LEADER 02434nam 2200565 a 450 001 9910480302703321 005 20170816145638.0 010 $a1-283-25932-X 010 $a9786613259325 010 $a90-485-1456-8 035 $a(CKB)2670000000114381 035 $a(EBL)770946 035 $a(OCoLC)751962333 035 $a(SSID)ssj0000642741 035 $a(PQKBManifestationID)11364209 035 $a(PQKBTitleCode)TC0000642741 035 $a(PQKBWorkID)10667404 035 $a(PQKB)11761085 035 $a(MiAaPQ)EBC770946 035 $a(EXLCZ)992670000000114381 100 $a20111105d2011 uy 0 101 0 $adut 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aWater in en om de stad$b[electronic resource] $emeer energie voor water : Openbare les uitgesproken op woensdag 13 april 2011 /$fdoor Jeroen Kluck 210 $aAmsterdam $cHVA Publicaties$d2011 215 $a1 online resource (43 p.) 225 1 $aHVA Openbare Lessen 300 $aDescription based upon print version of record. 311 $a90-5629-682-5 320 $aIncludes bibliographical references. 327 $aWater in en om de stad; Inleiding; 1. Rioolstelsels; 2. Afvoer van huishoudelijk afvalwater in de toekomst; 3. Duurzaamheid en energie in de waterketen; 4. Omgaan met extreme neerslag; 5. Waardevol water in de stad; 6. Wat wil ik op de Hogeschool van Amsterdam bereiken en hoe ga ik te werk?; 7. Afsluitend; Noten; Literatuur; Curriculum vitae 330 $aKluck draagt tijdens de open les een aantal oplossingen aan om in Amsterdam duurzamer te werk te gaan. Door de steeds vaker voorkomende zware regenval moet Amsterdam bijvoorbeeld betere afvoersystemen aanleggen. Ook geeft Kluck aan dat het raadzaam is om in de stad ruimte voor open water te cree?ren.Door duurzamer om te gaan met water hoeven winkels, huizen en tunnels bij zware regenval niet meer onder water te lopen en wordt het leven in de stad aangenamer. 410 0$aHVA Openbare Lessen 606 $aMunicipal water supply 606 $aWater 608 $aElectronic books. 615 0$aMunicipal water supply. 615 0$aWater. 676 $a344.41046 700 $aKluck$b Jeroen$0950425 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910480302703321 996 $aWater in en om de stad$92148838 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