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<p>Artificial intelligence (AI) and remote sensing (RS) are rapidly reshaping smart mobility systems from infrastructure-oriented transportation management toward data-driven, adaptive, and integrated mobility ecosystems. The convergence of multi-source spatial sensing and AI-driven analytics increasingly supports real-time mobility perception, behavioural inference, predictive optimisation, and multimodal operational coordination in complex urban environments. However, existing studies remain fragmented across transportation science, geospatial analytics, intelligent systems, and urban mobility research, while systematic synthesis of AI–RS-driven smart mobility remains limited. To address this gap, this study conducts a structured review of AI- and RS-driven smart mobility through bibliometric analysis, thematic synthesis, and conceptual framework development. It systematically examines the technological foundations, methodological pathways, intelligent mobility mechanisms, and emerging development trajectories underlying integrated intelligent mobility systems. The findings indicate that AI–RS integration can substantially enhance mobility environment reconstruction, intelligent service matching, spatiotemporal behavioural analytics, latent demand prediction, and cross-platform mobility coordination. Furthermore, the convergence of AI and RS is accelerating the evolution of smart mobility toward spatial intelligence, transportation digital twins, Mobility as a Service (MaaS) ecosystem, and air–space–ground collaborative mobility systems. Building on this synthesis, this study proposes a future-oriented framework for integrated intelligent mobility systems centred on spatial intelligence, multimodal coordination, adaptive governance, and collaborative mobility intelligence. Overall, this review contributes to the interdisciplinary integration of AI, RS, and smart mobility research, while providing theoretical and practical references for developing more resilient, equitable, and sustainable intelligent mobility systems.</p>

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AI- and Remote-Sensing-Driven Smart Mobility: A Structured Review of Data Fusion, Intelligent Analytics, and Mobility Applications

  • 期刊论文

  • 发表日期：

    2026年6月29日

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  • Binbin Hou,
  • Haishan Xia

摘要

Artificial intelligence (AI) and remote sensing (RS) are rapidly reshaping smart mobility systems from infrastructure-oriented transportation management toward data-driven, adaptive, and integrated mobility ecosystems. The convergence of multi-source spatial sensing and AI-driven analytics increasingly supports real-time mobility perception, behavioural inference, predictive optimisation, and multimodal operational coordination in complex urban environments. However, existing studies remain fragmented across transportation science, geospatial analytics, intelligent systems, and urban mobility research, while systematic synthesis of AI–RS-driven smart mobility remains limited. To address this gap, this study conducts a structured review of AI- and RS-driven smart mobility through bibliometric analysis, thematic synthesis, and conceptual framework development. It systematically examines the technological foundations, methodological pathways, intelligent mobility mechanisms, and emerging development trajectories underlying integrated intelligent mobility systems. The findings indicate that AI–RS integration can substantially enhance mobility environment reconstruction, intelligent service matching, spatiotemporal behavioural analytics, latent demand prediction, and cross-platform mobility coordination. Furthermore, the convergence of AI and RS is accelerating the evolution of smart mobility toward spatial intelligence, transportation digital twins, Mobility as a Service (MaaS) ecosystem, and air–space–ground collaborative mobility systems. Building on this synthesis, this study proposes a future-oriented framework for integrated intelligent mobility systems centred on spatial intelligence, multimodal coordination, adaptive governance, and collaborative mobility intelligence. Overall, this review contributes to the interdisciplinary integration of AI, RS, and smart mobility research, while providing theoretical and practical references for developing more resilient, equitable, and sustainable intelligent mobility systems.