Urban Vibrancy Embedding and Application on Traffic Prediction

TL;DR

A novel approach to derive Urban Vibrancy embeddings from real-time floating population data to enhance traffic prediction models and offer a more nuanced analysis of urban mobility.

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 and offer a more nuanced analysis of urban mobility.

Figures (6)

• Figure 1: Embedding Urban Vibrancy and Knowledge Adaption on Traffic Prediction.
• Figure 2: Variational Autoencoder
• Figure 3: UVE-Seq2Seq: Forecasting Future Urban Vibrancy Embedding.
• Figure 4: Comparison of Urban Vibrancy Embedding on Weekdays and Weekends
• Figure 5: Land Use of our Research Area