2024/03/05 by ChungYi Lin, Lin, ChungYi, Shen-Lung Tung +5
Engineering · Social Sciences · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Machine Learning (cs.LG) #Traffic Prediction and Management Techniques #Vehicular Ad Hoc Networks (VANETs)
paper · pdf · doi:10.48550/arxiv.2403.12991
openalex publication_date 2024/03/05 · openalex created_date 2024/03/23 · openalex updated_date 2026/07/28
Vehicle flow, a crucial indicator for transportation, is often limited by detector coverage. With the advent of extensive mobile network coverage, we can leverage mobile user activities, or cellular traffic, on roadways as a proxy for vehicle flow. However, as counts of cellular traffic may not directly align with vehicle flow due to data from various user types, we present a new task: predicting vehicle flow in camera-free areas using cellular traffic. To uncover correlations within multi-source data, we deployed cameras on selected roadways to establish the Tel2Veh dataset, consisting of extensive cellular traffic and sparse vehicle flows. Addressing this challenge, we propose a framework that independently extracts features and integrates them with a graph neural network (GNN)-based fusion to discern disparities, thereby enabling the prediction of unseen vehicle flows using cellular traffic. This work advances the use of telecom data in transportation and pioneers the fusion of telecom and vision-based data, offering solutions for traffic management.