Analyzing the importance of network topology in AADT estimation: insights from travel demand models using graph neural networks

Metropolitan traffic networks are becoming increasingly complex due to the growing population and diverse range of travel modes. However, the limited installation of continuous count stations leads to partially observable networks, posing a significant challenge for effective highway planning and design practices at various scales. Travel demand models have been developed and calibrated using sparse traffic counts at the metropolitan level. Nevertheless, these models are cumbersome to recalibrate and rerun whenever network changes occur. To overcome this challenge, we propose a flexible learning-based approach that extracts embedded knowledge from large-scale activity-based travel demand models to estimate Annual Average Daily Traffic (AADT). The approach offers two primary advantages: (1) directly learning network flow patterns based on segment attributes and network topology that can be transferred across regions, and (2) enabling efficient and reliable AADT estimation for projects of various scales. Our study explores a wide range of machine learning techniques, including novel graph neural networks that explicitly account for network topology, as well as modern and traditional regression and regression kriging models, which either disregard or implicitly consider network topology. We conducted extensive experiments using the loaded network data from the activity-based travel demand model for the Atlanta metropolitan area. Our findings underscore the importance of network topology in AADT estimation, with the diffusion graph convolutional network model demonstrating the best performance in both transductive and inductive settings. Additionally, modern tree ensemble models such as random forest regressor and CatBoost, despite their ignorance of network topology, show the second-best inductive performance with relatively lightweight structures.