LEADER 01921oam 2200493 450 001 9910794588603321 005 20210927174643.0 010 $a1-00-344553-5 010 $a1-000-97447-2 010 $a1-003-44553-5 010 $a1-64267-082-0 035 $a(CKB)4100000011744636 035 $a(MiAaPQ)EBC6465696 035 $a(EXLCZ)994100000011744636 100 $a20210620d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aKeeping us engaged $estudent perspectives (and research-based strategies) on what works and why /$fChristine Harrington, writer of foreword José Antonio Bowen 205 $aFirst edition. 210 1$aSterling, Virginia :$cStylus Publishing, LLC,$d[2021] 210 4$d©2021 215 $a1 online resource (xv 151 pages) 311 0 $a1-64267-080-4 320 $aIncludes bibliographical references and index. 330 $a"This book offers faculty practical strategies to engage students that are grounded in research and endorsed by students themselves. Through student stories, a signature feature of this book, readers will discover why professor actions result in changed attitudes, stronger connections to others and the course material, and increased learning"--$cProvided by publisher. 606 $aCollege teaching 606 $aAcademic achievement$xPsychological aspects 606 $aMotivation in education 615 0$aCollege teaching. 615 0$aAcademic achievement$xPsychological aspects. 615 0$aMotivation in education. 676 $a378.125 700 $aHarrington$b Christine$f1971-$01511955 702 $aBowen$b JoseÌ Antonio 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bUtOrBLW 906 $aBOOK 912 $a9910794588603321 996 $aKeeping us engaged$93745566 997 $aUNINA LEADER 04983nam 22006135 450 001 996464412803316 005 20240426084019.0 010 $a981-16-0178-X 024 7 $a10.1007/978-981-16-0178-1 035 $a(CKB)4100000011867214 035 $a(MiAaPQ)EBC6533384 035 $a(Au-PeEL)EBL6533384 035 $a(OCoLC)1245668365 035 $a(DE-He213)978-981-16-0178-1 035 $a(PPN)255295588 035 $a(EXLCZ)994100000011867214 100 $a20210401d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEnabling Smart Urban Services with GPS Trajectory Data /$fby Chao Chen, Daqing Zhang, Yasha Wang, Hongyu Huang 205 $a1st ed. 2021. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2021. 215 $axix, 347 pages $cillustrations ;$d24 cm 311 $a981-16-0177-1 320 $aIncludes bibliographical references. 327 $aChapter 1. Trajectory data map-matching -- Chapter 2. Trajectory data compression -- Chapter 3. Trajectory data protection -- Chapter 4. TripPlanner: Personalized trip planning leveraging heterogeneous trajectory data -- Chapter 5. ScenicPlanner: Recommending the most beautiful driving routes -- Chapter 6. GreenPlanner: Planning fuel-efficient driving routes -- Chapter 7.Hunting or waiting: Earning more by understanding taxi service strategies -- Chapter 8. iBOAT: Real-time detection of anomalous taxi trajectories from GPS traces -- Chapter 9. Real-Time imputing trip purpose leveraging heterogeneous trajectory data -- Chapter 10. GPS environment friendliness estimation with trajectory data -- Chapter 11. B-Planner: Planning night bus routes using taxi trajectory data -- Chapter 12. VizTripPurpose: Understanding city-wide passengers? travel behaviours -- Chapter 13. CrowdDeliver: Arriving as soon as possible -- Chapter 14. CrowdExpress: Arriving by the user-specified deadline -- Chapter 15. Open Issues -- Chapter 16. Conclusions. 330 $aWith the proliferation of GPS devices in daily life, trajectory data that records where and when people move is now readily available on a large scale. As one of the most typical representatives, it has now become widely recognized that taxi trajectory data provides rich opportunities to enable promising smart urban services. Yet, a considerable gap still exists between the raw data available, and the extraction of actionable intelligence. This gap poses fundamental challenges on how we can achieve such intelligence. These challenges include inaccuracy issues, large data volumes to process, and sparse GPS data, to name but a few. Moreover, the movements of taxis and the leaving trajectory data are the result of a complex interplay between several parties, including drivers, passengers, travellers, urban planners, etc. In this book, we present our latest findings on mining taxi GPS trajectory data to enable a number of smart urban services, and to bring us one step closer to the vision of smart mobility. Firstly, we focus on some fundamental issues in trajectory data mining and analytics, including data map-matching, data compression, and data protection. Secondly, driven by the real needs and the most common concerns of each party involved, we formulate each problem mathematically and propose novel data mining or machine learning methods to solve it. Extensive evaluations with real-world datasets are also provided, to demonstrate the effectiveness and efficiency of using trajectory data. Unlike other books, which deal with people and goods transportation separately, this book also extends smart urban services to goods transportation by introducing the idea of crowdshipping, i.e., recruiting taxis to make package deliveries on the basis of real-time information. Since people and goods are two essential components of smart cities, we feel this extension is bot logical and essential. Lastly, we discuss the most important scientific problems and open issues in mining GPS trajectory data. 606 $aSocial sciences$xData processing 606 $aData mining 606 $aBig data 606 $aMobile computing 606 $aComputer Application in Social and Behavioral Sciences 606 $aData Mining and Knowledge Discovery 606 $aBig Data 606 $aMobile Computing 615 0$aSocial sciences$xData processing. 615 0$aData mining. 615 0$aBig data. 615 0$aMobile computing. 615 14$aComputer Application in Social and Behavioral Sciences. 615 24$aData Mining and Knowledge Discovery. 615 24$aBig Data. 615 24$aMobile Computing. 700 $aChen$b Chao$0636283 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996464412803316 996 $aEnabling smart urban services with gps trajectory data$92814396 997 $aUNISA LEADER 00652nam0-2200241 --450 001 9910983695203321 005 20250311071945.0 100 $a20250310d2012----kmuy0itay5050 ba 101 0 $aita 102 $aIT 105 $aa 001yy 200 1 $aGrattacieli$fa cura di Alessandra Lucivero 210 $aMilano$cHachette$d2012 215 $a71 p.$cill.$d25x23 cm 225 1 $a<>maestri dell'architettura$v71 702 1$aLucivero,$bAlessandra 801 0$aIT$bUNINA$gREICAT$2UNIMARC 901 $aBK 912 $a9910983695203321 952 $aMON B 2044$b192/2025$fFARBC 959 $aFARBC 996 $aGrattacieli$91412162 997 $aUNINA