Unobserved heterogeneity and the statistical analysis of highway accident data
Highway accidents are complex events that involve a variety of human responses to external
stimuli, as well as complex interactions between the vehicle, roadway features/condition …
stimuli, as well as complex interactions between the vehicle, roadway features/condition …
Predicting intersection crash frequency using connected vehicle data: A framework for geographical random forest
Accurate crash frequency prediction is critical for proactive safety management. The
emerging connected vehicles technology provides us with a wealth of vehicular motion data …
emerging connected vehicles technology provides us with a wealth of vehicular motion data …
Exploratory analysis of automated vehicle crashes in California: A text analytics & hierarchical Bayesian heterogeneity-based approach
AM Boggs, B Wali, AJ Khattak - Accident Analysis & Prevention, 2020 - Elsevier
Automated vehicles (AVs) represent an opportunity to reduce crash frequency by eliminating
driver error, as safety studies reveal human error contributes to the majority of crashes. To …
driver error, as safety studies reveal human error contributes to the majority of crashes. To …
Macro-level pedestrian and bicycle crash analysis: Incorporating spatial spillover effects in dual state count models
This study attempts to explore the viability of dual-state models (ie, zero-inflated and hurdle
models) for traffic analysis zones (TAZs) based pedestrian and bicycle crash frequency …
models) for traffic analysis zones (TAZs) based pedestrian and bicycle crash frequency …
Random parameters multivariate tobit and zero-inflated count data models: addressing unobserved and zero-state heterogeneity in accident injury-severity rate and …
PC Anastasopoulos - Analytic methods in accident research, 2016 - Elsevier
This paper uses data collected over a five-year period between 2005 and 2009 in Indiana to
estimate random parameters multivariate tobit and zero-inflated count data models of …
estimate random parameters multivariate tobit and zero-inflated count data models of …
Commercial truck crash injury severity analysis using gradient boosting data mining model
Introduction Truck crashes contribute to a large number of injuries and fatalities. This study
seeks to identify the contributing factors affecting truck crash severity using 2010 to 2016 …
seeks to identify the contributing factors affecting truck crash severity using 2010 to 2016 …
An improved deep learning model for traffic crash prediction
Machine‐learning technology powers many aspects of modern society. Compared to the
conventional machine learning techniques that were limited in processing natural data in the …
conventional machine learning techniques that were limited in processing natural data in the …
Approach-level real-time crash risk analysis for signalized intersections
J Yuan, M Abdel-Aty - Accident Analysis & Prevention, 2018 - Elsevier
Intersections are among the most dangerous roadway facilities due to the complex traffic
conflicting movements and frequent stop-and-go traffic. However, previous intersection …
conflicting movements and frequent stop-and-go traffic. However, previous intersection …
Exploring the who, what, when, where, and why of automated vehicle disengagements
AM Boggs, R Arvin, AJ Khattak - Accident Analysis & Prevention, 2020 - Elsevier
Automated vehicles are emerging on the transportation networks as manufacturers test their
automated driving system (ADS) capabilities in complex real-world environments in testing …
automated driving system (ADS) capabilities in complex real-world environments in testing …
Fifty years of accident analysis & prevention: A bibliometric and scientometric overview
Abstract Accident Analysis & Prevention (AA&P) is a leading academic journal established
in 1969 that serves as an important scientific communication platform for road safety studies …
in 1969 that serves as an important scientific communication platform for road safety studies …