Urban Traffic Forecasting with Integrated Travel Time and Data Availability in a Conformal Graph Neural Network Framework

TL;DR

This paper introduces a novel graph neural network framework that integrates travel times and data availability, employing conformal prediction to improve traffic flow forecasting accuracy and uncertainty quantification.

Contribution

It presents a new GNN-based model incorporating travel times and data availability, combined with adaptive conformal prediction for uncertainty estimation in traffic forecasting.

Findings

Model outperformed competitors by 24% in MAE

Achieved 8% improvement in RMSE

Travel time distribution closely matches real-world data

Abstract

Traffic flow prediction is a big challenge for transportation authorities as it helps plan and develop better infrastructure. State-of-the-art models often struggle to consider the data in the best way possible, as well as intrinsic uncertainties and the actual physics of the traffic. In this study, we propose a novel framework to incorporate travel times between stations into a weighted adjacency matrix of a Graph Neural Network (GNN) architecture with information from traffic stations based on their data availability. To handle uncertainty, we utilized the Adaptive Conformal Prediction (ACP) method that adjusts prediction intervals based on real-time validation residuals. To validate our results, we model a microscopic traffic scenario and perform a Monte-Carlo simulation to get a travel time distribution for a Vehicle Under Test (VUT), and this distribution is compared against the real-world data. Experiments show that the proposed model outperformed the next-best model by approximately 24% in MAE and 8% in RMSE and validation showed the simulated travel time closely matches the 95th percentile of the observed travel time value.