Automated Vehicles (AV) pose new challenges in road safety, multimodal
interaction, and urban planning, requiring a holistic approach that prioritizes
sustainability and protects all road users. The KASSA.AST project addresses this
by deploying and evaluating an automated shuttle in southern Austria on three
routes. The study area is a Park & Ride zone near a train station, enabling
seamless transfers and higher transit use. To assess the safety impacts of the
automated shuttle, four Mobility Observation Boxes (MOBs) were deployed. These
AI-based systems detect and classify road users, track their trajectories and
geospatial coordinates, and identify safety-critical events via Surrogate Safety
Measures (SSMs). Over 10 days, a trajectory dataset captured interactions among
vehicles and the shuttle. The resulting real-world dataset is a core
contribution. This dataset underpins microscopic behavior modeling. Trajectory
pairs yield car-following and interaction metrics (relative distance, relative
speed, acceleration) to calibrate custom models for realistic mixed traffic.
Simulations generate a structured interaction database with time spans,
trajectories, conflict points, and SSMs (such as Time-to
Collision—TTC, Post-Encroachment Time—PET,
and Deceleration-rate-to-avoid-crash—DRAC). These outputs
support detailed analysis of shuttle interactions, including near misses. To
reveal patterns, clustering identified three interpretable safety-relevant
regimes: (i) a low-demand background regime (n = 96) with low
speeds and near-zero deceleration demand, (ii) a fast-and-tight regime
(n = 33) with reduced TTC, elevated
critical-event speeds, and high DRAC/Modified
(M)DRAC demand, and (iii) an AV-regulated regime
(n = 10) dominated by the shuttle as adversary, showing
short TTC but stable moderate speeds (~4 m/s) and conservative
headway policies. Ensemble-tree supervised learning reproduced these regimes
with high accuracy and revealed that critical-event speeds and counterpart
headway are the strongest discriminators, while AV role metadata contributes
marginally. This integrated approach—linking field data, behavior modeling,
simulation, and machine learning—provides a robust framework for assessing AV
safety in urban contexts.
