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[2602.21232] Urban Vibrancy Embedding and Application on Traffic Prediction
Summary
This paper presents a novel method for traffic prediction using Urban Vibrancy embeddings derived from real-time population data, enhancing existing models through advanced machine learning techniques.
Why It Matters
As urban areas continue to grow, understanding and predicting traffic patterns becomes crucial for city planning and management. This research introduces innovative methodologies that leverage real-time data, potentially leading to more efficient urban mobility solutions and improved traffic management systems.
Key Takeaways
- Urban Vibrancy embeddings can enhance traffic prediction accuracy.
- The combination of VAE and LSTM models allows for dynamic forecasting.
- Principal component analysis reveals significant temporal patterns in urban mobility.
Computer Science > Machine Learning arXiv:2602.21232 (cs) [Submitted on 7 Feb 2026] Title:Urban Vibrancy Embedding and Application on Traffic Prediction Authors:Sumin Han, Jisun An, Dongman Lee View a PDF of the paper titled Urban Vibrancy Embedding and Application on Traffic Prediction, by Sumin Han and 2 other authors View PDF HTML (experimental) Abstract:Urban vibrancy reflects the dynamic human activity within urban spaces and is often measured using mobile data that captures floating population trends. This study proposes a novel approach to derive Urban Vibrancy embeddings from real-time floating population data to enhance traffic prediction models. Specifically, we utilize variational autoencoders (VAE) to compress this data into actionable embeddings, which are then integrated with long short-term memory (LSTM) networks to predict future embeddings. These are subsequently applied in a sequence-to-sequence framework for traffic forecasting. Our contributions are threefold: (1) We use principal component analysis (PCA) to interpret the embeddings, revealing temporal patterns such as weekday versus weekend distinctions and seasonal patterns; (2) We propose a method that combines VAE and LSTM, enabling forecasting dynamic urban knowledge embedding; and (3) Our approach improves accuracy and responsiveness in traffic prediction models, including RNN, DCRNN, GTS, and GMAN. This study demonstrates the potential of Urban Vibrancy embeddings to advance traffic prediction an...
