Urban Expressway Traffic State Forecast via Graph Neural Network and LoRaWAN Communication

With the rapid expansion of smart cities, intelligent transportation systems (ITSs) are assuming an increasingly pivotal role. Among the multitude of tasks within ITS, traffic state forecasting stands out. As city boundaries grow, traffic forecasting encounters scalability and network transmission challenges. This research contributes to traffic state forecasting within large-scale, massive Internet of Things (IoT) scenarios. By investigating an urban expressway managing architecture that employs long range wide area network (LoRaWAN) communication, a novel deep learning-based model named Time Alignment-based temporal-graph attention network (TATGaN) is proposed. Using the temporal-graph attention mechanism, TATGaN is able to extract temporal-spatial information and predict traffic state accurately. Moreover, the time alignment block makes TATGaN capable of handling irregular sequences given by the asynchronous arrival of packets. Simulation results based on OpenStreetMap data of a specific region within Abu Dhabi show that TATGaN outperforms existing baseline methods in prediction performance with lower error and the higher reliability. Furthermore, the performance evaluation demonstrates the suitability of TATGaN for large-scale traffic network scenarios, attributing its efficiency to the transmission schedule and parameter mechanisms in LoRaWAN networks.