  Traditional mobility management strategies emphasize macro-level mobility
oversight from traffic-sensing infrastructures, often overlooking safety risks
that directly affect road users. To address this, we propose a Digital
Twin-based Driver Risk-Aware Intelligent Mobility Analytics (DT-DIMA) system.
The DT-DIMA system integrates real-time traffic information from
pan-tilt-cameras (PTCs), synchronizes this data into a digital twin to
accurately replicate the physical world, and predicts network-wide mobility and
safety risks in real time. The system's innovation lies in its integration of
spatial-temporal modeling, simulation, and online control modules. Tested and
evaluated under normal traffic conditions and incidental situations (e.g.,
unexpected accidents, pre-planned work zones) in a simulated testbed in
Brooklyn, New York, DT-DIMA demonstrated mean absolute percentage errors
(MAPEs) ranging from 8.40% to 15.11% in estimating network-level traffic volume
and MAPEs from 0.85% to 12.97% in network-level safety risk prediction. In
addition, the highly accurate safety risk prediction enables PTCs to
preemptively monitor road segments with high driving risks before incidents
take place. Such proactive PTC surveillance creates around a 5-minute lead time
in capturing traffic incidents. The DT-DIMA system enables transportation
managers to understand mobility not only in terms of traffic patterns but also
driver-experienced safety risks, allowing for proactive resource allocation in
response to various traffic situations. To the authors' best knowledge, DT-DIMA
is the first urban mobility management system that considers both mobility and
safety risks based on digital twin architecture.
