2 citations · 3 across the 6 of their papers we have counts for
6 papers
Learning from geometry-aware near misses to real-time COR: A corridor-wide grouped random parameters GEV framework
Mohammad Anis, Yang Zhou, Dominique Lord
Real-time corridor-wide crash-occurrence risk (COR) prediction is challenging because existing near-miss extreme value theory (EVT) models often oversimplify collision geometry, ne…
Simulating the Unseen: Crash Prediction Must Learn from What Did Not Happen
Zihao Li, Xinyuan Cao, Xiangbo Gao +12
Traffic safety science has long been hindered by a fundamental data paradox: the crashes we most wish to prevent are precisely those events we rarely observe. Existing crash-freque…
Why Anticipatory Sensing Matters in Commercial ACC Systems under Cut-In Scenarios: A Perspective from Stochastic Safety Analysis
Hao Zhang, Sixu Li, Zihao Li +3
This study presents an analytical solution for the vehicle state evolution of Adaptive Cruise Control (ACC) systems under cut-in scenarios, incorporating sensing delays and anticip…
Pedestrian crash causation analysis near bus stops: Insights from random parameters Negative Binomial-Lindley model
Mohammad Anis, Srinivas R. Geedipally, Dominique Lord
Pedestrian safety remains a pressing concern near bus stops along urban transit, where frequent pedestrian-vehicle interactions occur. While prior research has primarily focused on…
Real-time risk estimation for active road safety: Leveraging Waymo AV sensor data with hierarchical Bayesian extreme value models
Mohammad Anis, Sixu Li, Srinivas R. Geedipally +2
This study develops a real-time framework for estimating the risk of near-misses by using high-fidelity two-dimensional (2D) risk indicator time-to-collision (TTC), which is calcul…
Beyond 1D and oversimplified kinematics: A generic analytical framework for surrogate safety measures
Sixu Li, Mohammad Anis, Dominique Lord +3
This paper presents a generic analytical framework tailored for surrogate safety measures (SSMs) that is versatile across various highway geometries, capable of encompassing vehicl…