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.
Abstract (translated)
城市活力反映了城市空间内动态的人类活动，通常使用移动数据来捕捉流动人口趋势来进行衡量。本研究提出了一种新的方法，从实时浮动人口数据中推导出城市活力嵌入（Urban Vibrancy embeddings），以增强交通预测模型的性能。具体来说，我们利用变分自编码器(VAE)将这些数据压缩为可操作的嵌入，并将其与长短期记忆网络(LSTM)结合使用来预测未来的嵌入。然后在序列到序列框架中应用这些未来嵌入进行交通预测。 本研究的主要贡献包括： 1. 我们利用主成分分析(PCA)来解释这些嵌入，揭示了诸如工作日和周末区别以及季节性模式等时间模式。 2. 提出了一种结合VAE和LSTM的方法，能够预报动态的城市知识嵌入。 3. 我们的这种方法提高了包括RNN、DCRNN、GTS及GMAN在内的交通预测模型的准确性和响应能力。 这项研究展示了城市活力嵌入在推进交通预测方面以及对城市流动性进行更细致分析方面的潜力。
URL
https://arxiv.org/abs/2602.21232
