  While measures, such as traffic calming and advance driver assistance
systems, can improve safety for Vulnerable Road Users (VRUs), their
effectiveness ultimately relies on the responsible behavior of drivers and
pedestrians who must adhere to traffic rules or take appropriate actions.
However, these measures offer no solution in scenarios where a collision
becomes imminent, leaving no time for warning or corrective actions. Recently,
connected vehicle technology has introduced warning services that can alert
drivers and VRUs about potential collisions. Nevertheless, there is still a
significant gap in the system's ability to predict collisions in advance. The
objective of this study is to utilize Digital Twin (DT) technology to enable a
proactive safety alert system for VRUs. A pedestrian-vehicle trajectory
prediction model has been developed using the Encoder-Decoder Long Short-Term
Memory (LSTM) architecture to predict future trajectories of pedestrians and
vehicles. Subsequently, parallel evaluation of all potential future
safety-critical scenarios is carried out. Three Encoder-Decoder LSTM models,
namely pedestrian-LSTM, vehicle-through-LSTM, and vehicle-left-turn-LSTM, are
trained and validated using field-collected data, achieving corresponding root
mean square errors (RMSE) of 0.049, 1.175, and 0.355 meters, respectively. A
real-world case study has been conducted where a pedestrian crosses a road, and
vehicles have the option to proceed through or left-turn, to evaluate the
efficacy of DT-enabled proactive safety alert systems. Experimental results
confirm that DT-enabled safety alert systems were succesfully able to detect
potential crashes and proactively generate safety alerts to reduce potential
crash risk.
