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001435638 001__ 1435638
001435638 003__ OCoLC
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001435638 019__ $$a1244617878$$a1284940133
001435638 020__ $$a9789811601781$$q(electronic bk.)
001435638 020__ $$a981160178X$$q(electronic bk.)
001435638 020__ $$z9811601771
001435638 020__ $$z9789811601774
001435638 0247_ $$a10.1007/978-981-16-0178-1$$2doi
001435638 035__ $$aSP(OCoLC)1245668365
001435638 040__ $$aEBLCP$$beng$$epn$$cEBLCP$$dGW5XE$$dYDX$$dOCLCO$$dEBLCP$$dOCLCF$$dN$T$$dUKAHL$$dQGK$$dOCLCO$$dOCLCQ
001435638 049__ $$aISEA
001435638 050_4 $$aG70.4
001435638 08204 $$a910.285$$223
001435638 1001_ $$aChen, Chao$$c(Computer scientist)
001435638 24510 $$aEnabling smart urban services with GPS trajectory data /$$cChao Chen, Daqing Zhang, Yasha Wang, Hongyu Huang.
001435638 260__ $$aSingapore :$$bSpringer,$$c2021.
001435638 300__ $$a1 online resource (351 pages)
001435638 336__ $$atext$$btxt$$2rdacontent
001435638 337__ $$acomputer$$bc$$2rdamedia
001435638 338__ $$aonline resource$$bcr$$2rdacarrier
001435638 504__ $$aReferences.
001435638 504__ $$aIncludes bibliographical references.
001435638 5050_ $$aIntro -- Preface -- Acknowledgments -- Contents -- Part I: Foundations -- Chapter 1: Trajectory Data Map-matching -- 1.1 Introduction -- 1.2 Related Work -- 1.3 Main Concepts -- 1.4 The SD-Matching Algorithm -- 1.4.1 Algorithm Overview -- 1.4.2 Identifying Top-k Candidate Edges for a Given GPS Point -- 1.4.2.1 Spatial Probability -- 1.4.2.2 Direction Probability -- 1.4.3 Finding Paths Between Two Consecutive GPS Points -- 1.4.4 Refining Paths for a Given Raw Trajectory Segment -- 1.5 Evaluations -- 1.5.1 Data Preparation -- 1.5.2 Evaluation Criteria -- 1.5.3 Baseline Methods
001435638 5058_ $$a1.5.4 Effectiveness Study -- 1.5.4.1 Varying l -- 1.5.4.2 Varying k -- 1.5.5 Efficiency Study -- 1.5.5.1 Varying l -- 1.5.5.2 Varying k -- 1.5.5.3 Time Cost at Different Stages -- 1.6 Conclusions and Future Work -- References -- Chapter 2: Trajectory Data Compression -- 2.1 Introduction -- 2.2 Related Work -- 2.2.1 Line-Simplification-Based Compression -- 2.2.2 Map-Matching-Based Compression -- 2.3 Basic Concepts and System Overview -- 2.3.1 Basic Concepts -- 2.3.2 System Overview -- 2.4 HCC Algorithm -- 2.4.1 Motivation -- 2.4.2 Algorithm Details -- 2.4.3 Trajectory Decompression
001435638 5058_ $$a2.5 Evaluations of HCC -- 2.5.1 Baselines -- 2.5.2 Experimental Setup -- 2.5.3 Varying l -- 2.5.4 Boundary Choice of l -- 2.5.5 Quality of Response for When-and-Where Query -- 2.6 Evaluations of VTracer -- 2.6.1 Experimental Setup -- 2.6.2 Effectiveness Study -- 2.6.3 Efficiency Study -- 2.6.3.1 Time Cost -- 2.6.3.2 Energy Consumption -- 2.6.3.3 Memory Consumption -- 2.7 Conclusions and Future Work -- References -- Chapter 3: Trajectory Data Protection -- 3.1 Introduction -- 3.2 Related Work -- 3.3 Preliminary -- 3.4 Trajectory Protection Mechanism -- 3.5 Evaluations
001435638 5058_ $$a3.6 Conclusions and Future Work -- References -- Part II: Enabling Smart Urban Services: Drivers -- Chapter 4: Hunting or Waiting: Earning More by Understanding Taxi Service Strategies -- 4.1 Introduction -- 4.2 Related Work -- 4.3 Empirical Study -- 4.3.1 Extracting Individual Driverś Digital Traces -- 4.3.1.1 Shift Handover Event Detection -- 4.3.1.2 Statistical Study of Taxi Shift Handover Location and Time -- 4.3.2 Analyzing Individual Driverś Performance -- 4.3.2.1 Taxi Performance Quantification -- 4.3.2.2 Number of Passenger Delivery Trips
001435638 5058_ $$a4.3.2.3 Popular Passenger Pickup and Drop-Off Areas -- 4.4 Taxi Strategy Formulation -- 4.4.1 Taxi Service Strategy Extraction -- 4.4.1.1 Passenger-Searching Strategies -- 4.4.1.2 Passenger-Delivery Strategies -- 4.4.1.3 Service-Area Preference -- 4.4.2 Driver-Strategy Matrix Construction -- 4.5 Understanding Taxi Service Strategies -- 4.5.1 Discovering Good Taxi Service Strategies -- 4.5.1.1 Passenger-Searching Strategies -- 4.5.1.2 Passenger-Delivery Strategies -- 4.5.1.3 Service-Area Preference -- 4.5.2 Performance Prediction Based on Historical Strategies -- 4.6 Conclusions and Future Work
001435638 506__ $$aAccess limited to authorized users.
001435638 520__ $$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.
001435638 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed April 22, 2021).
001435638 650_0 $$aSpatial data mining.
001435638 650_0 $$aGlobal Positioning System.
001435638 650_0 $$aSmart cities.
001435638 650_6 $$aGPS.
001435638 650_6 $$aVilles intelligentes.
001435638 655_0 $$aElectronic books.
001435638 7001_ $$aZhang, Daqing.
001435638 7001_ $$aWang, Yasha.
001435638 7001_ $$aHuang, Hongyu.
001435638 77608 $$iPrint version:$$aChen, Chao.$$tEnabling Smart Urban Services with GPS Trajectory Data.$$dSingapore : Springer Singapore Pte. Limited, ©2021$$z9789811601774
001435638 852__ $$bebk
001435638 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-16-0178-1$$zOnline Access$$91397441.1
001435638 909CO $$ooai:library.usi.edu:1435638$$pGLOBAL_SET
001435638 980__ $$aBIB
001435638 980__ $$aEBOOK
001435638 982__ $$aEbook
001435638 983__ $$aOnline
001435638 994__ $$a92$$bISE