Mechanism-Driven Explainable Urban Spatio-Temporal Prediction (Big Data Management) - Hardcover

Wang, Jingyuan; Ji, Jiahao

Hardcover

ISBN 10: 9819206618 ISBN 13: 9789819206612

Verlag: Springer, 2026

Inhaltsangabe

Urban environments generate massive streams of spatio-temporal data, yet accurately predicting urban dynamics remains a fundamental challenge due to complex human mobility patterns, evolving environmental conditions, and distributional shifts across time and space. Mechanism-Driven Explainable Urban Spatio-Temporal Prediction offers a comprehensive and innovative framework that integrates physical mechanisms, causal modeling, and information-theoretic principles into modern deep learning methods, enabling more interpretable, reliable, and generalizable spatio-temporal forecasting.

This monograph presents a unified perspective across intrinsic and extrinsic factors that shape urban mobility. It introduces a gravity-inspired potential energy field model to capture intrinsic behavioral mechanisms at both regional and road-network scales, bridging discrete and continuous temporal modeling through differential equation networks. Beyond intrinsic mechanisms, the book proposes a causal basis-vector representation to model spatio-temporal distribution shifts caused by unknown confounders, enhancing robustness under varying scenarios. Furthermore, it develops a theoretically grounded information-theoretic decomposition framework that reduces the complexity of mixed urban data distributions and pushes the predictive performance beyond existing limits.

Combining theoretical foundations, methodological innovations, and extensive empirical studies on real-world urban traffic datasets, this book provides a rigorous yet accessible resource for researchers in spatio-temporal modeling, intelligent transportation systems, machine learning, and urban computing. It also serves as a valuable reference for practitioners seeking interpretable and mechanism-aware prediction models for smart city applications.

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Über die Autorin bzw. den Autor

Jingyuan Wang received the Ph.D. degree from the Department of Computer Science and Technology, Tsinghua University. He is currently a Professor at School of Computer Science and Engineering, and School of Economics and Management, Beihang University. He is also the head of the Beihang Interest Group on SmartCity (BIGSCity), and Vice Director of the Beijing City Lab (BCL). His general area of research is data mining and machine learning, with special interests in smart cities and spatiotemporal data analytics. He received the various national awards including NSFC Excellent Youth, Youth Beijing Scholar, and first prize of technical invention of Ministry of Education.

Jiahao Ji is currently a researcher with Meituan, Beijing, China. He received his Ph.D. and B.E. degrees from the School of Computer Science and Engineering, Beihang University, in 2025 and 2019, respectively. His general area of research is spatiotemporal data mining, interpretable machine learning, and urban computing. He was awarded the China National Scholarship in 2023, and the China MIIT Innovation and Entrepreneurship Scholarship in 2019. He won the First Prize of the ASC Student Supercomputer Challenge in 2018.

 

Von der hinteren Coverseite

Urban environments generate massive streams of spatio-temporal data, yet accurately predicting urban dynamics remains a fundamental challenge due to complex human mobility patterns, evolving environmental conditions, and distributional shifts across time and space. Mechanism-Driven Explainable Urban Spatio-Temporal Prediction offers a comprehensive and innovative framework that integrates physical mechanisms, causal modeling, and information-theoretic principles into modern deep learning methods, enabling more interpretable, reliable, and generalizable spatio-temporal forecasting.

This monograph presents a unified perspective across intrinsic and extrinsic factors that shape urban mobility. It introduces a gravity-inspired potential energy field model to capture intrinsic behavioral mechanisms at both regional and road-network scales, bridging discrete and continuous temporal modeling through differential equation networks. Beyond intrinsic mechanisms, the book proposes a causal basis-vector representation to model spatio-temporal distribution shifts caused by unknown confounders, enhancing robustness under varying scenarios. Furthermore, it develops a theoretically grounded information-theoretic decomposition framework that reduces the complexity of mixed urban data distributions and pushes the predictive performance beyond existing limits.

Combining theoretical foundations, methodological innovations, and extensive empirical studies on real-world urban traffic datasets, this book provides a rigorous yet accessible resource for researchers in spatio-temporal modeling, intelligent transportation systems, machine learning, and urban computing. It also serves as a valuable reference for practitioners seeking interpretable and mechanism-aware prediction models for smart city applications.

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2026
• Sprache: Englisch
• ISBN 10: 9819206618
• ISBN 13: 9789819206612
• Einband: Gebundene Ausgabe
• Anzahl der Seiten: 215
• Kontakt zum Hersteller: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Deutschland