001437050 000__ 04442cam\a2200577\i\4500
001437050 001__ 1437050
001437050 003__ OCoLC
001437050 005__ 20230309004128.0
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001437050 007__ cr\nn\nnnunnun
001437050 008__ 210518s2021\\\\si\a\\\\ob\\\\001\0\eng\d
001437050 019__ $$a1252421051$$a1252703857
001437050 020__ $$a9789811622410$$q(electronic bk.)
001437050 020__ $$a9811622418$$q(electronic bk.)
001437050 020__ $$z9789811622403
001437050 020__ $$z981162240X
001437050 0247_ $$a10.1007/978-981-16-2241-0$$2doi
001437050 035__ $$aSP(OCoLC)1253557509
001437050 040__ $$aLIP$$beng$$erda$$epn$$cLIP$$dOCLCO$$dYDX$$dGW5XE$$dEBLCP$$dOCLCO$$dGZM$$dOCLCF$$dUKAHL$$dOCLCQ$$dOCLCO$$dCOM$$dOCLCQ
001437050 049__ $$aISEA
001437050 050_4 $$aHE369$$b.L58 2021
001437050 08204 $$a388.3/10285$$223
001437050 1001_ $$aLiu, Zhidan,$$eauthor.
001437050 24510 $$aMobility data-driven urban traffic monitoring /$$cZhidan Liu, Kaishun Wu.
001437050 264_1 $$aSingapore :$$bSpringer,$$c[2021]
001437050 264_4 $$c©2021
001437050 300__ $$a1 online resource (xi, 69 pages) :$$billustrations (chiefly color)
001437050 336__ $$atext$$btxt$$2rdacontent
001437050 337__ $$acomputer$$bc$$2rdamedia
001437050 338__ $$aonline resource$$bcr$$2rdacarrier
001437050 4901_ $$aSpringerBriefs in computer science,$$x2191-5768
001437050 504__ $$aIncludes bibliographical references and index.
001437050 5050_ $$aChapter 1 Introduction -- Chapter 2 Urban Traffic Monitoring from Mobility Data -- Chapter 3 A Compressive Sensing based Traffic Monitoring Approach -- Chapter 4 A Dynamic Correlation Modeling based Traffic Monitoring Approach -- Chapter 5 A Crowdsensing based Traffic Monitoring Approach.-Chapter 6 Conclusion and Future Work.
001437050 506__ $$aAccess limited to authorized users.
001437050 520__ $$aThis book introduces the concepts of mobility data and data-driven urban traffic monitoring. A typical framework of mobility data-based urban traffic monitoring is also presented, and it describes the processes of mobility data collection, data processing, traffic modelling, and some practical issues of applying the models for urban traffic monitoring. This book presents three novel mobility data-driven urban traffic monitoring approaches. First, to attack the challenge of mobility data sparsity, the authors propose a compressive sensing-based urban traffic monitoring approach. This solution mines the traffic correlation at the road network scale and exploits the compressive sensing theory to recover traffic conditions of the whole road network from sparse traffic samplings. Second, the authors have compared the traffic estimation performances between linear and nonlinear traffic correlation models and proposed a dynamical non-linear traffic correlation modelling-based urban traffic monitoring approach. To address the challenge of involved huge computation overheads, the approach adapts the traffic modelling and estimations tasks to Apache Spark, a popular parallel computing framework. Third, in addition to mobility data collected by the public transit systems, the authors present a crowdsensing-based urban traffic monitoring approach. The proposal exploits the lightweight mobility data collected from participatory bus riders to recover traffic statuses through careful data processing and analysis. Last but not the least, the book points out some future research directions, which can further improve the accuracy and efficiency of mobility data-driven urban traffic monitoring at large scale. This book targets researchers, computer scientists, and engineers, who are interested in the research areas of intelligent transportation systems (ITS), urban computing, big data analytic, and Internet of Things (IoT). Advanced level students studying these topics benefit from this book as well.
001437050 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed June 10, 2021).
001437050 650_0 $$aTraffic monitoring$$xData processing.
001437050 650_0 $$aIntelligent transportation systems.
001437050 650_6 $$aCirculation$$xSurveillance$$xInformatique.
001437050 650_6 $$aSystèmes de transport intelligents.
001437050 655_0 $$aElectronic books.
001437050 7001_ $$aWu, Kaishun,$$eauthor.
001437050 77608 $$iPrint version:$$z9789811622403
001437050 77608 $$iPrint version:$$z9789811622427
001437050 830_0 $$aSpringerBriefs in computer science,$$x2191-5768
001437050 852__ $$bebk
001437050 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-2241-0$$zOnline Access$$91397441.1
001437050 909CO $$ooai:library.usi.edu:1437050$$pGLOBAL_SET
001437050 980__ $$aBIB
001437050 980__ $$aEBOOK
001437050 982__ $$aEbook
001437050 983__ $$aOnline
001437050 994__ $$a92$$bISE