87 citations · 307 across the 10 of their papers we have counts for
10 papers
Heterogeneous Ensemble Learning for Enhanced Crash Forecasts -- A Frequentest and Machine Learning based Stacking Framework
Numan Ahmad, Behram Wali, Asad J. Khattak
A variety of statistical and machine learning methods are used to model crash frequency on specific roadways with machine learning methods generally having a higher prediction accu…
Big Data Generated by Connected and Automated Vehicles for Safety Monitoring, Assessment and Improvement, Final Report (Year 3)
Asad J. Khattak, Iman Mahdinia, Sevin Mohammadi +2
This report focuses on safety aspects of connected and automated vehicles (CAVs). The fundamental question to be answered is how can CAVs improve road users' safety? Using advanced…
Harnessing Ambient Sensing & Naturalistic Driving Systems to Understand Links Between Driving Volatility and Crash Propensity in School Zones: A generalized hierarchical mixed logit framework
Behram Wali, Asad Khattak
With the advent of seemingly unstructured big data, and through seamless integration of computation and physical components, cyber-physical systems (CPS) provide an innovative way…
The relationship between driving volatility in time to collision and crash injury severity in a naturalistic driving environment
Behram Wali, Asad Khattak, Thomas Karnowski
As a key indicator of unsafe driving, driving volatility characterizes the variations in microscopic driving decisions. This study characterizes volatility in longitudinal and late…
Fuel Economy Gaps Within & Across Garages: A Bivariate Random Parameters Seemingly Unrelated Regression Approach
Behram Wali, Asad Khattak, David Greene +1
The key objective of this study is to investigate the interrelationship between fuel economy gaps and to quantify the differential effects of several factors on fuel economy gaps o…
Analysis of Volatility in Driving Regimes Extracted from Basic Safety Messages Transmitted Between Connected Vehicles
Asad Khattak, Behram Wali
Driving volatility captures the extent of speed variations when a vehicle is being driven. Extreme longitudinal variations signify hard acceleration or braking. Warnings and alerts…