In today’s urban landscape, traffic congestion poses a critical challenge, especially during outlier scenarios. These outliers can indicate abrupt traffic peaks, drops, or irregular trends, often arising from factors such as accidents, events, or roadwork. Moreover, given the dynamic nature of traffic, the need for real-time traffic modeling also becomes crucial to ensure accurate and up-to-date traffic predictions. To address these challenges, we introduce the Outlier Weighted-Autoencoder Modeling (OWAM) framework. OWAM employs autoencoders for local outlier detection and generates correlation scores to assess neighboring traffic’s influence. These scores serve as weighted factors for neighboring sensors, before fusing them into the model. This integration enhances the traffic model’s performance and supports effective real-time updates, a crucial aspect for capturing dynamic traffic patterns. OWAM demonstrates a favorable trade-off between accuracy and efficiency, rendering it highly suitable for real-world applications. The findings contribute directly to an industrial application but also to the development of more efficient and adaptive traffic prediction systems. The code and datasets of our framework are publicly available at https://github.com/himanshudce/OWAM .