  Traffic congestion has significant impacts on both the economy and the
environment. Measures of Effectiveness (MOEs) have long been the standard for
evaluating the level of service and operational efficiency of traffic
intersections. However, the scarcity of traditional high-resolution loop
detector data (ATSPM) presents challenges in accurately measuring MOEs or
capturing the intricate temporospatial characteristics inherent in urban
intersection traffic. In response to this challenge, we have introduced the
Multi-Task Deep Learning Digital Twin (MTDT) as a solution for multifaceted and
precise intersection traffic flow simulation. MTDT enables accurate,
fine-grained estimation of loop detector waveform time series for each lane of
movement, alongside successful estimation of several MOEs for each lane group
associated with a traffic phase concurrently and for all approaches of an
arbitrary urban intersection. Unlike existing deep learning methodologies, MTDT
distinguishes itself through its adaptability to local temporal and spatial
features, such as signal timing plans, intersection topology, driving
behaviors, and turning movement counts. While maintaining a straightforward
design, our model emphasizes the advantages of multi-task learning in traffic
modeling. By consolidating the learning process across multiple tasks, MTDT
demonstrates reduced overfitting, increased efficiency, and enhanced
effectiveness by sharing representations learned by different tasks.
Furthermore, our approach facilitates sequential computation and lends itself
to complete parallelization through GPU implementation. This not only
streamlines the computational process but also enhances scalability and
performance.
