Pub Date : 2025-02-12DOI: 10.1016/j.amar.2025.100373
Qinzhong Hou , Jinglun Zhuang , Chenrui Zhai , Xiaoyan Huo , Fred Mannering
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.
{"title":"A note on data segmentation, sample size, and model specification for crash injury severity modeling","authors":"Qinzhong Hou , Jinglun Zhuang , Chenrui Zhai , Xiaoyan Huo , 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}
Pub Date : 2025-01-23DOI: 10.1016/j.amar.2025.100371
Yunjie Ju , Shi Ye , Tiantian Chen , Guanyang Xing , Feng Chen
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.
{"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 , Shi Ye , Tiantian Chen , Guanyang Xing , 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}
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.
{"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 , Apostolos Ziakopoulos , Athanasios Theofilatos , 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}
Pub Date : 2025-01-15DOI: 10.1016/j.amar.2025.100370
Yuzhi Chen , Yuanchang Xie , Chen Wang , Liguo Yang , Nan Zheng , Lan Wu
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.
{"title":"Time-dependent effect of advanced driver assistance systems on driver behavior based on connected vehicle data","authors":"Yuzhi Chen , Yuanchang Xie , Chen Wang , Liguo Yang , Nan Zheng , 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}
Pub Date : 2024-12-20DOI: 10.1016/j.amar.2024.100369
Yiru Jiao , Simeon C. Calvert , Sander van Cranenburgh , Hans van Lint
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.
{"title":"A unified probabilistic approach to traffic conflict detection","authors":"Yiru Jiao , Simeon C. Calvert , Sander van Cranenburgh , 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}
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.
{"title":"Econometric approaches to examine the onset and duration of temporal variations in pedestrian and bicyclist injury severity analysis","authors":"Natakorn Phuksuksakul , Naveen Eluru , Md. Mazharul Haque , 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}
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.
{"title":"Determinants influencing alcohol-related two-vehicle crash severity: A multivariate Bayesian hierarchical random parameters correlated outcomes logit model","authors":"Miaomiao Yang, Qiong Bao, Yongjun Shen, Qikai Qu, Rui Zhang, Tianyuan Han, 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}
Pub Date : 2024-09-03DOI: 10.1016/j.amar.2024.100353
Faizan Nazir , Yasir Ali , Md Mazharul Haque
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.
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Pub Date : 2024-08-29DOI: 10.1016/j.amar.2024.100352
Depeng Niu , Tarek Sayed , Chuanyun Fu , Fred Mannering
Extreme Value Theory (EVT) models have recently gained increasing popularity for crash risk estimation using traffic conflict data. Extreme value modeling consists of two fundamental approaches: the block maxima approach and the peak-over-threshold approach, each with several variants. However, a comprehensive comparison of these two approaches and their variants in crash risk estimation is lacking. This study bridges this gap by comparing different extreme value modeling techniques and evaluating their performance in estimating crash frequencies. Within a non-stationary Bayesian hierarchical modeling framework, the analyzed models include the block maxima model, the largest order statistic model, and the peak-over-threshold model with the fixed and dynamic threshold, across univariate and bivariate traffic conflict cases. The analysis utilizes modified time-to-collision and post-encroachment time conflict indicator data collected from four signalized intersections in the City of Surrey, British Columbia, Canada. The results show that incorporating additional order statistics in the largest order statistic model improves predictive performance, particularly with limited extreme conflict samples. Moreover, employing the dynamic threshold within the peak-over-threshold model enhances model goodness-of-fit and yields more accurate crash frequency estimates compared to using the fixed threshold. While the performance of the block maxima and peak-over-threshold models varies with the selected conflict indicator in the univariate case, the bivariate peak-over-threshold model with the dynamic threshold exhibits superior overall prediction accuracy over the corresponding block maxima model. This is likely due to the effectiveness of the dynamic threshold in precisely identifying truly critical extreme conflicts.
极值理论（EVT）模型最近在利用交通冲突数据进行碰撞风险估算方面越来越受欢迎。极值模型包括两种基本方法：块状最大值方法和峰值超过阈值方法，每种方法都有几种变体。然而，目前还缺乏对这两种方法及其变体在碰撞风险估计中的应用进行全面比较。本研究通过比较不同的极值建模技术并评估其在估计碰撞频率方面的性能，弥补了这一空白。在非稳态贝叶斯分层建模框架内，所分析的模型包括块最大值模型、r 最大阶统计量模型，以及具有固定阈值和动态阈值的峰值超过阈值模型，适用于单变量和双变量交通冲突案例。分析利用了从加拿大不列颠哥伦比亚省萨里市四个信号灯路口收集的修改后碰撞时间和蚕食后时间冲突指标数据。结果表明，在 r 最大阶统计量模型中加入额外的阶统计量可提高预测性能，尤其是在极端冲突样本有限的情况下。此外，与使用固定阈值相比，在峰值超过阈值模型中使用动态阈值可提高模型拟合度，并获得更准确的碰撞频率估计值。虽然在单变量情况下，区块最大值模型和峰值超过阈值模型的性能随所选冲突指标的不同而变化，但采用动态阈值的双变量峰值超过阈值模型的总体预测准确性优于相应的区块最大值模型。这可能是由于动态阈值能有效地精确识别真正关键的极端冲突。
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The posted speed limit, as a proxy of actual speed, is one of the most fundamental predictors of active travelers’ (pedestrian and bicyclist) injury outcomes when involved in crashes with motor vehicles. Although earlier studies predominantly considered posted speed limit as an exogenous variable and provided highly insightful findings, majorities of them assume the effects of active traveler behavior to remain the same across different posted speed limits, which in turn neglect the heterogeneity in active traveler behaviors on high-speed roads vs. low-speed roads. This study proposes to develop a latent segmentation-based active traveler injury severity model to relax the homogeneity assumption of the posted speed limit by active traveler behavior. Specifically, this study proposes to estimate a latent segmentation-based correlated random parameters generalized ordered logit model to examine active travel injury severity mechanisms. The proposed model accommodates systematic heterogeneity in the effects of posted speed limit, crash year and active traveler group by using a piecewise linear function in injury severity component of the latent segment model. The model is demonstrated by using active traveler crash data from Queensland, Australia, for the years 2015 through 2019. To demonstrate the implications of the estimated models, a number of hypothetical scenario analyses are performed with a specific focus on active traveler behavior and reduction in posted speed limits. The outcomes from the hypothetical scenario analysis highlighted that a 76 % (73 %) reduction in active traveler fatalities can be achieved by converting 50–60 km/hr roadways to 10–40 km/hr roadways in the urban areas (rural areas) of Queensland. The outcomes of the study will allow us to identify effective speed management strategies while targeting those with high-risk behavior.
{"title":"The role of posted speed limit on pedestrian and bicycle injury severities: An investigation into systematic and unobserved heterogeneities","authors":"Natakorn Phuksuksakul , Mazharul Haque , Shamsunnahar Yasmin","doi":"10.1016/j.amar.2024.100351","DOIUrl":"10.1016/j.amar.2024.100351","url":null,"abstract":"<div><p>The posted speed limit, as a proxy of actual speed, is one of the most fundamental predictors of active travelers’ (pedestrian and bicyclist) injury outcomes when involved in crashes with motor vehicles. Although earlier studies predominantly considered posted speed limit as an exogenous variable and provided highly insightful findings, majorities of them assume the effects of active traveler behavior to remain the same across different posted speed limits, which in turn neglect the heterogeneity in active traveler behaviors on high-speed roads vs. low-speed roads. This study proposes to develop a latent segmentation-based active traveler injury severity model to relax the homogeneity assumption of the posted speed limit by active traveler behavior. Specifically, this study proposes to estimate a latent segmentation-based correlated random parameters generalized ordered logit model to examine active travel injury severity mechanisms. The proposed model accommodates systematic heterogeneity in the effects of posted speed limit, crash year and active traveler group by using a piecewise linear function in injury severity component of the latent segment model. The model is demonstrated by using active traveler crash data from Queensland, Australia, for the years 2015 through 2019. To demonstrate the implications of the estimated models, a number of hypothetical scenario analyses are performed with a specific focus on active traveler behavior and reduction in posted speed limits. The outcomes from the hypothetical scenario analysis highlighted that a 76 % (73 %) reduction in active traveler fatalities can be achieved by converting 50–60 km/hr roadways to 10–40 km/hr roadways in the urban areas (rural areas) of Queensland. The outcomes of the study will allow us to identify effective speed management strategies while targeting those with high-risk behavior.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"44 ","pages":"Article 100351"},"PeriodicalIF":12.5,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213665724000356/pdfft?md5=1107e410ee25ebd24c74e3af72ce8cc7&pid=1-s2.0-S2213665724000356-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142122150","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}
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