用实时人流数据生成城市活力嵌入，提升交通预测精度。
Urban Vibrancy Embedding and Application on Traffic Prediction
- 用变分自编码器压缩人流数据，生成可解释的城市活力嵌入。
- 结合VAE与LSTM，实现对未来城市活力的动态预测。
- 在多个模型上验证有效，适合城市规划与智能交通研究者。
城市活力反映城市空间中的人类活动动态，通常通过捕捉流动人口趋势的移动数据来衡量。本研究提出一种新方法，从实时流动人口数据中提取城市活力嵌入，以增强交通预测模型。具体而言，我们使用变分自编码器（VAE）将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，预测未来嵌入。这些嵌入随后被用于序列到序列框架进行交通预测。主要贡献包括：(1) 利用主成分分析（PCA）解析嵌入，揭示工作日与周末差异、季节性等时间模式；(2) 提出一种融合VAE与LSTM的方法，实现动态城市知识嵌入的预测；(3) 在RNN、DCRNN、GTS和GMAN等模型上均提升了预测准确性和响应速度。研究表明，城市活力嵌入有助于推进交通预测，并提供更细致的城市流动性分析。
原文摘要 · Abstract (English)
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.
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