Analytic Methods in Accident Research最新文献

{"title":"Unveiling the determinants of injury severities across age groups and time: A deep dive into the unobserved heterogeneity among pedestrian crashes","authors":"Qingli Liu,&nbsp;Fan Li,&nbsp;Kam K.H. Ng","doi":"10.1016/j.amar.2024.100336","DOIUrl":"10.1016/j.amar.2024.100336","url":null,"abstract":"<div><p>Pedestrians, particularly susceptible to road traffic crashes, experience varying injury severities influenced by age and time shifts. This research aims to investigate the differences and temporal shifts in factors influencing pedestrian injury severities across different age groups. To achieve this, three random parameters binary logit models with heterogeneity in the means (and variances) were employed. Four years of pedestrian crash data in Hong Kong were utilized in this study. According to United Nations’ definitions of the young and elderly, pedestrians were categorized into three groups: young (under 25 years old), middle-aged (25–65 years old), and elderly (over 65 years old). Initial likelihood ratio tests indicated temporal stability in the young group between 2019 and 2021, with further tests confirming age transferability and overall temporal stability after integrating the three years of young data. The partially constrained temporal stability approach was then developed to further capture the temporal stability of individual variables and simplify model results. Model results identified factors impacting pedestrian injury severities, encompassing pedestrian, driver, vehicle, temporal, and light condition characteristics. Some contributing variables exhibit age-transferability or temporal stability, such as controlled crossing, near controlled crossing, inattentive driver and private car. However, the significance of most contributors varies across age groups and years, with certain factors being age-specific or year-specific. Out-of-sample predictions underscore the cumulative likelihood of fatal or severe injuries with advancing age, and the middle-aged models showed the highest level of temporal stability regarding the risk of injury severity compared to the other two age models. Moreover, middle-aged pedestrians in Hong Kong faced the highest risk of fatal or severe injuries during the first year of the COVID-19 lockdown (2020), but the risk significantly declined for pedestrians of all age groups in the subsequent year. Based on these findings, targeted preventive measures that take into account age differences have been proposed to effectively enhance pedestrian safety.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141134305","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 systematic unified approach for addressing temporal instability in road safety analysis","authors":"Kazi Redwan Shabab ,&nbsp;Tanmoy Bhowmik ,&nbsp;Mohamed H. Zaki ,&nbsp;Naveen Eluru","doi":"10.1016/j.amar.2024.100335","DOIUrl":"https://doi.org/10.1016/j.amar.2024.100335","url":null,"abstract":"<div><p>Multivariate models are widely employed for crash frequency analysis in traffic safety literature. In the context of analyzing data for multiple instances (such as years), it becomes essential to evaluate the stability of parameters over time. The current research proposes a novel approach, labelled the mixed spline indicator pooled model, that offers significant enhancement relative to current approaches employed for capturing temporal instability. The proposed approach entails carefully creating independent variables that allow us to measure parameter slope changes over time and can be easily integrated into existing methodological frameworks. The current research effort compares four multivariate model systems: year specific negative binomial model, year indicator pooled model, spline indicator pooled model, and mixed spline indicator pooled model. The model performance is compared using log-likelihood and Bayesian Information Criterion. The empirical analysis is conducted using the Traffic Analysis Zone (TAZ) level crash severity records from Central Florida for the years from 2011 to 2019. The comparison results indicate that the proposed mixed spline indicator pooled model outperforms the other models providing superior data fit while optimizing the number of parameters. The proposed mixed spline model can allow a piece-wise linear functional form for the parameter and is suitable to forecast crashes for future years as illustrated in our predictive performance analysis.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141083493","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":"Pedestrian injury severities resulting from vehicle/pedestrian intersection crashes: An assessment of COVID-contributing temporal shifts","authors":"Natalia Barbour ,&nbsp;Mohamed Abdel-Aty ,&nbsp;Samgyu Yang ,&nbsp;Fred Mannering","doi":"10.1016/j.amar.2024.100334","DOIUrl":"10.1016/j.amar.2024.100334","url":null,"abstract":"<div><p>Pedestrian mobility has become an increasingly important concern in transportation system analysis because of its positive impacts on the environment and healthy lifestyles. However, pedestrian safety in a vehicle-dominated transportation network remains a concern and potential barrier to pedestrian mobility, with pedestrian intersection safety being of particular concern. In addition, it is important to understand how pedestrian safety has been affected by the COVID-19 pandemic, perhaps permanently shifting pedestrian injury risks. This research seeks to provide insight into how pedestrian injury risks at intersections have changed as a result of the pandemic by estimating a series pedestrian injury severity models. To do so, unconstrained and partially constrained random parameters multinomial logit models with heterogeneity in the means of random parameters were estimated. Using Florida data, two one-year periods (one year before and one year after the COVID-19 pandemic) were defined based on vehicle miles traveled. The sample includes 3,780 single pedestrian-single vehicle crashes (2,348 from daytime and 1,432 from nighttime). A broad range of variables was considered to assess how the parameters may have shifted between the before and after periods. A series of likelihood ratio tests were conducted to examine the stability of model parameter estimates across the predefined time periods as well as to determine the differences between the daytime and nighttime crash injury severity outcomes. The results show that the nighttime crashes experienced more temporal shifts relative to daytime crashes. The findings also showed that both pedestrian and driver behavior played key temporally-shifting roles before and after the COVID-19 pandemic period. Finally, the out-of-sample simulations suggest that pedestrian injuries have become more severe after the pandemic.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140773044","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":"Modeling the risk of single-vehicle run-off-road crashes on horizontal curves using connected vehicle data","authors":"Yuzhi Chen ,&nbsp;Chen Wang ,&nbsp;Yuanchang Xie","doi":"10.1016/j.amar.2024.100333","DOIUrl":"10.1016/j.amar.2024.100333","url":null,"abstract":"<div><p>Crash risk measures (CRMs) are widely used in safety analysis to complement crash reports. However, none of the existing CRMs are specifically developed for modeling the risk of single-vehicle run-off-road (SVROR) crashes, especially those on horizontal curves. This paper proposes a novel crash risk measure for modeling SVROR crash risk using connected vehicle data. The proposed SVROR crash risk measure (SVROR-CRM) is based on the concept of tetraquark in particle physics. It utilizes the adjusted position deviation risk force (<span><math><mrow><msubsup><mi>F</mi><mrow><mi>posi</mi></mrow><mrow><mi>risk</mi></mrow></msubsup></mrow></math></span>) and adjusted attitude deviation risk moment (<span><math><mrow><msubsup><mi>Γ</mi><mrow><mi>atti</mi></mrow><mrow><mi>risk</mi></mrow></msubsup></mrow></math></span>) to quantify SVROR crash risk. The SVROR crash risk is then estimated by the joint probability of <span><math><mrow><msubsup><mi>F</mi><mrow><mi>posi</mi></mrow><mrow><mi>risk</mi></mrow></msubsup></mrow></math></span> and <span><math><mrow><msubsup><mi>Γ</mi><mrow><mi>atti</mi></mrow><mrow><mi>risk</mi></mrow></msubsup></mrow></math></span> using a peak-over threshold approach. The risk threshold is automatically determined via a mean absolute error function. The SVROR-CRM is validated using connected vehicle and crash data from sixteen curves on Interstate 80 in Wyoming. The results suggest that the estimated SVROR crash risks well match historical crash records. Also, it is found that attitude deviation poses a higher risk of SVROR crash than position deviation on horizontal curves. Therefore, it is critical for drivers to steer properly on curves to minimize SVROR crash risks. The proposed approach bridges an important gap in crash risk measure research and can be used to estimate SVROR crash risk and identify unsafe trajectories and high-crash locations and/or periods on highway horizontal curves.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140772166","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":"Investigating autonomous vehicle discretionary lane-changing execution behaviour: Similarities, differences, and insights from Waymo dataset","authors":"Yasir Ali ,&nbsp;Anshuman Sharma ,&nbsp;Danjue Chen","doi":"10.1016/j.amar.2024.100332","DOIUrl":"https://doi.org/10.1016/j.amar.2024.100332","url":null,"abstract":"<div><p>Recently released autonomous vehicle datasets like Waymo can provide rich information (and unprecedented opportunities) to investigate lane-changing behaviour of autonomous vehicles, requiring data from multiple drivers and lanes with different objectives. As such, the study investigates the discretionary lane-changing execution behaviour of autonomous vehicles and compares its behaviour with human-driven vehicles from Waymo and Next Generation Simulation (NGSIM) datasets. Several behavioural factors are statistically analysed and compared, whereas the discretionary lane-changing execution time (or duration) is modelled by a random parameters hazard-based duration modelling approach, which accounts for unobserved heterogeneity. Descriptive analyses suggest that autonomous vehicles maintain larger lead and lag gaps, longer discretionary lane-changing execution time, and lower acceleration variation than human-driven vehicles. The random parameters duration model reveals heterogeneity in discretionary lane-changing execution behaviour, which is higher in human-driven vehicles but decreases significantly for autonomous vehicles. Whilst contradictory to a general hypothesis in the literature that autonomous vehicles will eliminate heterogeneity, our finding indicates that heterogeneous behaviour also exists in autonomous vehicles (although to a lesser extent than in human-driven vehicles), which can be contextual to prevailing traffic conditions. Overall, autonomous vehicles show safer discretionary lane-changing behaviour compared to human-driven vehicles.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140547295","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":"Estimating crash risk and injury severity considering multiple traffic conflict and crash types: A bivariate extreme value approach","authors":"Md Mohasin Howlader ,&nbsp;Fred Mannering ,&nbsp;Md Mazharul Haque","doi":"10.1016/j.amar.2024.100331","DOIUrl":"https://doi.org/10.1016/j.amar.2024.100331","url":null,"abstract":"<div><p>Traffic conflicts are generally considered independent events in existing extreme value theory models to estimate the risk of total or single types of crashes. However, traffic events at a road entity are not necessarily independent interactions and can lead to multiple traffic conflicts with shared common unobserved factors. A comprehensive estimation of crash risks in a road entity needs to consider the correlation of potential traffic conflicts to avoid possible bias in prediction performance and the problem of undetected deficiencies. This study proposes a Bayesian non-stationary bivariate generalised extreme value modelling framework to estimate the severe and non-severe crash risks accounting for the correlation between right-turn and rear-end conflicts at signalised intersections. A deep neural network-based computer vision technique was applied to extract the traffic conflicts from 77 h of video recordings over two right-turn approaches at two signalised intersections in Cairns, Australia. Post encroachment time and modified time to collision were used to characterise right-turn and rear-end conflicts, respectively, while an expected post-collision velocity difference was combined with post encroachment time and modified time to collision for crash risk estimation by injury severity levels. Several covariates were used to address the time-varying heterogeneity of traffic conflict extremes and to estimate the differential crash risks at signal cycles. Results showed a significant correlation between right-turn and rear-end crashes at signal cycle levels, indicating the importance of accounting for the dependency among traffic conflict types. Overall, the bivariate models considering the correlation among traffic conflict types were found to understandably perform better than their univariate counterparts. This study provides a demonstration of a correlated crash risk modelling framework that addresses issues related to the suitable traffic conflict measures, time varying risks (non-stationarity), heterogeneity, and injury severity levels of different crash types.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2213665724000150/pdfft?md5=6bc905ca260e0b524a0447807f24d14f&pid=1-s2.0-S2213665724000150-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140309791","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":"An error components mixed logit with heterogeneity in means and variance for fixed object occupant severity outcomes","authors":"Rohan Shrestha,&nbsp;Lan Ventura,&nbsp;Narayan Venkataraman,&nbsp;Venkataraman Shankar","doi":"10.1016/j.amar.2024.100330","DOIUrl":"https://doi.org/10.1016/j.amar.2024.100330","url":null,"abstract":"<div><p>This paper presents an error components mixed logit with heterogeneity in means and variance to capture the heterogeneous effects of contributing factors on fixed object occupant severity. One year (2021) of crash data on fixed object related crashes in Lubbock County, Texas was analyzed with fixed object details extracted from crash narratives and classified into 11 groupings. Crash data included any fixed object collision occurring at any point in the sequence of crash events (not exclusive to the first harmful event). The random parameters were identified as indicators for occupant involvement in the first harmful crash sequence event, with that event being collision with a fixed object, for possible injury and injury severity outcomes. Heterogeneity in the means of these random parameters was found with respect to six different indicator variables. Additionally, heterogeneity in the variance of the injury random parameter was found with respect to two different indicator variables. Inclusion of two error component nests improved prediction accuracy at the observation level for higher severity outcomes. The findings in this study suggest that fixed object classification types should be explored further in relation to heterogeneous effects on occupant severity outcomes. Furthermore, the findings also highlight the applicability of an error components mixed logit model for severity analysis.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140191624","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 integrated multi-resolution framework for jointly estimating crash type and crash severity","authors":"Shahrior Pervaz ,&nbsp;Tanmoy Bhowmik ,&nbsp;Naveen Eluru","doi":"10.1016/j.amar.2024.100321","DOIUrl":"https://doi.org/10.1016/j.amar.2024.100321","url":null,"abstract":"<div><p>The current research effort contributes to safety literature by developing an integrated framework that allows for the influence of independent variables from crash type and severity components at the disaggregate level to be incorporated within the aggregate level propensity to estimate crash frequency by crash type and severity. 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 15,518 crash records of three crash types including rear end, angular and sideswipe. Each crash record contains crash specific factors, driver and vehicle factors, roadway attributes, road environmental and weather information. For aggregate level model analysis, the study aggregates the crash records by crash type over 300 traffic analysis zones. An exhaustive set of independent variables including roadway and traffic characteristics, land-use attributes, built environment and sociodemographic factors are considered in this level. The empirical analysis is further augmented by employing several goodness of fit and predictive measures. A validation exercise is also conducted using a holdout sample to highlight the superiority of the proposed integrated model relative to the non-integrated model system. The proposed framework can also incorporate unobserved heterogeneity in the model system. The findings of the study indicate that the proposed framework is advantageous for capturing the variable effects simultaneously across the aggregate and disaggregate levels.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140191623","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 non-motorist safety in motor vehicle crashes – a copula-based approach to jointly estimate crash location type and injury severity","authors":"Robert Marcoux,&nbsp;Shahrior Pervaz,&nbsp;Naveen Eluru","doi":"10.1016/j.amar.2024.100322","DOIUrl":"https://doi.org/10.1016/j.amar.2024.100322","url":null,"abstract":"<div><p>Non-motorist injury severity can be affected by various observed and unobserved attributes related to the crash location type (segment or intersection). Recognizing the distinct non-motorist injury severity profiles by crash location type, we propose a joint modeling framework to study crash location type and non-motorist injury severity as two dimensions of the severity process. We employ a copula-based joint framework that ties the crash location type (represented as a binary logit model) and injury severity (represented as a generalized ordered logit model) through a closed form flexible dependency structure to study the injury severity process. The proposed approach also accommodates the potential heterogeneity (across non-motorists) in the dependency structure. The data for our analysis is drawn from the Central Florida region for the years of 2015 to 2021. The model system explicitly accounts for temporal heterogeneity across the two dimensions. A comprehensive set of independent variables including non-motorist user characteristics, driver and vehicle characteristics, roadway attributes, weather and environmental factors, temporal and socio-demographic factors are considered for the analysis. We also conducted an elasticity analysis to show the actual magnitude of the independent variables on non-motorist injury severity for the two locations. The results highlight the importance of examining the effect of various independent variables on non-motorist injury severity outcome by crash location type.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140187614","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 speed difference on injury severity of freeway rear-end crashes: Insights from correlated joint random parameters bivariate probit models and temporal instability","authors":"Chenzhu Wang,&nbsp;Mohamed Abdel-Aty,&nbsp;Lei Han","doi":"10.1016/j.amar.2024.100320","DOIUrl":"https://doi.org/10.1016/j.amar.2024.100320","url":null,"abstract":"<div><p>Rear-end crashes particularly on freeways are the most frequent type of collisions causing many injuries, damage and congestion. This paper investigates the impact of varying speed differences between following and leading vehicles on injury severity in two-vehicle rear-end crashes. It develops three groups of correlated joint random parameters bivariate probit models with heterogeneity in means. The rear-end crash data from 2019 to 2021 on Interstate freeways in Florida are utilized, and categorized into periods before, during, and after the COVID-19 pandemic. The study considers two potential injury severity outcomes: no injury and injury/fatality, for both drivers involved in these crashes. The findings indicate that a range of variables, including driver, vehicle, roadway, environmental, crash, and temporal attributes, significantly influence the injury severity outcomes for drivers in both following and leading vehicles. Demonstrating superior goodness-of-fit, the proposed approach sheds light on interactive unobserved heterogeneity, captured through heterogeneity in means and significant correlations among random parameters. The study observes critical influences on the injury severity outcomes of both drivers, with significant factors such as gender, age, vehicle type, weather conditions, lighting, and time of day. Furthermore, the results substantiate the heightened risk outcomes associated with greater speed differences and the period of the COVID-19 pandemic. These findings yield further insights into the risk mechanisms of two-vehicle rear-end crashes and offer guidance for the development of effective safety countermeasures.</p></div>","PeriodicalId":47520,"journal":{"name":"Analytic Methods in Accident Research","volume":null,"pages":null},"PeriodicalIF":12.9,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139993615","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}