Assessing traffic conflict/crash relationships with extreme value theory: Recent developments and future directions for connected and autonomous vehicle and …
With proactive safety assessment gaining significant attention in the literature, the
relationship between traffic conflicts (which form the underpinnings of proactive safety …
relationship between traffic conflicts (which form the underpinnings of proactive safety …
A review of spatial approaches in road safety
A Ziakopoulos, G Yannis - Accident Analysis & Prevention, 2020 - Elsevier
Spatial analyses of crashes have been adopted in road safety for decades in order to
determine how crashes are affected by neighboring locations, how the influence of …
determine how crashes are affected by neighboring locations, how the influence of …
Comparison of four statistical and machine learning methods for crash severity prediction
A Iranitalab, A Khattak - Accident Analysis & Prevention, 2017 - Elsevier
Crash severity prediction models enable different agencies to predict the severity of a
reported crash with unknown severity or the severity of crashes that may be expected to …
reported crash with unknown severity or the severity of crashes that may be expected to …
A comparative study of state-of-the-art driving strategies for autonomous vehicles
The autonomous vehicle is regarded as a promising technology with the potential to
reshape mobility and solve many traffic issues, such as accessibility, efficiency …
reshape mobility and solve many traffic issues, such as accessibility, efficiency …
Review of recent trends in charging infrastructure planning for electric vehicles
The exhaustive nature of fossil fuels and environmental concerns associated with
greenhouse gases are the major causes of the paradigm shift from conventional vehicles to …
greenhouse gases are the major causes of the paradigm shift from conventional vehicles to …
Modeling crash spatial heterogeneity: Random parameter versus geographically weighting
The widely adopted techniques for regional crash modeling include the negative binomial
model (NB) and Bayesian negative binomial model with conditional autoregressive prior …
model (NB) and Bayesian negative binomial model with conditional autoregressive prior …
SVM and KNN ensemble learning for traffic incident detection
J Xiao - Physica A: Statistical Mechanics and its Applications, 2019 - Elsevier
Traffic incident detection is a very important research area of intelligent transportation
systems. Many methods have obtained good performance in traffic incident detection …
systems. Many methods have obtained good performance in traffic incident detection …
Exploring the determinants of pedestrian–vehicle crash severity in New York City
Pedestrian–vehicle crashes remain a major concern in New York City due to high
percentage of fatalities. This study develops random parameter logit models for explaining …
percentage of fatalities. This study develops random parameter logit models for explaining …
Support vector machine in crash prediction at the level of traffic analysis zones: assessing the spatial proximity effects
In zone-level crash prediction, accounting for spatial dependence has become an
extensively studied topic. This study proposes Support Vector Machine (SVM) model to …
extensively studied topic. This study proposes Support Vector Machine (SVM) model to …
Exploring a Bayesian hierarchical approach for developing safety performance functions for a mountainous freeway
While rural freeways generally have lower crash rates, interactions between driver behavior,
traffic and geometric characteristics, and adverse weather conditions may increase the crash …
traffic and geometric characteristics, and adverse weather conditions may increase the crash …