LEADER 05065nam 22006495 450 001 9910483232403321 005 20251113181041.0 010 $a9789811601781 010 $a981160178X 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 08$a9789811601774 311 08$a9811601771 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 theuser-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. 676 $a300.00285 700 $aChen$b Chao$0636283 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483232403321 996 $aEnabling smart urban services with gps trajectory data$92814396 997 $aUNINA