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.

使用移动通信流量数据预测车辆流量，通过提取特征并将其与图神经网络结合，使得在没有摄像头的区域能够预测车辆流量，为交通管理提供了解决方案。

Tel2Veh: 融合电信数据和车辆流量的时空框架用于预测无需摄像头的交通

Tel2Veh: Fusion of Telecom Data and Vehicle Flow to Predict Camera-Free  Traffic via a Spatio-Temporal Framework

Cellular traffic prediction is an indispensable part for intelligent
telecommunication networks. Nevertheless, due to the frequent user mobility and
complex network scheduling mechanisms, cellular traffic often inherits
complicated spatial-temporal patterns, making the prediction incredibly
challenging. Although recent advanced algorithms such as graph-based prediction
approaches have been proposed, they frequently model spatial dependencies based
on static or dynamic graphs and neglect the coexisting multiple spatial
correlations induced by traffic generation. Meanwhile, some works lack the
consideration of the diverse cellular traffic patterns, result in suboptimal
prediction results. In this paper, we propose a novel deep learning network
architecture, Adaptive Hybrid Spatial-Temporal Graph Neural Network (AHSTGNN),
to tackle the cellular traffic prediction problem. First, we apply adaptive
hybrid graph learning to learn the compound spatial correlations among cell
towers. Second, we implement a Temporal Convolution Module with multi-periodic
temporal data input to capture the nonlinear temporal dependencies. In
addition, we introduce an extra Spatial-Temporal Adaptive Module to conquer the
heterogeneity lying in cell towers. Our experiments on two real-world cellular
traffic datasets show AHSTGNN outperforms the state-of-the-art by a significant
margin, illustrating the superior scalability of our method for
spatial-temporal cellular traffic prediction.

本研究提出了一种名为自适应混合时空图神经网络 (AHSTGNN) 的网络架构，用于解决基于时空模式的移动通信网络中的流量预测问题。实验结果表明，AHSTGNN 模型在两个真实的移动通信数据集上性能优于现有的预测方法。