Analytical Methods in Accident Research is an academic journal dedicated to accident research methodology, aimed at demonstrating how innovative analytical methods can be utilized to conduct in-depth research on vehicle collisions and other traffic accidents. The papers published in this journal cover the development and application of new methods, as well as practical application cases of these methods in accident research. The goal of the journal is to provide a platform for researchers to share their latest research findings and innovative methods in the field of accident analysis. These methods may include advanced statistical models, computer simulation techniques, big data analysis, etc., which provide new perspectives and tools for understanding the causes of accidents, predicting accident trends, and evaluating the effectiveness of preventive measures.By publishing these papers, the journal aims to promote academic exchange in the field of accident research, advance the development and application of new methods, and provide scientific basis for reducing the incidence and severity of accidents. At the same time, the journal also provides valuable reference resources for policy makers, safety engineers, and other relevant professionals, helping them to more effectively respond to accident risks and develop corresponding prevention strategies.
Analytic方法Accident研究《事故研究中的分析方法》是一本专注于事故研究方法论的学术期刊,旨在展示如何利用创新的分析方法来深入研究车辆碰撞和其他交通事故。该杂志发表的论文涵盖了新方法的开发和应用,以及这些方法在事故研究中的实际应用案例。期刊的目标是提供一个平台,让研究者分享他们在事故分析领域的最新研究成果和创新方法。这些方法可能包括先进的统计模型、计算机模拟技术、大数据分析等,它们为理解事故发生的原因、预测事故趋势以及评估预防措施的有效性提供了新的视角和工具。通过发表这些论文,期刊旨在促进事故研究领域的学术交流,推动新方法的发展和应用,为减少事故发生率和减轻事故严重程度提供科学依据。同时,期刊也为政策制定者、安全工程师和其他相关专业人士提供了宝贵的参考资源,帮助他们更有效地应对事故风险和制定相应的预防策略。
The effects of driver fatigue, gender, and distracted driving on perceived and observed aggressive driving behavior: A correlated grouped random parameters bivariate probit approach
来源期刊:Analytic Methods in Accident ResearchDOI:10.1016/J.AMAR.2019.100091
Time-of-day variations and temporal instability of factors affecting injury severities in large-truck crashes
来源期刊:Analytic Methods in Accident ResearchDOI:10.1016/J.AMAR.2019.100102
A statistical assessment of temporal instability in the factors determining motorcyclist injury severities
来源期刊:Analytic Methods in Accident ResearchDOI:10.1016/J.AMAR.2019.100090
Analyzing road crash frequencies with uncorrelated and correlated random-parameters count models: An empirical assessment of multilane highways
来源期刊:Analytic Methods in Accident ResearchDOI:10.1016/J.AMAR.2019.100101
An exploratory investigation of public perceptions towards safety and security from the future use of flying cars in the United States
来源期刊:Analytic Methods in Accident ResearchDOI:10.1016/J.AMAR.2019.100103
A preliminary investigation of the effectiveness of high visibility enforcement programs using naturalistic driving study data: A grouped random parameters approach
来源期刊:Analytic Methods in Accident ResearchDOI:10.1016/J.AMAR.2018.10.003
A hierarchical Bayesian spatiotemporal random parameters approach for alcohol/drug impaired-driving crash frequency analysis
来源期刊:Analytic Methods in Accident ResearchDOI:10.1016/J.AMAR.2019.01.002
Combining driving simulator and physiological sensor data in a latent variable model to incorporate the effect of stress in car-following behaviour
来源期刊:Analytic Methods in Accident ResearchDOI:10.1016/J.AMAR.2019.02.001
Bayesian hierarchical modeling of the non-stationary traffic conflict extremes for crash estimation
来源期刊:Analytic Methods in Accident ResearchDOI:10.1016/J.AMAR.2019.100100
Bayesian hierarchical modeling of traffic conflict extremes for crash estimation: A non-stationary peak over threshold approach
来源期刊:Analytic Methods in Accident ResearchDOI:10.1016/j.amar.2019.100106
Modeling unobserved heterogeneity for zonal crash frequencies: A Bayesian multivariate random-parameters model with mixture components for spatially correlated data
来源期刊:Analytic Methods in Accident ResearchDOI:10.1016/j.amar.2019.100105
A latent class approach for driver injury severity analysis in highway single vehicle crash considering unobserved heterogeneity and temporal influence
来源期刊:Analytic Methods in Accident ResearchDOI:10.1016/j.amar.2019.100110
Do we need multivariate modeling approaches to model crash frequency by crash types? A panel mixed approach to modeling crash frequency by crash types
来源期刊:Analytic Methods in Accident ResearchDOI:10.1016/j.amar.2019.100107
Hourly associations between weather factors and traffic crashes: non-linear and lag effects
来源期刊:Analytic Methods in Accident ResearchDOI:10.1016/j.amar.2019.100109
Comprehensive evaluation of signal-coordinated arterials on traffic safety
来源期刊:Analytic Methods in Accident ResearchDOI:10.1016/J.AMAR.2019.01.001
Contrasting case-wise deletion with multiple imputation and latent variable approaches to dealing with missing observations in count regression models
来源期刊:Analytic Methods in Accident ResearchDOI:10.1016/J.AMAR.2019.100104
A marginalized random effects hurdle negative binomial model for analyzing refined-scale crash frequency data
来源期刊:Analytic Methods in Accident ResearchDOI:10.1016/J.AMAR.2019.100092