Digital Twin-based Driver Risk-Aware Intelligent Mobility Analytics for Urban Transportation Management

TL;DR

DT-DIMA presents a digital twin framework that jointly models mobility and safety risk in urban transportation by integrating multi-PTC sensing with driver-informed predictive analytics. The architecture combines STTE for network-wide traffic estimation, MTSS for mesoscopic safety simulation, and LSTT for adaptive twinning, all coordinated by RiCCOL for risk-aware PTC tilting. Experiments in a Brooklyn testbed show DT-DIMA achieving $MAPE$ levels suitable for proactive management, with mobility $MAPE$ $[8.40\%,15.11\%]$ and safety $MAPE$ $[0.85\%,12.97\%]$, and a roughly 5-minute lead time in incident awareness. The work demonstrates the practical value of driver-centric, predictive, and proactive surveillance in urban mobility management, suggesting broader applicability with expanded sensing and control modalities.

Abstract

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.

Figures (14)

• Figure 1: Feedback loops at stakeholder and modeling levels in the mobility management process
• Figure 2: Architecture of overall framework of DT-DIMA. Major components in this figure, STTE: Spatio-Temporal Traffic Estimation, MTSS: Mesoscopic Traffic Safety Simulation, LSTT: Long-Short Term Twinning, RiCCOL: Risk-Constrained Correlated Online Learning.
• Figure 3: Workflow of DT-DIMA in real-time operation
• Figure 4: Workflow of driver-informed predictive service
• Figure 5: The spatio-temporal traffic estimation (STTE) component