  In this study, a digital twin (DT) technology based Adaptive Traffic Signal
Control (ATSC) framework is presented for improving signalized intersection
performance and user satisfaction. Specifically, real-time vehicle trajectory
data, future traffic demand prediction and parallel simulation strategy are
considered to develop two DT-based ATSC algorithms, namely DT1 (Digital Twin 1)
and DT2 (Digital Twin 2). DT1 uses the delay experienced by each vehicle from
all approaches connected to the subject intersection, while DT2 uses the delay
of each vehicle that occurred in all the approaches connected to the subject
intersection as well as immediate adjacent intersection. To demonstrate the
effectiveness of these algorithms, the DT-based ATSC algorithms are evaluated
with varying traffic demands at intersection, and individual user level.
Evaluation results show that both DT1 and DT2 performs significantly better
compared to the density-based baseline algorithm in terms of control delay
reductions ranging from 1% to 52% for low traffic demands. DT1 outperforms
baseline algorithm for moderate traffic demands, achieving reduction in control
delay ranging from 3% to 19%, while the performance of DT2 declines with
increasing demand. For high traffic demands, DT1 achieved control delay
reduction ranging from 1% to 45% and DT2 achieved 8% to 36% compared to the
baseline algorithm. Moreover, DT1 and DT2 effectively distribute the delay per
vehicle among all the vehicles, which approach towards intersection, compared
to the baseline ATSC algorithm. This helps to improve user satisfaction by
reducing prolonged delays at a traffic signal, specifically, for moderate and
high traffic demands.
