LEADER 03837nam 2200493 450 001 996464394903316 005 20220122183014.0 010 $a981-16-2241-8 024 7 $a10.1007/978-981-16-2241-0 035 $a(CKB)4100000011930476 035 $a(DE-He213)978-981-16-2241-0 035 $a(MiAaPQ)EBC6627587 035 $a(Au-PeEL)EBL6627587 035 $a(OCoLC)1253557509 035 $a(PPN)255883277 035 $a(EXLCZ)994100000011930476 100 $a20220122d2021 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMobility data-driven urban traffic monitoring /$fZhidan Liu, Kaishun Wu 205 $a1st ed. 2021. 210 1$aGateway East, Singapore :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (XI, 69 p. 21 illus., 18 illus. in color.) 225 1 $aSpringerBriefs in Computer Science,$x2191-5768 311 $a981-16-2240-X 327 $aChapter 1 Introduction -- Chapter 2 Urban Traffic Monitoring from Mobility Data -- Chapter 3 A Compressive Sensing based Traffic Monitoring Approach -- Chapter 4 A Dynamic Correlation Modeling based Traffic Monitoring Approach -- Chapter 5 A Crowdsensing based Traffic Monitoring Approach. -Chapter 6 Conclusion and Future Work. 330 $aThis book introduces the concepts of mobility data and data-driven urban traffic monitoring. A typical framework of mobility data-based urban traffic monitoring is also presented, and it describes the processes of mobility data collection, data processing, traffic modelling, and some practical issues of applying the models for urban traffic monitoring. This book presents three novel mobility data-driven urban traffic monitoring approaches. First, to attack the challenge of mobility data sparsity, the authors propose a compressive sensing-based urban traffic monitoring approach. This solution mines the traffic correlation at the road network scale and exploits the compressive sensing theory to recover traffic conditions of the whole road network from sparse traffic samplings. Second, the authors have compared the traffic estimation performances between linear and nonlinear traffic correlation models and proposed a dynamical non-linear traffic correlation modelling-based urban traffic monitoring approach. To address the challenge of involved huge computation overheads, the approach adapts the traffic modelling and estimations tasks to Apache Spark, a popular parallel computing framework. Third, in addition to mobility data collected by the public transit systems, the authors present a crowdsensing-based urban traffic monitoring approach. The proposal exploits the lightweight mobility data collected from participatory bus riders to recover traffic statuses through careful data processing and analysis. Last but not the least, the book points out some future research directions, which can further improve the accuracy and efficiency of mobility data-driven urban traffic monitoring at large scale. This book targets researchers, computer scientists, and engineers, who are interested in the research areas of intelligent transportation systems (ITS), urban computing, big data analytic, and Internet of Things (IoT). Advanced level students studying these topics benefit from this book as well. 410 0$aSpringerBriefs in Computer Science,$x2191-5768 606 $aTraffic monitoring$xData processing 615 0$aTraffic monitoring$xData processing. 676 $a388.3140723 700 $aLiu$b Zhidan$0853153 702 $aWu$b Kaishun 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996464394903316 996 $aMobility Data-Driven Urban Traffic Monitoring$91905054 997 $aUNISA