Srinivas R. Geedipally

arXiv2025

Pedestrian crash causation analysis near bus stops: Insights from random parameters Negative Binomial-Lindley model

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 intersections and midblock locations, bus stops have often been treated as secondary contributors rather than as distinct sites requiring targeted safety assessments. This has left a critical gap in understanding how traffic exposure, roadway characteristics, and bus stop design features specifically influence pedestrian crash risks around bus stop locations. To address these gaps, this study develops a comprehensive framework focused on pedestrian safety in the vicinity of bus stops. The proposed approach employs a Random Parameters Negative Binomial-Lindley (RPNB-L) model to account for unobserved heterogeneity and site-specific variability.

stat.AP↗

arXiv2024

Real-time risk estimation for active road safety: Leveraging Waymo AV sensor data with hierarchical Bayesian extreme value models

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 calculated from high-resolution data collected by autonomous vehicles (AVs). The framework utilizes extreme value theory (EVT) to derive near-miss risk based on observed TTC data. Most existing studies employ a generalized extreme value (GEV) distribution for specific sites and conflict types and often overlook individual vehicle dynamics heterogeneity. This framework is versatile across various highway geometries and can encompass vehicle dynamics and fidelity by incorporating covariates such as speed, acceleration, steering angle, and heading. This makes the risk estimation framework suitable for dynamic, real-world traffic environments. The dataset for this study is derived from Waymo perception data, encompassing six sites across three cities: San Francisco, Phoenix, and Los Angeles. Vehicle trajectory data were extracted from the dataset, and near-miss frequencies were calculated using high-fidelity 2D TTC. The crash risk was derived from observed near misses using four hierarchical Bayesian GEV models, explicitly focusing on conflicting pairs as block minima (BM), which revealed that crash risk varies across pairs.The proposed framework is efficient using a hierarchical Bayesian structure random parameter (HBSRP) model, offering superior statistical performance and flexibility by accounting for unobserved heterogeneity across sites. The study identifies and quantifies that the most hazardous conditions involve conflicting vehicle speeds and rapid acceleration and deceleration, significantly increasing crash risk in urban arterials.

stat.AP↗