  Event cameras, with their high temporal and dynamic range and minimal memory
usage, have found applications in various fields. However, their potential in
static traffic monitoring remains largely unexplored. To facilitate this
exploration, we present eTraM - a first-of-its-kind, fully event-based traffic
monitoring dataset. eTraM offers 10 hr of data from different traffic scenarios
in various lighting and weather conditions, providing a comprehensive overview
of real-world situations. Providing 2M bounding box annotations, it covers
eight distinct classes of traffic participants, ranging from vehicles to
pedestrians and micro-mobility. eTraM's utility has been assessed using
state-of-the-art methods for traffic participant detection, including RVT, RED,
and YOLOv8. We quantitatively evaluate the ability of event-based models to
generalize on nighttime and unseen scenes. Our findings substantiate the
compelling potential of leveraging event cameras for traffic monitoring,
opening new avenues for research and application. eTraM is available at
https://eventbasedvision.github.io/eTraM
