A Memory-augmented Conditional Neural Process model for traffic prediction

Abstract

This paper presents the first neural process-based model for traffic prediction, the Memory-augmented Conditional Neural Process (MemCNP). Spatio-temporal traffic prediction involves predicting future traffic patterns based on historical traffic data and the road network structure. This problem remains a challenge due to the dynamic and heterogeneous nature of urban traffic. Existing models often struggle to capture these complexities, particularly in data-limited scenarios. To address these limitations, our model presents a novel framework for uncertainty estimation based on the conditional neural process, and further incorporates a memory network module designed to acquire a representative contextual reference, thereby improving model performance under complex data distributions. By integrating the conditional neural process and the memory network, MemCNP enables the learning of the most representative contexts through iterative updates, enhancing the model’s generalisability. This allows our model to be applicable beyond car traffic, effectively handling diverse real-world traffic scenarios, including urban non-motorised traffic such as cycling, which is essential for advancing more sustainable transportation systems. This is demonstrated by comprehensive experimental results on six benchmark datasets (PeMS04, PeMS07, PeMS08, NYCTaxi, CHIBike, and T-Drive) against existing state-of-the-art traffic prediction models, where MemCNP demonstrates superior performance. Additionally, through ablation and reliability studies, we provide a comprehensive analysis of the model’s effectiveness.