用时空模型与生成网络预测城市出行和空间变化，提升规划精准度。
Integrating Travel Behavior Forecasting and Generative Modeling for Predicting Future Urban Mobility and Spatial Transformations
- 结合时序融合变换器与生成对抗网络，从人口数据和卫星图像中预测出行与城市形态。
- 出行预测R²达0.76，生成图像结构相似度达0.81，质量高。
- 适合城市规划、交通政策制定者，助力可持续基础设施布局。
交通规划在塑造城市发展、经济流动性和基础设施可持续性方面至关重要。然而，传统规划方法往往难以准确预测长期城市增长和交通需求，有时甚至导致为满足当前规划而拆除现有基础设施。本研究将时间融合变压器（Temporal Fusion Transformer）用于从人口数据中预测出行模式，并结合生成对抗网络（Generative Adversarial Network）通过卫星图像预测未来城市形态。该框架在出行行为预测中达到0.76的R²得分，生成的卫星图像具有0.81的结构相似性指数，表明其具备高保真度。结果表明，整合预测分析与空间可视化可显著提升决策质量，推动更可持续、高效的城镇化发展。本研究强调了数据驱动方法在现代交通规划中的重要性，并为优化基础设施布局、容量及长期可行性迈出关键一步。
原文摘要 · Abstract (English)
Transportation planning plays a critical role in shaping urban development, economic mobility, and infrastructure sustainability. However, traditional planning methods often struggle to accurately predict long-term urban growth and transportation demands. This may sometimes result in infrastructure demolition to make room for current transportation planning demands. This study integrates a Temporal Fusion Transformer to predict travel patterns from demographic data with a Generative Adversarial Network to predict future urban settings through satellite imagery. The framework achieved a 0.76 R-square score in travel behavior prediction and generated high-fidelity satellite images with a Structural Similarity Index of 0.81. The results demonstrate that integrating predictive analytics and spatial visualization can significantly improve the decision-making process, fostering more sustainable and efficient urban development. This research highlights the importance of data-driven methodologies in modern transportation planning and presents a step toward optimizing infrastructure placement, capacity, and long-term viability.
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