MTDT: A Multi-Task Deep Learning Digital Twin

• URL: http://arxiv.org/abs/2405.00922v1
• Date: Thu, 2 May 2024 00:34:10 GMT
• Title: MTDT: A Multi-Task Deep Learning Digital Twin
• Authors: Nooshin Yousefzadeh, Rahul Sengupta, Yashaswi Karnati, Anand Rangarajan, Sanjay Ranka,
• Abstract summary: We introduce 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. By consolidating the learning process across multiple tasks, MTDT demonstrates reduced overfitting, increased efficiency, and enhanced effectiveness.
• Score: 8.600701437207725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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