國立虎尾科技大學 |

Enabling Smart Urban Services with GPS Trajectory Data

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Enabling Smart Urban Services with GPS Trajectory Data/ by Chao Chen, Daqing Zhang, Yasha Wang, Hongyu Huang.
Author:	Chen, Chao.
other author:	Zhang, Daqing.
Description:	XIX, 347 p. 152 illus.online resource. :
Contained By:	Springer Nature eBook
Subject:	Mobile computing. -
Online resource:	https://doi.org/10.1007/978-981-16-0178-1
ISBN:	9789811601781

Enabling Smart Urban Services with GPS Trajectory Data
Chen, Chao.

Enabling Smart Urban Services with GPS Trajectory Data[electronic resource] /by Chao Chen, Daqing Zhang, Yasha Wang, Hongyu Huang. - 1st ed. 2021. - XIX, 347 p. 152 illus.online resource.

Chapter 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.

With 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.

ISBN: 9789811601781

Standard No.: 10.1007/978-981-16-0178-1doiSubjects--Topical Terms:

562918
Mobile computing.

LC Class. No.: QA76.76.A65

Dewey Class. No.: 004

Enabling Smart Urban Services with GPS Trajectory Data
LDR:04409nam a22003975i 4500 001 1050108
003 DE-He213
005 20210623110626.0
007 cr nn 008mamaa
008 220103s2021 si | s |||| 0|eng d
020 $a 9789811601781 $9 978-981-16-0178-1
024 7 $a 10.1007/978-981-16-0178-1 $2 doi
035 $a 978-981-16-0178-1
050 4 $a QA76.76.A65
072 7 $a UB $2 bicssc
072 7 $a COM018000 $2 bisacsh
072 7 $a UB $2 thema
082 0 4 $a 004 $2 23
100 1 $a Chen, Chao. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1188730
245 1 0 $a Enabling Smart Urban Services with GPS Trajectory Data $h [electronic resource] / $c by Chao Chen, Daqing Zhang, Yasha Wang, Hongyu Huang.
250 $a 1st ed. 2021.
264 1 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2021.
300 $a XIX, 347 p. 152 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a Chapter 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.
520 $a With 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.
650 0 $a Mobile computing. $3 562918
650 0 $a Application software. $3 528147
650 0 $a Data mining. $3 528622
650 0 $a Big data. $3 981821
650 1 4 $a Computer Applications. $3 669785
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Big Data. $3 1017136
650 2 4 $a Mobile Computing. $3 1115990
700 1 $a Zhang, Daqing. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1021567
700 1 $a Wang, Yasha. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1354360
700 1 $a Huang, Hongyu. $e editor. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1309681
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789811601774
776 0 8 $i Printed edition: $z 9789811601798
776 0 8 $i Printed edition: $z 9789811601804
856 4 0 $u https://doi.org/10.1007/978-981-16-0178-1
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)