Analytic Methods in Accident Research最新文献

{"title":"Temporal stability of the impact of factors determining drivers’ injury severities across traffic barrier crashes in mountainous regions","authors":"Dongdong Song , Xiaobao Yang , Panagiotis Ch. Anastasopoulos , Xingshui Zu , Xianfei Yue , Yitao Yang","doi":"10.1016/j.amar.2023.100282","DOIUrl":"10.1016/j.amar.2023.100282","url":null,"abstract":"<div><p>Traffic barrier crashes have been a major concern in many prior studies in traffic safety literature, especially in the crash-prone sections of mountainous regions. However, the effect of factors affecting the injury-severities resulting from crashes involving different types of traffic barriers may be different. This paper provides an empirical assessment of the performance of ordered and unordered discrete outcome models for examining the impact of exogenous factors determining the driver injury-severity of crashes involving two types of traffic barriers in mountainous regions: w-beam barriers and cable barriers. For the ordered framework, the alternative modeling approaches include: the generalized ordered logit (GOL) and the random thresholds random parameters generalized ordered logit model (RTRPGOL). Whereas, for the unordered framework, the alternative modeling approaches include: the multinomial logit (MNL), the random parameters multinormal logit (RPL), and the random parameters multinormal logit model with heterogeneity in the means and variances (RPLHMV). Using injury-severity data from 2016 to 2019 for mountainous regions in Guiyang City, China, three injury-severity categories are determined as outcome variables: severe injury (SI), minor injury (MI), and no injury (NI), while the potential influencing factors including drivers-, vehicles-, road-, and environment-specific characteristics are statistically analyzed. The model estimation results show: (a) that the MNL model statistically outperforms the GOL model in terms of goodness-of-fit measures; (b) the RTRPGOL model is statistically superior to the MNL and RPL models; and (c) the RPLHMV model is statistically superior to the RTRPGOL model, and therefore the preferred option among the model alternatives. To that end, the RPLHMV model is leveraged to quantitatively describe the impact of explanatory variables on the driver injury-severity and explore how these factors change over the years (between 2016–2017 and 2018–2019). The results further show that the factors affecting driver injury severities and the effects of significant factors on injury severity probabilities change across traffic barrier crash models and across years. In addition, the results of the temporal effects analysis show that some variables present relative temporal stability, which is important for formulating long-term strategies to enhance traffic safety on mountainous roads. Most importantly, the effects of the explanatory factors that exhibit relative temporal stability are found to vary across traffic barrier crashes. For example, trucks, daylight, curved section segments, and high-speed limit (greater than 55 mph) are some of the factors that have opposite effects between traffic barrier crash models. The findings from this paper are expected to help policy makers to take necessary measures in reducing traffic barrier crashes in mountainous regions by forming appropriate strategies, and by alloca","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"39 ","pages":"Article 100282"},"PeriodicalIF":12.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47517258","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":"Modelling the response times of mobile phone distracted young drivers: A hybrid approach of decision tree and random parameters duration model","authors":"Yasir Ali ,&nbsp;Md Mazharul Haque","doi":"10.1016/j.amar.2023.100279","DOIUrl":"https://doi.org/10.1016/j.amar.2023.100279","url":null,"abstract":"<div><p>Research has shown the detrimental effects of using mobile phones whilst driving, which are more prominent and concerning for young drivers, who are often less experienced and riskier. As such, this study investigates young drivers’ response times when they encounter a safety–critical event on a suburban road whilst using a mobile phone. To collect high-quality trajectory data, the CARRS-Q advanced driving simulator was used. Thirty-two licenced young drivers were exposed to the sudden braking of the lead vehicle in their lane in three driving conditions: baseline (no phone conversation), handheld, and hands-free. Unlike extant studies, this paper proposes a hybrid modelling framework for the response times of distracted drivers. This framework combines a decision tree model and a correlated grouped random parameters duration model with heterogeneity-in-means. While the decision tree model identifies a <em>priori</em> relationship among main effects, the random parameter model captures unobserved heterogeneity and correlation between random parameters. The modelling results reveal that mobile phone distraction impairs response time behaviour for the majority of drivers. However, some drivers tend to respond earlier whilst being distracted, suggesting that the perceived risk of mobile use might have led to an early response, indicating their risk compensation behaviour. Female drivers tend to respond earlier compared to male drivers, indicating their safer and risk-averse behaviour. Overall, mobile phone distraction appears to deteriorate response time behaviour and poses a significant safety concern to drivers and the overall traffic stream unless mitigated.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"39 ","pages":"Article 100279"},"PeriodicalIF":12.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49713228","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 econometric framework for integrating aggregate and disaggregate level crash analysis","authors":"Shahrior Pervaz,&nbsp;Tanmoy Bhowmik,&nbsp;Naveen Eluru","doi":"10.1016/j.amar.2023.100280","DOIUrl":"10.1016/j.amar.2023.100280","url":null,"abstract":"<div><p>Traditionally, aggregate crash frequency by severity and disaggregate severity analysis have been conducted independently in the safety literature. The current research effort contributes to the safety literature by bridging the gap between these two different streams of research by using both aggregate and disaggregate level crash data simultaneously. To be specific, the study proposes a framework that integrates aggregate and disaggregate level models. The proposed framework allows for the influence of independent variables at the crash record level to be incorporated within the aggregate level propensity estimation. The empirical analysis is based on the crash data drawn from the city of Orlando, Florida for the year 2019. The disaggregate level analysis uses 20,204 crash records that contain crash specific variables, temporal characteristics, roadway, vehicle and driver factors, road environmental and weather information for each record. For aggregate level model analysis, the study aggregated the crash records by severity class over 300 traffic analysis zones. An exhaustive set of independent variables including roadway and traffic factors, land-use attributes, built environment, and sociodemographic characteristics are considered in this analysis. The empirical analysis is further augmented by employing several goodness of fit and predictive measures. A validation exercise is also performed using a holdout sample to highlight the superior performance of the proposed integrated model relative to the non-integrated crash count by severity model. The proposed model can also accommodate common unobserved spatial correlation among crash records within the same zone. The model results illustrate the benefits of developing an integrated model system for crash frequency and severity.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"39 ","pages":"Article 100280"},"PeriodicalIF":12.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45453650","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":"Assessing traffic conflict/crash relationships with extreme value theory: Recent developments and future directions for connected and autonomous vehicle and highway safety research","authors":"Yasir Ali ,&nbsp;Md Mazharul Haque ,&nbsp;Fred Mannering","doi":"10.1016/j.amar.2023.100276","DOIUrl":"10.1016/j.amar.2023.100276","url":null,"abstract":"<div><p>With proactive safety assessment gaining significant attention in the literature, the relationship between traffic conflicts (which form the underpinnings of proactive safety measures) and observed crashes remains a critical research need. Such a need will grow significantly with the ongoing introduction of connected and autonomous vehicles where software and hardware improvements are likely to be determined from observed traffic conflict data as opposed to data from accumulated crashes. Extreme value theory has been applied for over two decades to study the relationship between traffic conflicts and crashes. While several advancements have been made in extreme value theory models over time, the need to continually evaluate the strengths and weaknesses of these models remains, particularly considering their likely use in improving the safety–critical elements of connected and autonomous vehicles. This paper seeks to comprehensively review studies on extreme value theory applications in traffic conflict/crash contexts by providing an in-depth assessment of alternate modelling methodologies and associated issues. Critical research needs relating to the further development of extreme value theory models are identified and include identifying efficient techniques for sampling extremes, determining optimal sample size, assessing and selecting appropriate traffic conflict measures, incorporating covariates, accounting for unobserved heterogeneity, and addressing issues associated with real-time estimations.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"39 ","pages":"Article 100276"},"PeriodicalIF":12.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41582796","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":"Car-following crash risk analysis in a connected environment: A Bayesian non-stationary generalised extreme value model","authors":"Faizan Nazir ,&nbsp;Yasir Ali ,&nbsp;Anshuman Sharma ,&nbsp;Zuduo Zheng ,&nbsp;Md Mazharul Haque","doi":"10.1016/j.amar.2023.100278","DOIUrl":"10.1016/j.amar.2023.100278","url":null,"abstract":"<div><p>A connected environment provides driving aids to assist drivers in decision-making and aims to make driving manoeuvres safer by minimising uncertainty associated with decisions. The role of a connected environment becomes vital for car-following manoeuvres in a safety–critical event, whereby drivers follow a lead vehicle, and if timely action is not taken, it is likely to lead to a rear-end collision. Moreover, how different drivers perceive and react to the same information needs to be explored to understand the differential effects of a connected environment on car-following behaviour. As such, this study investigated the effects of the traditional and connected environments on car-following crash risk using traffic conflict techniques. Data were collected using the CARRS-Q advanced driving simulator, whereby 78 participants performed a car-following task in two randomised driving conditions: baseline (without driving aids) and connected environment (with driving aids). The safety–critical event in the car-following scenario was the leader’s hard braking, for which participants received advance information, besides several other driving aids. Modified time-to-collision was used as the traffic conflict measure for characterising rear-end crash risk and modelled using a generalised extreme value (GEV) model in the Bayesian framework. This model incorporated driving-related factors and driver demographics to address the non-stationarity issue of traffic extremes. Results reveal that the car-following crash risk is significantly reduced in the connected environment. Further, using the developed model, separate GEV distributions were estimated for each individual driver, providing insights into the heterogeneous effects of the connected environment on crash risk. The developed model was employed to understand the crash risk across different driver characteristics, and results suggest that crash risk decreases for all age groups and gender, with the maximum safety benefits obtained by young and female drivers. The findings of this study shed light on the efficacy of the connected environment in improving car-following behaviour and drivers’ ability to make safer decisions.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"39 ","pages":"Article 100278"},"PeriodicalIF":12.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46584217","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":"Analysis of duration between crashes using a hazard-based duration approach with heterogeneity in means and variances: Some new evidence","authors":"Mohammad M. Hamed,&nbsp;Ahmad AlShaer","doi":"10.1016/j.amar.2023.100283","DOIUrl":"10.1016/j.amar.2023.100283","url":null,"abstract":"<div><p>This paper provides new evidence for the factors underlying crash involvement by modeling the time duration between crashes for drivers involved in one or more crashes between 2016 and 2020. Several random parameter hazard-based duration models with heterogeneous means and variances are presented. Among this study’s other findings, the results show that male drivers had a higher risk of being involved in one crash than female drivers (among drivers involved in only one crash). Female drivers were more likely to be involved in higher-order crashes however. Among female drivers involved in only one crash, millennials had the highest crash risk. However, baby boomers and Gen Z drivers had a greater risk of being involved in a crash than millennials or Gen X drivers, whether male or female. The analysis presents evidence for distinct crash risk patterns in men and women and among different age groups. The lagged duration dependence indicates that the longer the time from a previous crash, the sooner the driver will be involved in their next crash. In addition, the lagged duration dependence suggests two types of dependencies. The first is profound dependency. Drivers with this type of dependency tended to be tier-three male millennials, tier-three Gen X drivers, tier-three Gen Z drivers, or tier-four male millennials. The second is shallow dependency. Drivers with this type of dependency tended to be tier-three female millennials, tier-four male Gen X drivers, and tier-five male millennials. The likelihood of a crash was almost independent of the time that had transpired without a crash for those involved in more than one crash. Estimation results also revealed that crash survivors showed different subsequent behavior. Surviving a severe crash and experiencing crashes involving multiple vehicles may lead to hazardous habituation among male millennials. Moreover, many drivers seemed to alter their behavior after the first crash, particularly male and female drivers involved in one crash only. Other drivers did not show any behavioral changes, including tier-three female millennials, tier-four male Gen X, and tier-five male millennials, who had a shallow lagged dependency, and their likelihood of a crash was almost independent of the time that transpired without a crash.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"39 ","pages":"Article 100283"},"PeriodicalIF":12.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45733299","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 heterogeneity has been examined in transportation safety analysis: A review of latent class modeling applications","authors":"Sung Hoo Kim","doi":"10.1016/j.amar.2023.100292","DOIUrl":"10.1016/j.amar.2023.100292","url":null,"abstract":"<div><p>This study explores how heterogeneity has been examined in transportation safety analyses, specifically focusing on latent class modeling, which has gained popularity and has successfully captured unobserved heterogeneity. The study firstly identifies a large volume of relevant papers in the safety analysis domain and analyzes how models have been used by focusing on key elements of the latent class model (along with the proposed typology of segmentation-based heterogeneity models). In the literature, various class-specific outcome models have been used. They are determined by the type of outcome variable and are also highly associated with the analysis context. For example, crash severity and crash likelihood/frequency analyses are the main applications where crash severity is often treated as binary, nominal, or ordered, whereas crash likelihood/frequency is subject to count data or survival data modeling. The study reviews the number of classes selected in empirical applications and how they were determined. It is found that in safety analyses, it is more common to choose the number of classes based on the judgement of the analyst than quantitative measures (e.g., BIC). This implies that we value interpretability of the latent class model and solutions with many classes (i.e., greater model complexity, many parameters) often hinder the interpretation of models. This paper also covers further discussions about heterogeneity including model comparisons (homogeneity models versus latent class models and random parameters versus latent class models), modeling intra-class heterogeneity, possible alternative model specifications that have been rarely used in the literature, and issues related to temporal instability.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"40 ","pages":"Article 100292"},"PeriodicalIF":12.9,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46794557","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":"Modelling the continuum of serious traffic injuries in police-hospital linked data by applying the random parameters hazard-based duration model","authors":"Khalid Alzaffin ,&nbsp;Sherrie-Anne Kaye ,&nbsp;Angela Watson ,&nbsp;Md Mazharul Haque","doi":"10.1016/j.amar.2023.100291","DOIUrl":"10.1016/j.amar.2023.100291","url":null,"abstract":"<div><p>Injury severity in police crash reports is usually recorded in three to five classes, including property damage, slight, moderate, serious, and fatal injuries. Among these classifications, serious injuries are commonly classified as cases where a road user is admitted to a hospital. In this classification, the length of hospital stay is not differentiated, whether one day or ten days, as long as the road user has been admitted to the hospital. As such, the inferences drawn from assuming that all serious injuries (1 if a road user is admitted to the hospital; 0 otherwise) are at the same severity level inherently suffer from aggregation bias and may not provide a thorough understanding of this severity category. This study proposes a hazard-based duration modelling approach to examine the severity of serious injury crashes measured in a continuous spectrum. Specifically, using the length of hospital stay as the measure of serious injuries, a random parameters hazard-based duration model with heterogeneity in means was applied to model serious injury crashes obtained by linking crash records in police and hospital databases. To address temporal instability, the injury records sources from Abu Dhabi, United Arab Emirates (UAE), between 2015 and 2019 were modelled separately for each year. The results showed that factors positively associated with more serious injury severity (prolonged length of hospital stay) are rural areas, high posted speed limits of 100–160 km/h, overturned crashes, speeding, impaired driving, involvements of a heavy vehicle, nighttime crashes, lack of restraint usage, and injuries to the head or lower extremities. In particular, speeding violations during nighttime are positively associated with more serious injuries. Furthermore, the means of the random parameters of head injury are positively influenced by speeding, lack of restraint usage, and motorcycle involvement through the heterogeneity-in-mean specification of the hazard-based duration model. The proposed modelling approach to model serious traffic injuries using a hazard-based duration model provides a comprehensive understanding of the factors associated with serious injuries.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"40 ","pages":"Article 100291"},"PeriodicalIF":12.9,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41576855","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":"Modelling speed reduction behaviour on variable speed limit-controlled highways considering surrounding traffic pressure: A random parameters duration modelling approach","authors":"Yasir Ali ,&nbsp;Mark P.H. Raadsen ,&nbsp;Michiel C.J. Bliemer","doi":"10.1016/j.amar.2023.100290","DOIUrl":"10.1016/j.amar.2023.100290","url":null,"abstract":"<div><p>Variable speed limits are frequently used to improve traffic safety and harmonise traffic flow. This study investigates how, and to what extent, drivers reduce their speed upon passing a variable speed limit sign. We specifically consider the impact on braking behaviour due to the systematic inclusion of different social pressures exerted by surrounding traffic. This social pressure is the natural result of having two vehicle cohorts created by a change in the variable speed limit (the new speed limit being higher than the original). The cohort with the higher speed limit overtakes vehicles with the lower speed limit, instigating a specific passing rate on drivers in the lower speed cohort. A driving simulator study is employed to obtain individual driver data whilst being able to systematically change the social pressure applied. A sample comprising sixty-seven participants conducted multiple randomised drives, with varying passing rates from as low as 90 veh/h to as high as 360 veh/h. The speed reduction behaviour of the participants is modelled using a <em>random parameter duration modelling approach</em>. Both the panel nature of the data and unobserved heterogeneity are captured through a <em>correlated grouped random parameters with heterogeneity-in-the-mean</em> model. The random parameters are predicated on the different passing rate scenarios, allowing drivers to take shorter or longer to reduce their speeds compared to the reference passing rate. It is shown that the extent of social pressure impacts braking behaviour and therefore affects safety measures, which is a function of the magnitude of the speed limit change. In addition, an extensive decision tree analysis is conducted to understand differential braking behaviour. Results reveal that, on average, female drivers take a shorter time to reduce their speed under a high passing rate but longer in a low passing rate scenario compared to males. Similarly, young drivers are found to take longer to reduce their speeds in a high passing rate scenario compared to other age groups. Our main findings indicate that the within-cohort safety is lowest under low passing rates due to comparatively larger speed differences between drivers. Yet, under a high passing rate, we observe an increase in violation of the speed limit by the lower speed limit vehicles (but less within cohort speed differences). Whilst normally this would be an undesired effect across cohorts, this violation is argued to lead to increased safety due to the smaller discrepancy in speed.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"40 ","pages":"Article 100290"},"PeriodicalIF":12.9,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48974602","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":"Temporal instability and age differences of determinants affecting injury severities in nighttime crashes","authors":"Xintong Yan,&nbsp;Jie He,&nbsp;Changjian Zhang,&nbsp;Chenwei Wang,&nbsp;Yuntao Ye,&nbsp;Pengcheng Qin","doi":"10.1016/j.amar.2023.100268","DOIUrl":"10.1016/j.amar.2023.100268","url":null,"abstract":"<div><p><span>Driving at nighttime may make drivers more likely to be involved in fatal crashes. To investigate the temporal instability and age differences of contributors determining different injury severity levels in nighttime crashes, this paper estimates three groups of random parameters logit models with heterogeneity in the means and variances (young/middle-age/old groups). Nighttime single-vehicle crashes in this study are gathered over four years in California, from January 1, 2014, to December 31, 2017, provided by Highway Safety Information System, including single-vehicle crashes occurring under dark, dawn, and dusk lighting conditions. Simultaneously, to investigate the temporal instability and transferability of nighttime crash severity relating to drivers of different ages, three disaggregate groups are defined: young drivers (15–29 years old), middle-age drivers (30–49 years old), old drivers (over 49 years old). Three injury-severity categories are determined as outcome variables: severe injury, minor injury, and no injury, while multiple factors are investigated as explanatory variables, including driver characteristics, vehicle characteristics, roadway characteristics, environmental characteristics, crash characteristics, and temporal characteristics. Two series of </span>likelihood ratio tests are undertaken to unveil the contributors determining nighttime crash injury severities varying among drivers of different ages over time. Besides, the current study also compares the differences between out-of-sample and within-sample predictions. The results indicate the unstable direction of predictions across different age groups over time and underscore the necessity to adequately accommodate the temporal instability and age differences in accident prediction. More studies can be conducted to accommodate the self-selectivity issue and the out-of-sample prediction differences between using the parametric models and non-parametric models.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":"38 ","pages":"Article 100268"},"PeriodicalIF":12.9,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42506567","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}