Integrating Travel Behavior Forecasting and Generative Modeling for Predicting Future Urban Mobility and Spatial Transformations

2025-03-27

AI Summary

Traditional urban planning struggles to support long-term infrastructure decision-making due to the decoupling of transportation demand forecasting from spatial evolution prediction. To address this, we propose a population-driven and satellite-image-driven bidirectional collaborative forecasting framework. This work is the first to jointly integrate a Temporal Fusion Transformer (TFT) for modeling resident mobility behavior and a Generative Adversarial Network (GAN) for simulating urban spatial morphology evolution, enabled by multi-source spatiotemporal data fusion to achieve dynamic mutual feedback. Experimental results demonstrate strong predictive performance: an R² of 0.76 for trip demand forecasting and a Structural Similarity Index (SSIM) of 0.81 for synthetically generated satellite imagery. The framework significantly enhances the foresight and spatial adaptability of infrastructure planning, delivering interpretable, actionable, and quantitatively grounded decision support for sustainable long-term urban development.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Predicting long-term urban growth and transportation demands accurately
• Integrating travel behavior forecasting with generative urban modeling
• Improving infrastructure sustainability through data-driven decision-making

Innovation

• Temporal Fusion Transformer predicts travel patterns
• Generative Adversarial Network generates urban images
• Combines predictive analytics and spatial visualization