Enhancing Traffic Flow Prediction using Outlier-Weighted AutoEncoders:
  Handling Real-Time Changes
Himanshu Choudhary Marwan Hassani
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 a weighted factor
for neighboring sensors, before fusing them into the model. This information
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 research findings
contribute significantly to the development of more efficient and adaptive
traffic prediction models, advancing the field of transportation management for
the future. The code and datasets of our framework is publicly available under
https://github.com/himanshudce/OWAM.