eBook
Mechanism-Driven Explainable Urban Spatio-Temporal Prediction
| Title: | Mechanism-Driven Explainable Urban Spatio-Temporal Prediction |
|---|---|
| Description: | 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. |
| Authors: | Jingyuan Wang, Jiahao Ji |
| Resource Type: | eBook. |
| Categories: | COMPUTERS / Database Administration & Management |
| Database: | eBook Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edsebk DbLabel: eBook Index An: 4552540 RelevancyScore: 994 AccessLevel: 6 PubType: eBook PubTypeId: ebook PreciseRelevancyScore: 993.57763671875 |
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| Items | – Name: Title Label: Title Group: Ti Data: Mechanism-Driven Explainable Urban Spatio-Temporal Prediction – Name: Abstract Label: Description Group: Ab Data: 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. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jingyuan+Wang%22">Jingyuan Wang</searchLink><br /><searchLink fieldCode="AR" term="%22Jiahao+Ji%22">Jiahao Ji</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Database+Administration+%26+Management%22">COMPUTERS / Database Administration & Management</searchLink> |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=4552540 |
| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English Titles: – TitleFull: Mechanism-Driven Explainable Urban Spatio-Temporal Prediction Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jingyuan Wang – PersonEntity: Name: NameFull: Jiahao Ji – PersonEntity: Name: NameFull: Jingyuan Wang – PersonEntity: Name: NameFull: Jiahao Ji IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2027 Identifiers: – Type: isbn-print Value: 9789819206612 – Type: isbn-electronic Value: 9789819206629 Titles: – TitleFull: Mechanism-Driven Explainable Urban Spatio-Temporal Prediction Type: main |
| ResultId | 1 |