Traffic forecasting is a crucial aspect of modern Intelligent Transportation Systems (ITS) and the Internet of Vehicles (IoV), playing a vital role in improving the safety and efficiency of daily transportation activities. Despite the valuable contributions of traditional machine learning (ML) models and advanced deep learning (DL) techniques, there persist challenges in capturing the intricate spatial and temporal dependencies inherent in traffic flow. In response to these challenges, we present GRAPHITE, an innovative framework that combines Graph Neural Networks (GNNs) and Generative Adversarial Networks (GANs) to leverage generative reasoning for efficient traffic management. Our model seamlessly integrates historical traffic volume data collected by road sensors with local spatial information encoded through knowledge graphs (KGs) associated with each sensor. These KGs offer a structured representation of relationships between traffic sensors and points of interest (POIs) in their neighborhood, thereby enhancing the comprehension of the urban context and leading to more accurate traffic predictions. Extensive experiments conducted on diverse datasets underscore the efficacy of GRAPHITE. Notably, we achieved a maximum decrease in RMSE of 31.05% compared to GANGRU and a maximum increase in R² of 8.15% compared to GAN-RNN, positioning GRAPHITE as a standout solution among the current state-of-the-art approaches.

Figure: GRAPHITE framework. Traffic volume is gathered from sensors which are localized using geographical coordinates (latitude, longitude). Traffic time series undergo decomposition into three components: trend, seasonal, and residual, while starting from the sensors’ coordinates, two knowledge graphs are constructed by extracting POIs around them by means of OSM. The GAN model is subsequently fed with the both kind of data. Specifically, the generator dissects the provided information, comprising a spatial component based on GCN and a temporal component relying on GRU and LSTM. Meanwhile, the discriminator, incorporating Conv1D and Fully Connected (FC) layers, discerns between the generated and authentic traffic data. Finally, the model yields predictions for traffic flow.

📄 Published in: Information Fusion
🔗 DOI: 10.1016/j.inffus.2024.101065

If you use this work in your research, please cite:

Canzaniello, M., Piccialli, F., Longo, A., Izzo, S., & Chiaro, D. (2024). GRAPHITE: Generative Reasoning and Analysis for Predictive Handling in Traffic Efficiency. Information Fusion, 101065. https://doi.org/10.1016/j.inffus.2024.101065

• PNRR project FAIR - Future AI Research (PE00000013), Spoke 3, under the NRRP MUR program funded by the NextGenerationEU.
• G.A.N.D.A.L.F. - Gan Approaches for Non-iiD Aiding Learning in Federations, CUP: E53D23008290006, PNRR - Missione 4 “Istruzione e Ricerca” - Componente C2 Investimento 1.1 “Fondo per il Programma Nazionale di Ricerca e Progetti di Rilevante Interesse Nazionale (PRIN)”.
• PNRR Centro Nazionale HPC, Big Data e Quantum Computing, (CN_00000013)(CUP: E63C22000980007), under the NRRP MUR program funded by the NextGenerationEU.