Data-Driven Strategies in Energy, Transportation, and Urban Planning: Challenges and Opportunities

Xu Liu

doi:10.71222/x0zmth39

Authors

Xu Liu University of Wisconsin–Milwaukee, Milwaukee, WI, USA Author

DOI:

https://doi.org/10.71222/x0zmth39

Keywords:

data-driven strategies, urban planning, energy systems, transportation networks, multi-source data, predictive modeling

Abstract

Data-driven strategies have become essential for optimizing complex systems in energy, transportation, and urban planning. By integrating advanced machine learning techniques, real-time sensing, behavioral analytics, and multi-source data, these approaches enable predictive modeling, adaptive resource allocation, and evidence-based decision-making. Applications range from carbon-aware energy optimization and resilience planning to personalized transportation services and smart urban infrastructure management. Despite challenges such as data quality, model interpretability, and behavioral variability, the convergence of multi-modal data, optimization frameworks, and socio-behavioral insights provides a roadmap for sustainable, efficient, and resilient urban systems. This review synthesizes recent methodologies, applications, and challenges, highlighting opportunities for future research and policy development in data-driven urban and energy systems.

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