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In today's urban landscape, traffic congestion poses a critical challenge,especially during outlier scenarios. These outliers can indicate abrupt trafficpeaks, drops, or irregular trends, often arising from factors such asaccidents, events, or roadwork. Moreover, Given the dynamic nature of traffic,the need for real-time traffic modeling also becomes crucial to ensure accurateand up-to-date traffic predictions. To address these challenges, we introducethe Outlier Weighted Autoencoder Modeling (OWAM) framework. OWAM employsautoencoders for local outlier detection and generates correlation scores toassess neighboring traffic's influence. These scores serve as a weighted factorfor neighboring sensors, before fusing them into the model. This informationenhances the traffic model's performance and supports effective real-timeupdates, a crucial aspect for capturing dynamic traffic patterns. OWAMdemonstrates a favorable trade-off between accuracy and efficiency, renderingit highly suitable for real-world applications. The research findingscontribute significantly to the development of more efficient and adaptivetraffic prediction models, advancing the field of transportation management forthe future. The code and datasets of our framework is publicly available underhttps://github.com/himanshudce/OWAM.

Enhancing Traffic Flow Prediction using Outlier-Weighted AutoEncoders: Handling Real-Time Changes