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Effects of speed difference on injury severity of freeway rear-end crashes: Insights from correlated joint random parameters bivariate probit models and temporal instability Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2024-02-27 Chenzhu Wang, Mohamed Abdel-Aty, Lei Han
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
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A novel integrated approach to modeling and predicting crash frequency by crash event state Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2024-02-13 Angela Haddad, Aupal Mondal, Naveen Eluru, Chandra R. Bhat
In this study, we propose a novel integrated parametric framework for analyzing multivariate crash count data based on linking a univariate count model for the total count of motor vehicle crashes across all possible crash states with a discrete choice model for crash event state given a crash. In doing so, we are able to use information at the disaggregate crash-level from an unordered model structure
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Estimating the effect of proximity to school on cyclist safety using a simultaneous-equations model with heterogeneity in covariance to address potential endogeneity Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2024-02-02 Shahram Heydari, Michael Forrest
Traffic safety around schools is a major concern for policy makers and as such safety interventions are often targeted near schools. This paper shows the importance of accounting for the potential endogeneity of proximity to school when attempting to estimate its impact on traffic safety. In this research, we use a Bayesian simultaneous econometric approach with heterogeneity in covariance to disentangle
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A multi-year statistical analysis of driver injury severities in single-vehicle freeway crashes with and without airbags deployed Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2024-01-17 Richard Dzinyela, Nawaf Alnawmasi, Emmanuel Kofi Adanu, Bahar Dadashova, Dominique Lord, Fred Mannering
This paper seeks to identify factors that influence driver injury severities in single-vehicle freeway crashes when airbags deployed and when airbags did not deploy. Injury-severity models were estimated using random parameters logit models with consideration given to possible heterogeneity in means and variances of the random parameters to account for unobserved heterogeneity. Three years of pre-COVID
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Multi-dimensional unobserved heterogeneities: Modeling likelihood of speeding behaviors in different patterns for taxi speeders with mixed distributions, multivariate errors, and jointly correlated random parameters Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-12-28 Yue Zhou, Chuanyun Fu, Xinguo Jiang
Speeding behaviors can be classified into different patterns according to both speeding-range and speeding-distance. Among the speeding patterns, some are more frequently observed in specific traffic scenarios, implying that the likelihood of speeding behaviors may vary across the speeding patterns due to the inconsistent impact of temporal, road, environmental, and other traffic factors. Additionally
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A Poisson Lognormal-Lindley model for simultaneous estimation of multiple crash-types: Application of multivariate and pooled univariate models Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-12-28 Hassan Bin Tahir, Shamsunnahar Yasmin, Md Mazharul Haque
Challenges addressing overdispersion, unobserved heterogeneity, the preponderance of zeros, and correlation in the dependent variables of crash count models are of significant interest. Accounting for all these data issues simultaneously is few and far between. This study proposes a new mixing distribution model that accounts for overdispersion and the preponderance of zeros in crash count models.
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On the need to address fixed-parameter issues before applying random parameters: A simulation-based study Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-11-28 Numan Ahmad, Tanmoy Bhowmik, Vikash V. Gayah, Naveen Eluru
Count regression models have been applied to model expected crash frequency at individual roadway locations. Random parameters have been increasingly integrated into these models to account for unobserved heterogeneity. However, the introduction of random parameters might also mask issues in the model specification, leading to inaccurate relationships and model interpretation. Two of these specification-related
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Real-time crash risk prediction in freeway tunnels considering features interaction and unobserved heterogeneity: A two-stage deep learning modeling framework Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-11-07 Jieling Jin, Helai Huang, Chen Yuan, Ye Li, Guoqing Zou, Hongli Xue
Real-time prediction of crash risk is an effective method for enhancing traffic safety, but it is not fully explored in freeway tunnels. A two-stage deep learning modeling framework comprising a preliminary exploration stage and a prediction and analysis stage is proposed for real-time crash risk prediction in freeway tunnels. A random parameters logit model with heterogeneity in means and variances
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Exploring variations and temporal instability of factors affecting driver injury severities between different vehicle impact locations under adverse road surface conditions Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-11-10 Qiaoqiao Ren, Min Xu
The adverse road surface condition has been identified as an important factor resulting in serious casualties and property losses in traffic accidents, and there is a tremendous need to uncover the interaction mechanism between deteriorating road surfaces and vehicle impact locations on the driver injury severity at a disaggregate level. In this paper, three groups of random parameters logit models
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Dynamic Bayesian hierarchical peak over threshold modeling for real-time crash-risk estimation from conflict extremes Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-11-02 Chuanyun Fu, Tarek Sayed
Using traffic conflict-based extreme value theory (EVT) models to quantify real-time crash-risk of road facilities is a promising direction for developing proactive traffic safety management strategies. Existing EVT real-time crash-risk analysis studies have only focused on using block maxima models. This study proposes a dynamic Bayesian hierarchical peak over threshold modeling approach to estimate
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A generalized driving risk assessment on high-speed highways using field theory Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-09-19 Yang-Jun Joo, Eui-Jin Kim, Dong-Kyu Kim, Peter Y. Park
This study presents a new safety measure derived from field theory. It evaluates the risk arising from the various concurrent conflicts within a platoon that can occur on high-speed highway driving situations, such as car-following, yielding, and lane changing. We defined the risk field as a finite scalar field produced by traveling vehicles on the road, and we defined the conflict field as the overlapping
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Real-time crash risk forecasting using Artificial-Intelligence based video analytics: A unified framework of generalised extreme value theory and autoregressive integrated moving average model Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-09-09 Fizza Hussain, Yasir Ali, Yuefeng Li, Md Mazharul Haque
With the recent advancements in computer vision and artificial intelligence, traffic conflicts occurring at an intersection and associated traffic characteristics can be obtained at the granular level of a signal cycle in real-time. This capability enables the estimation of the real-time crash risk using sophisticated modelling techniques, e.g., extreme value theory. However, these models are inherently
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An analysis of day and night bicyclist injury severities in vehicle/bicycle crashes: A comparison of unconstrained and partially constrained temporal modeling approaches Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-09-09 Nawaf Alnawmasi, Fred Mannering
Due to visibility limitations and other factors, the injuries sustained by bicyclists in nighttime vehicle-bicycle crashes tend to be more severe than daytime crashes. This paper seeks to provide insights into this day/night injury severity phenomenon by studying how day/night bicyclist injury severities have changed in crashes that occurred before, during, and after the COVID-19 lock downs. Using
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Effects of design consistency measures and roadside hazard types on run-off-road crash severity: Application of random parameters hierarchical ordered probit model Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-09-06 Shinthia Azmeri Khan, Shamsunnahar Yasmin, Md Mazharul Haque
Run-off-road crashes are one of the most significant causes of road deaths worldwide. Given such significant safety concerns, a number of earlier studies examined the critical factors contributing towards run-off-road crash severity outcomes, mostly by using the information compiled in the official crash database. However, the official crash databases are less likely to have detailed information on
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Modelling the continuum of serious traffic injuries in police-hospital linked data by applying the random parameters hazard-based duration model Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-08-19 Khalid Alzaffin, Sherrie-Anne Kaye, Angela Watson, Md Mazharul Haque
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How heterogeneity has been examined in transportation safety analysis: A review of latent class modeling applications Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-08-19 Sung Hoo Kim
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
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Modelling speed reduction behaviour on variable speed limit-controlled highways considering surrounding traffic pressure: A random parameters duration modelling approach Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-07-10 Yasir Ali, Mark P. H. Raadsen, Michiel C. J. Bliemer
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
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Analysis of duration between crashes using a hazard-based duration approach with heterogeneity in means and variances: Some new evidence Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-06-02 Mohammad M. Hamed, Ahmad AlShaer
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
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Temporal stability of the impact of factors determining drivers’ injury severities across traffic barrier crashes in mountainous regions Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-05-05 Dongdong Song, Xiaobao Yang, Panagiotis Ch. Anastasopoulos, Xingshui Zu, Xianfei Yue, Yitao Yang
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
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Identification of adequate sample size for conflict-based crash risk evaluation: An investigation using Bayesian hierarchical extreme value theory models Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-04-28 Chuanyun Fu, Tarek Sayed
The use of traffic conflict-based models to estimate crash risk and evaluate the safety of road locations is a popular direction for road safety analysis. However, a challenging issue of traffic conflict-based crash risk modeling is the selection of an appropriate sample size. Reliable conflict-based crash risk models typically require a large sample size which is always very difficult to collect.
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An econometric framework for integrating aggregate and disaggregate level crash analysis Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-04-26 Shahrior Pervaz, Tanmoy Bhowmik, Naveen Eluru
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
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Modelling the response times of mobile phone distracted young drivers: A hybrid approach of decision tree and random parameters duration model Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-04-26 Yasir Ali, Md Mazharul Haque
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
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Car-following crash risk analysis in a connected environment: A Bayesian non-stationary generalised extreme value model Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-04-25 Faizan Nazir, Yasir Ali, Anshuman Sharma, Zuduo Zheng, Md Mazharul Haque
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
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Real-time safest route identification: Examining the trade-off between safest and fastest routes Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-04-22 Tarek Ghoul, Tarek Sayed, Chuanyun Fu
Several studies have shown that crash risk is a dynamic quantity that is frequently changing with considerable spatial and temporal variations. Recent advances in safety evaluation techniques such as using extreme value theory (EVT) models provided the opportunity to use traffic conflict data obtained from road user trajectories to estimate real time safety metrics. These metrics can aggregate crash
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Assessing traffic conflict/crash relationships with extreme value theory: Recent developments and future directions for connected and autonomous vehicle and highway safety research Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-04-13 Yasir Ali, Md Mazharul Haque, Fred Mannering
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
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Traffic conflict prediction using connected vehicle data Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-03-10 Zubayer Islam, Mohamed Abdel-Aty
Transportation safety studies have been mostly focused on using crash data that are rare events. Alternatively, conflict estimation can be used to assess safety. This has been proven as a proactive design methodology that does not rely on crashes and requires shorter observation. Traditionally, the safety studies involving both these reactive and proactive methods were based on aggregated data that
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Temporal instability and age differences of determinants affecting injury severities in nighttime crashes Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-02-24 Xintong Yan, Jie He, Changjian Zhang, Chenwei Wang, Yuntao Ye, Pengcheng Qin
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
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Incorporating real-time weather conditions into analyzing clearance time of freeway accidents: A grouped random parameters hazard-based duration model with time-varying covariates Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-02-14 Qiang Zeng, Fangzhou Wang, Tiantian Chen, N.N. Sze
To minimize non-recurrent congestion, a better understanding of the factors that affect accident clearance time is crucial, in order to optimize incident management strategies. A number of methods have been developed to predict incident clearance duration, but few of those have considered the time-varying nature of certain observed factors. In addressing this gap in the literature, this study developed
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A random parameters copula-based binary logit-generalized ordered logit model with parameterized dependency: Application to active traveler injury severity analysis Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-01-23 Natakorn Phuksuksakul, Shamsunnahar Yasmin, Md. Mazharul Haque
A copula-based dependence approach accommodates various facets of dependence structures in building multivariate stochastic models. In existing studies, applications of copula for ordinal random variables are predominantly modeled by employing traditional ordered models (ordered logit/probit) while assuming the effects of parameters to remain the same across all observations. The methodological contributions
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An empirical investigation of driver car-following risk evolution using naturistic driving data and random parameters multinomial logit model with heterogeneity in means and variances Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2023-01-04 Qiangqiang Shangguan, Junhua Wang, Ting Fu, Shou'en Fang, Liping Fu
This study aims to address the questions of how driving risk evolves during car-following processes and what factors contribute to the underlying evolution patterns. An empirical study is conducted using real world car-following data collected in the Shanghai Naturalistic Driving Study (SH-NDS). The evolution of the driving risk induced by the dynamic coupling between the leading and following vehicles
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A Bayesian generalised extreme value model to estimate real-time pedestrian crash risks at signalised intersections using artificial intelligence-based video analytics Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-12-15 Yasir Ali, Md. Mazharul Haque, Fred Mannering
Pedestrians represent a vulnerable road user group at signalised intersections. As such, properly estimating pedestrian crash risk at discrete short intervals is important for real-time safety management. This study proposes a novel real-time vehicle-pedestrian crash risk modelling framework for signalised intersections. At the core of this framework, a Bayesian Generalised Extreme Value modelling
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Evidence of sample selectivity in highway injury-severity models: The case of risky driving during COVID-19 Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-12-14 Mouyid Islam, Asim Alogaili, Fred Mannering, Michael Maness
Research in highway safety continues to struggle to address two potentially important issues; the role that unobserved factors may play on resulting crash and injury-severity likelihoods, and the issue of identification in safety modeling caused by the self-selective sampling inherent in commonly used safety data (the fact that drivers in observed crashes are not a random sample of the driving population
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Dynamic identification of short-term and longer-term hazardous locations using a conflict-based real-time extreme value safety model Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-11-24 Tarek Ghoul, Tarek Sayed, Chuanyun Fu
A novel and effective approach to safety management requires evaluating the safety of locations over short time periods (e.g. minutes). Unlike traditional methods that are based on aggregate crash records over a few years, crash proneness in this approach reflects short-time durations and is related to dynamic traffic changes and dangerous driving events. This paper proposes a new approach to dynamically
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Grouped Random Parameters Negative Binomial-Lindley for accounting unobserved heterogeneity in crash data with preponderant zero observations Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-11-15 A.S.M. Mohaiminul Islam, Mohammadali Shirazi, Dominique Lord
Developing robust and reliable statistical models to estimate, analyze, and understand crash data is a key element in various highway safety evaluation tasks. Crash data have characteristics not found in other data, including but not limited to the excess number of zero responses. The Negative Binomial-Lindley (NB-L) model has been proposed as a method to analyze data with many zero observations. In
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Modeling traveler’s speed-route joint choice behavior with heterogeneous safety concern Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-11-01 Chunyang Han, Guangming Xu, Amjad Pervez, Fan Gao, Helai Huang, Xin Pei, Yi Zhang
In this study, a speed-route joint choice model considering traveler’s safety concerns is proposed to concurrently model traveler’s safety-oriented travel speed and route choice behavior. Specifically, the safe-speed choice behavior is modeled as a trade-off process between perceived traffic safety and efficiency using a disutility function. The safe-route choice behavior is described by the proposed
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Unobserved heterogeneity in ramp crashes due to alignment, interchange geometry and truck volume: Insights from a random parameter model Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-10-31 Nardos Feknssa, Narayan Venkataraman, Venky Shankar, Tewodros Ghebrab
This paper presents a negative binomial random parameter model with heterogeneity in means and variance to capture the effect of heterogeneous effect of ramp type, alignment, truck volume and interchange geometry and on freeway ramp crash frequency. Two years (2018–2019) of crash data on freeway ramps in Washington State were analyzed. Model estimation results show ramp type (directional, semi-directional
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A physics-informed road user safety field theory for traffic safety assessments applying artificial intelligence-based video analytics Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-10-12 Ashutosh Arun, Md. Mazharul Haque, Simon Washington, Fred Mannering
The rapid technological advancements in video analytics and the availability of big data have made traffic conflict techniques a viable tool for road safety assessments. They can potentially overcome many major limitations of conventional road safety practices that use crash-data analyses. However, the current traffic conflict techniques flag serious concerns regarding the context-dependence of the
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A crash feature-based allocation method for boundary crash problem in spatial analysis of bicycle crashes Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-09-28 Hongliang Ding, Yuhuan Lu, N.N. Sze, Constantinos Antoniou, Yanyong Guo
In conventional safety analysis, traffic and crash data are often aggregated at the geographical units like census tracts, street blocks, and traffic analysis zones, which are often delineated by roads and other physical entities. A considerable proportion of crashes may occur at or near the boundary of geographical units. Such the crashes, also known as boundary crashes, can correlate with the explanatory
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Exploring the temporal variability of the factors affecting driver injury severity by body region employing a hybrid econometric approach Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-09-16 Ahmed Kabli, Tanmoy Bhowmik, Naveen Eluru
The current study contributes to safety literature by incorporating the influence of temporal factors (observed and unobserved) within a multivariate model system for medical professional generated body region specific injury severity score. For this purpose, we adopt a hybrid econometric modeling approach that accommodates for the unobserved factors using two mechanisms. First, we parameterize unobserved
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Evaluating gender differences in injury severities of non-helmet wearing motorcyclists: Accommodating temporal shifts and unobserved heterogeneity Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-09-16 Chenzhu Wang, Muhammad Ijaz, Fei Chen, Yunlong Zhang, Jianchuan Cheng, Muhammad Zahid
With rapid growth in motorcycle use and relatively low helmet-wearing usage rates, injuries and fatalities resulting from motorcycle crashes in Pakistan are a critical concern. To investigate possible temporal instability and differences in the factors that determine resulting injury severities between male and female non-helmet wearing motorcyclists, this study estimated male and female injury severity
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A hybrid modelling framework of machine learning and extreme value theory for crash risk estimation using traffic conflicts Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-09-16 Fizza Hussain, Yuefeng Li, Ashutosh Arun, Md. Mazharul Haque
Extreme value theory is the state-of-the-art modelling technique for estimating crash risk from traffic conflicts, with two different sampling techniques, i.e. block maxima and peak-over-threshold, at its core. However, the uncertainty associated with the estimates obtained by these sampling techniques has been too large to enable its widespread practical use. A fundamental reason for this issue is
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Accounting for unobserved heterogeneity and spatial instability in the analysis of crash injury-severity at highway-rail grade crossings: A random parameters with heterogeneity in the means and variances approach Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-09-16 Sheikh Shahriar Ahmed, Francesco Corman, Panagiotis Ch. Anastasopoulos
Crashes at highway-rail grade crossings often result in higher proportion of injury and fatality of the vehicle occupants as compared to other crash types, necessitating in-depth investigation to identify their causal factors. In this study, injury-severity outcomes from highway-rail grade crossing crashes are analyzed using crash data from Texas and California, which are the most vulnerable states
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Modeling endogeneity between motorcyclist injury severity and at-fault status by applying a Bayesian simultaneous random-parameters model with a recursive structure Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-09-06 Fangrong Chang, Shamsunnahar Yasmin, Helai Huang, Alan H.S. Chan, Md. Mazharul Haque
Motorcyclists’ at-fault status is an important factor influencing crash injury severity in that intrinsically unsafe riders tend to be at fault and are the ones likely to be involved in severe crashes. However, this endogeneity issue and its influence on model estimations have seldom been investigated with regard to motorcyclist crash severity analysis. This study proposes a simultaneous model system
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Addressing endogeneity in modeling speed enforcement, crash risk and crash severity simultaneously Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-09-02 Shamsunnahar Yasmin, Naveen Eluru, Md. Mazharul Haque
Speeding is one of the major significant causes of high crash risk and the associated injury severity outcomes. To combat such significant safety concerns, a speed limit enforcement system has been adopted widely around the world. This study aims to present an econometric approach that estimates the casual effect of speed enforcement on safety while addressing the endogeneity issue by employing an
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A multivariate method for evaluating safety from conflict extremes in real time Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-08-28 Chuanyun Fu, Tarek Sayed
Several studies have advocated the use of extreme value theory (EVT) traffic conflict models for real-time crash risk prediction using real-time safety indices such as the risk of crash (RC) and return level of a cycle (RLC). This approach provides a logical framework to estimate crash risk by extrapolating from the observed level (i.e., traffic conflict) to the unobserved level (i.e., crash). In these
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Addressing unobserved heterogeneity at road user level for the analysis of conflict risk at tunnel toll plaza: A correlated grouped random parameters logit approach with heterogeneity in means Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-08-26 Penglin Song, N.N. Sze, Ou Zheng, Mohamed Abdel-Aty
Toll plaza is a designated area of controlled-access roads like expressway, bridge, and tunnel for toll collection. A number of toll booths are often placed at the toll plaza accommodating high passing traffic and multiple payment methods. Traffic and safety characteristics of toll plazas are different from that of other road entities. Different conflict risk indicators, which are usually longitudinal
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Real-time crash potential prediction on freeways using connected vehicle data Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-08-19 Shile Zhang, Mohamed Abdel-Aty
The real-time crash potential prediction model is one of the important components of proactive traffic management systems. Over the years numerous models have been proposed to predict crash potential and achieved promising results using input data from roadside detectors. However, the detectors are normally installed at certain locations with limited coverage, while the connected vehicle data can provide
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The impact of weekday, weekend, and holiday crashes on motorcyclist injury severities: Accounting for temporal influence with unobserved effect and insights from out-of-sample prediction Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-08-13 Chamroeun Se, Thanapong Champahom, Sajjakaj Jomnonkwao, Nopadon Kronprasert, Vatanavongs Ratanavaraha
This paper examines the differences between weekday, weekend, and holiday crashes on the severity of motorcyclist injury using four-year motorcycle crash data in Thailand from 2016 to 2019. While also considering the temporal stability assessment of significant factors, this study adopted a random parameters logit model with possible heterogeneity in means and variances to account for unobserved heterogeneity
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Investigating the effects of sleepiness in truck drivers on their headway: An instrumental variable model with grouped random parameters and heterogeneity in their means Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-08-13 Amir Pooyan Afghari, Eleonora Papadimitriou, Fran Pilkington-Cheney, Ashleigh Filtness, Tom Brijs, Kris Brijs, Ariane Cuenen, Bart De Vos, Helene Dirix, Veerle Ross, Geert Wets, André Lourenço, Lourenço Rodrigues
Sleepiness is a common human factor among truck drivers resulting from sleep loss or time of day and causing impairment in vigilance, attention, and driving performance. While driver sleepiness may be associated with increased risk on the road, sleepy drivers may drive more cautiously as a result of risk-compensating behaviour. This endogeneity has been overlooked in the previous driver behaviour studies
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Integrating macro and micro level crash frequency models considering spatial heterogeneity and random effects Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-06-26 Shahrior Pervaz, Tanmoy Bhowmik, Naveen Eluru
Safety literature has traditionally developed independent model systems for macroscopic and microscopic level analysis. The current research effort contributes to the literature on crash frequency by building a bridge between these two divergent streams of crash frequency research. The study proposes an integrated micro–macro level model for crash frequency estimation. Specifically, the study develops
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Modelling animal-vehicle collision counts across large networks using a Bayesian hierarchical model with time-varying parameters Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-05-27 Krishna Murthy Gurumurthy, Prateek Bansal, Kara M. Kockelman, Zili Li
Animal-vehicle collisions (AVCs) are common around the world and result in considerable loss of animal and human life, as well as significant property damage and regular insurance claims. Understanding their occurrence in relation to various contributing factors and being able to identify high-risk locations are valuable to AVC prevention, yielding economic, social, and environmental cost savings.
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Evaluating the safety of autonomous vehicle–pedestrian interactions: An extreme value theory approach Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-05-21 Abdul Razak Alozi, Mohamed Hussein
With the increasing advancements in autonomous vehicle (AV) technologies, the forecasts of AV market shares seem to follow an ever-growing trend. This leads to the inherent need for proactive safety evaluations of AV impacts on other road users. To that end, this study proposes a modeling framework for the proactive assessment of pedestrian safety in AV environments. The proposed framework relies on
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A multiple membership multilevel negative binomial model for intersection crash analysis Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-05-02 Ho-Chul Park, Byung-Jung Park, Peter Y. Park
Many intersections belong to more than one zone, but most research has not considered the effects of multiple zones in intersection crash analysis. This issue is known as a boundary problem. Unobserved heterogeneity between zones can lead to model misspecification which can result in biased parameter estimates and poor model fitting performance. This study investigated the issue using five years of
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A Bayesian correlated grouped random parameters duration model with heterogeneity in the means for understanding braking behaviour in a connected environment Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-04-18 Yasir Ali, Md. Mazharul Haque, Zuduo Zheng, Amir Pooyan Afghari
Driver’s response to a pedestrian crossing requires braking, whereby both excess and inadequate braking is directly associated with crash risk. The highly anticipated connected environment aims to increase drivers’ situational awareness by providing advanced information and assisting them during critical driving tasks such as braking. Focussing on this crucial behaviour and combined with the promise
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Differences of overturned and hit-fixed-object crashes on rural roads accompanied by speeding driving: Accommodating potential temporal shifts Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-04-09 Xintong Yan, Jie He, Guanhe Wu, Changjian Zhang, Chenwei Wang, Yuntao Ye
Overturned crashes are associated with a disproportionate number of severe injuries and fatalities, while hit-fixed-object crashes are acknowledged as the most frequent single-vehicle crashes. To investigate the temporal stability and differences of contributing factors determining different injury severity levels in overturned and hit-fixed-object crashes on rural roads accompanied by speeding driving
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Temporal stability of factors affecting injury severity in rear-end and non-rear-end crashes: A random parameter approach with heterogeneity in means and variances Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-04-01 Chenzhu Wang, Fei Chen, Yunlong Zhang, Shuyi Wang, Bin Yu, Jianchuan Cheng
Rear-end crashes have become a serious global issue, with increasing injuries and fatalities accounting for massive property loss. The purpose of this study is to investigate the variation in the influence of factors affecting injury severity in rear-end and non-rear-end crashes and the change in impact degree over time. Using the three-year crash data of the Beijing–Shanghai Expressway from 2017 to
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An alternate crash severity multicategory modeling approach with asymmetric property Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-03-26 Dawei Li, Mustafa F.M. Al-Mahamda, Yuchen Song, Siqi Feng, N.N. Sze
The logit model and its variations have been used extensively in the field of traffic safety in general, and crash severity analysis in particular. Attempts were made to overcome the logit's shortcomings and limitations by generalizing its binary form to a more relaxed and unconstrained setting. Such attempts include the addition of shape parameters in order to add more flexibility to the probability
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A temporal instability analysis of environmental factors affecting accident occurrences during snow events: The random parameters hazard-based duration model with means and variances heterogeneity Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-03-16 Jiajun Pang, Adam Krathaus, Irina Benedyk, Sheikh Shahriar Ahmed, Panagiotis Ch. Anastasopoulos
The present paper introduces the time between the start of a snowfall and the occurrence of a motor vehicle accident as a novel measure for evaluating motor vehicle safety during snowfalls. Detailed information of accidents that occurred during snowfalls between 2017 and 2020 in the state of New York are used to explore the accelerating or delaying effect of different factors on the time between the
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Using traffic flow characteristics to predict real-time conflict risk: A novel method for trajectory data analysis Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-03-15 Chen Yuan, Ye Li, Helai Huang, Shiqi Wang, Zhenhao Sun, Yan Li
The real-time conflict prediction model using traffic flow characteristics is much less studied than the crash-based model. This study aims at exploring the relationship between conflicts and traffic flow features with the consideration of heterogeneity and developing predictive models to identify conflict-prone conditions in a real-time manner. The high-resolution trajectory data from the HighD dataset
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A temporal assessment of distracted driving injury severities using alternate unobserved-heterogeneity modeling approaches Anal. Methods Accid. Res. (IF 12.9) Pub Date : 2022-03-11 Nawaf Alnawmasi, Fred Mannering
This study explores temporal shifts in the effects of explanatory variables on the injury severity outcomes of crashes involving distracted driving. Using data from distracted driving crashes on Kansas State highways over a four-year period (from 2014 to 2017 inclusive), separate yearly models of driver-injury severities (with possible outcomes of severe injury, minor injury, and no injury) were estimated