LEADER 03839nam 2200493 450 001 9910483099803321 005 20220122183014.0 010 $a981-16-2241-8 024 7 $a10.1007/978-981-16-2241-0 035 $a(CKB)4100000011930476 035 $a(DE-He213)978-981-16-2241-0 035 $a(MiAaPQ)EBC6627587 035 $a(Au-PeEL)EBL6627587 035 $a(OCoLC)1253557509 035 $a(PPN)255883277 035 $a(EXLCZ)994100000011930476 100 $a20220122d2021 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMobility data-driven urban traffic monitoring /$fZhidan Liu, Kaishun Wu 205 $a1st ed. 2021. 210 1$aGateway East, Singapore :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (XI, 69 p. 21 illus., 18 illus. in color.) 225 1 $aSpringerBriefs in Computer Science,$x2191-5768 311 $a981-16-2240-X 327 $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. 330 $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. 410 0$aSpringerBriefs in Computer Science,$x2191-5768 606 $aTraffic monitoring$xData processing 615 0$aTraffic monitoring$xData processing. 676 $a388.3140723 700 $aLiu$b Zhidan$0853153 702 $aWu$b Kaishun 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483099803321 996 $aMobility Data-Driven Urban Traffic Monitoring$91905054 997 $aUNINA LEADER 03218oam 2200673I 450 001 9910781076603321 005 20230725044833.0 010 $a1-135-16332-4 010 $a1-135-16333-2 010 $a1-282-57154-0 010 $a9786612571541 010 $a0-203-85832-8 024 7 $a10.4324/9780203858325 035 $a(CKB)2550000000006749 035 $a(EBL)481081 035 $a(OCoLC)551146099 035 $a(SSID)ssj0000363080 035 $a(PQKBManifestationID)11274720 035 $a(PQKBTitleCode)TC0000363080 035 $a(PQKBWorkID)10399292 035 $a(PQKB)10955097 035 $a(MiAaPQ)EBC481081 035 $a(Au-PeEL)EBL481081 035 $a(CaPaEBR)ebr10370138 035 $a(CaONFJC)MIL257154 035 $a(OCoLC)551146099 035 $a(EXLCZ)992550000000006749 100 $a20180706d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aMega-schools, technology, and teachers $eachieving education for all /$fJohn S. Daniel 210 1$aNew York, N.Y. :$cRoutledge,$d2010. 215 $a1 online resource (209 p.) 225 1 $aOpen and flexible learning series 300 $aDescription based upon print version of record. 311 $a0-415-87205-7 311 $a0-415-87204-9 320 $aIncludes bibliographical references and index. 327 $aBook Cover; Title; Copyright; Dedication; Contents; List of Figures and Tables; Series Editor's Foreword; Acknowledgements; Glossary of Acronyms; Introduction; 1 Education for All: Unfinished Business; 2 Seeking a Silver Bullet; 3 Technology Is the Answer: What Is the Question?; 4 Open Schools and Mega-Schools; 5 Teacher Education at Scale; 6 Strategies for Success; APPENDIX 1 Profiles: Selected Open Schools and Mega-Schools; APPENDIX 2 Programmes and Mechanisms for Expanding Teacher Supply; Bibliography; Subject Index; Name Index 330 $aEducation for All (EFA) has been a top priority for governments and intergovernmental development agencies for the last twenty years. So far the global EFA movement has placed its principal focus on providing quality universal primary education (UPE) for all children by 2015.The latest addition to The Open and Flexible Learning series, this book addresses the new challenges created by both the successes and the failures of the UPE campaign. This book advocates new approaches for providing access to secondary education for today's rapidly growing population of childr 410 0$aOpen & flexible learning series. 606 $aEducation, Elementary$zDeveloping countries 606 $aEducational equalization$zDeveloping countries 606 $aDistance education$xComputer-assisted instruction$zDeveloping countries 615 0$aEducation, Elementary 615 0$aEducational equalization 615 0$aDistance education$xComputer-assisted instruction 676 $a372.9172/4 676 $a372.91724 700 $aDaniel$b John S.$f1942-,$01509134 801 0$bFlBoTFG 801 1$bFlBoTFG 906 $aBOOK 912 $a9910781076603321 996 $aMega-schools, technology, and teachers$93740730 997 $aUNINA