Summary
Asia-Pacific Network Operations and Management Symposium
2022
Session Number:TS8
Session:
Number:TS8-03
Multi-Class Traffic Density Forecasting in IoV using Spatio-Temporal Graph Neural Networks
Asif Mehmood, Talha Ahmed Khan, Afaq Muhammad, Wang-Cheol Song,
pp.-
Publication Date:2022/09/28
Online ISSN:2188-5079
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Summary:
Internet of Vehicles (IoV) is an emerging archetype that is a distributed network of various vehicles armed with sensors, actuators, technologies, and applications to connect and exchange data with each other over the Internet. The primary goal of IoV is to provide a vehicular platform to enable better communication and Quality of Service (QoS) for vehicles, pedestrians, and roadside infrastructure in real-time. Various services e.g., video streaming, vehicle parking, and nearby charging stations etc., are served over various channels such as Vehicle-to-Vehicle (V2V), Vehicle-to-Pedestrian (V2P), Vehicle-to-Infrastructure (V2I), Vehicle-to-Network (V2N), and Vehicle-to-Cloud (V2C). However, the increasing number of IoV network channels and services pose serious concerns and challenges to researchers and architects. These challenges include real-time traffic forecasting, service placement, security, reliability, and routing. The primary focus of this work is concerned with the challenge of multi-regional forecasting of multi-class traffic. These traffic forecasting models enable pro-activeness in systems by the provision of real-time and accurate predictions. Also, they can explore traffic densities over spatial and temporal domains. for various vehicle types. However, current literature lacks the ability to provide forecasts for multi-region and multi-class vehicles at the same time. This study aims at enabling a proactive platform which could make decisions based on the integrated Graph Neural Network (GNN) and Gated Recurrent Unit (GRU) based traffic forecasting model, i.e., Spatio-Temporal GNN (STGNN) based traffic forecasting model. In the STGNN based traffic forecasting model, the GNN and GRU models are utilized for the exploration of spatial and temporal features of varying vehicular multi-class traffic densities. In GNN, spatial data consisting of multi-class traffic densities are utilized for the feature extraction that results in graph embeddings. In the GRU, these graph embeddings are utilized for the temporal feature extraction. This approach enables the forecasting of multi-class vehicular traffic densities and the pro-activeness into an IoV platform. In addition, the performance results show that an intelligent platform can be built upon the proposed traffic forecasting model that is capable of inspecting complex and nonlinear traffic accurately.