时空大数据在交通系统韧性研究中的应用回顾与展望  

Review and Prospects:Application of Spatio-Temporal Big Data in Transportation System Resilience Studies

作　　者：汤俊卿 安梦琪 赵鹏军 宫兆亚 郭增骏 罗陶然 吕薇 TANG Junqing;AN Mengqi;ZHAO Pengjun;GONG Zhaoya;GUO Zengjun;LUO Taoran;LYU Wei(School of Urban Planning and Design,Peking University Shenzhen Graduate School,Shenzhen 518055,China;Key Laboratory of Earth Surface System and Human-Earth Relations of Ministry of Natural Resources of China,Shenzhen Graduate School,Peking University,Shenzhen 518055,China)

机构地区：[1]北京大学深圳研究生院城市规划与设计学院,深圳518055 [2]自然资源部陆表系统与人地关系重点实验室,深圳518055

出　　处：《地球信息科学学报》2025年第3期553-569,共17页Journal of Geo-information Science

基　　金：深圳市科技计划资助项目(KQTD20221101093604016);国家自然科学基金项目(42376213、41925003);广东省基础与应用基础研究基金(2023A1515010979、2021A1515110537)。

摘　　要：【意义】在全球城市多灾害风险频发的当下,如何建设具有高水平防灾韧性的交通系统已成为学界关注的焦点。相较于传统的数据类型,时空大数据以其高精细度和高信息密度的优势,在交通系统韧性研究中发挥日益显著的重要作用。然而,目前对于时空大数据在交通系统韧性研究中进展脉络的理解相对不清晰,客观上缺少对交通系统韧性领域中时空大数据的类型、应用场景和发展趋向的综合分析。【进展】本文利用系统性文献综述方法,对在CNKI中国知网和Web of Science数据库中检索到的中英文相关文献进行了系统的综述分析,全面探讨了时空大数据应用于交通系统韧性研究的主流数据类型,量化评估、监测预警、模拟预测与系统优化4个具体实践领域及在各领域所运用的研究方法,以及相关研究的发展趋势。【展望】在总结当前时空大数据在交通系统韧性研究中的应用成就与不足的基础上,进一步展望了若干交通系统韧性研究领域未来可能的发展方向,以期为我国时空大数据赋能交通可持续发展、推进交通强国战略目标落实提供有益思考与借鉴。[Significance]Cities globally face increasingly frequent multi-hazard risks,driving them pursuing more sustainable and resilient urban transportation systems.This paper presents a comprehensive systematic literature review of the application of spatial-temporal data in transportation system resilience studies.It highlights the pivotal role of spatial-temporal big data in understanding and enhancing the resilience of urban transportation systems under various hazard scenarios.Spatial-temporal big data,characterized by high temporal resolution and fine spatial granularity,has been increasingly applied to the field of transportation system resilience,providing essential support for decision-makers.[Progress]This study reveals two significant findings:Firstly,quantitative analysis of transportation system resilience is one of the most widely applied uses of spatial-temporal big data.However,real-time monitoring and early warning explorations are relatively rare.Most studies remain at the modelling and numerical simulation stage,indicating a need for more empirical studies using multi-source spatial-temporal big data.Moreover,compared to English literature,Chinese transportation system resilience studies are primarily qualitative and lack empirical research,indicating divergent research emphases between domestic and international scholars.Secondly,high-quality,multi-source spatial-temporal big data could facilitate more comprehensive spatial analysis in transportation system resilience studies.Improved data quality allows for deeper exploration from a microscopic perspective,focusing on individual behaviors and aligning closely with real-world needs.The concept of resilience has evolved from its previous post-disaster focus to a comprehensive life-cycle perspective encompassing pre-,during-,and post-disaster phases,transforming the study framework for transportation system resilience.[Prospect]As spatial-temporal big data technology advances and new transportation modes emerge,more innovations and breakthroughs in tra

关 键 词：时空大数据 交通系统韧性 文献综述 系统性综述 回顾与展望 可持续发展 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]