.1016/j.amar.2024.100362","DOIUrl":"10.1016/j.amar.2024.100362","url":null,"abstract":"<div><div>There is considerable evidence in existing safety literature that the exogenous variable effects are likely to be time-varying in the injury severity analysis. The majority of these earlier studies tested time-varying effects of exogenous variables by crash year. However, there might be variability in the variable effects within a year, while the same effect might carry over in some or all parts of the preceding years. Towards that end, in this study, we propose a flexible framework to identify when the time-varying effect is likely to occur (the onset of temporal variation) and how long such time-varying effect lasts (duration of temporal variation) in the model estimates. In the study design, we assume that the onset of temporal variation can be any quarter of a year under consideration, while the time-varying effect can continue over different quarters after the onset of temporal variation in a variable effect. The injury severity model is estimated by using Correlated Random Parameter Generalized Ordered Logit formulation with piecewise linear functions. The empirical analysis is demonstrated by employing active traveler (pedestrian and bicyclist) crash data from Queensland, Australia for the years 2015 through 2020. The estimation results are further augmented by computing elasticity effects. The results indicate that the time-varying effects are likely to be different across years for several variables, while for other variables, the onset of time-varying effects could be different than the start of a year. Such flexibility in model specification is likely to have significant implications for devising and implementing effective countermeasures since it allows us to understand how road traffic injuries are evolving over time and when a new road safety issue might be arising.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"45 ","pages":"Article 100362"},"PeriodicalIF":12.5,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142699737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determinants influencing alcohol-related two-vehicle crash severity: A multivariate Bayesian hierarchical random parameters correlated outcomes logit model","authors":"Miaomiao Yang,&nbsp;Qiong Bao,&nbsp;Yongjun Shen,&nbsp;Qikai Qu,&nbsp;Rui Zhang,&nbsp;Tianyuan Han,&nbsp;Huansong Zhang","doi":"10.1016/j.amar.2024.100361","DOIUrl":"10.1016/j.amar.2024.100361","url":null,"abstract":"<div><div>Alcohol-related driving remains a significant concern due to its profound association with the likelihood of traffic crashes and the severity of resulting injuries, especially between two vehicles. To investigate the determinants influencing the alcohol-related two-vehicle crash severity, a foundational framework employed was a multinomial logit model. Meanwhile, by incorporating random intercept from individual case and vehicle levels to accommodate unobserved heterogeneity, and covariance matrices to underscore correlated outcomes, a multivariate hierarchical random parameters correlated outcomes logit model was proposed. Additionally, to further explore the potential temporal instability of explanatory variables, a random slope from a per-year indicator was introduced into the model. Crash data from the US Statewide Integrated Traffic Records System (SWITRS) database spanning from January 1, 2016, to December 31, 2021, was used. Three crash injury severity categories were examined, encompassing severe injury, minor injury, and no injury, with characteristics related to the driver, vehicle, road, environment, crash, and time serving as explanatory variables. The model results highlighted significant heterogeneity, with each case and vehicle accounting for 56.9% of the total variance for minor injuries and 50.8% for severe injuries. Furthermore, a significant negative correlation was explicitly exhibited between minor injury and severe injury outcomes at the case level. In terms of potential temporal instability, we provided per-year (2016–2019) parameter estimates and identified significant instability for indicators such as non-intersection, broadside and head-on collisions, cloudy weather conditions, and drivers who had been drinking but were not under the influence. Considering the impact of the COVID-19 pandemic, we divided the accident time into pre-COVID and during-COVID periods, modeling parameter estimates for both periods. This analysis revealed significant instability in several factors influenced by the pandemic. Additionally, noteworthy disparities in the estimated results of explanatory variables emerged in comparison to those general two-vehicle crashes or alcohol-related crashes, providing valuable insights. For instance, drivers who had been drinking but were not under the influence were less likely to sustain severe injuries, but the probability of minor injuries increased. These findings underscore the significance of thorough investigations into the determinants of injury severity in alcohol-impaired two-vehicle crash severity, along with the temporal instability of such factors. They hold important implications for effective traffic safety management and the formulation of prohibitive countermeasures.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"44 ","pages":"Article 100361"},"PeriodicalIF":12.5,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142322065","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":"Effects of sample size on pedestrian crash risk estimation from traffic conflicts using extreme value models","authors":"Faizan Nazir ,&nbsp;Yasir Ali ,&nbsp;Md Mazharul Haque","doi":"10.1016/j.amar.2024.100353","DOIUrl":"10.1016/j.amar.2024.100353","url":null,"abstract":"<div><div>Sample size plays a critical role in an Extreme Value Theory (EVT) model for estimating crash risks from traffic conflicts. Many studies have raised concerns regarding sample size and its consequent negative impact on the performance of EVT models. However, the effects of sample size on EVT models are not well-known, requiring an extensive investigation and a deeper understanding of the effects of sample size on model performance. Motivated by this research gap, this study proposes a systematic approach to examine the effects of sample size on EVT models aimed at estimating pedestrian crash risks from traffic conflicts. Ten smaller and homogeneous samples of traffic conflicts are derived from a total of 144 h of video data collected from three signalised intersections in Brisbane, Australia, whereby vehicle–pedestrian conflicts are measured by post encroachment time. To ensure that each subset contains equal data from three intersections, samples are formed using a uniform distribution, and their effects are tested using non-stationary Block Maxima and Peak Over Threshold models estimated in the Bayesian framework. Results show that the sample size influences the prediction of mean crash frequencies, confidence intervals, and relative errors. Although the effect of sample size is non-uniform, the model performance appears to improve with the increase in sample size, whereby the block maxima models show higher sensitivity towards sample size variation, and the peak over threshold models reveal relatively stable and better performance. Moreover, a comparison of sample size thresholds indicates that the peak over threshold approach is more cost-efficient than its counterpart. Overall, the findings of this study demonstrate that improper sample size can lead to poor predictability, low reliability, and large uncertainties.</div></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"44 ","pages":"Article 100353"},"PeriodicalIF":12.5,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}