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Big Data and Mobility as a Service [Pehme köide]

Edited by , Edited by , Edited by (Assistant Professor, Center for Spatial Information Science, University of Tokyo, Tokyo, Japan; Researcher, School of Business Society and Engineering, Mälardalen University, Sweden; Senior Scientist, Locationmind Inc., Tokyo, Japan)
  • Formaat: Paperback / softback, 306 pages, kõrgus x laius: 229x152 mm, kaal: 450 g
  • Ilmumisaeg: 05-Oct-2021
  • Kirjastus: Elsevier - Health Sciences Division
  • ISBN-10: 0323901697
  • ISBN-13: 9780323901697
Teised raamatud teemal:
Big Data and Mobility as a ServiceSuurem pilt
  • Formaat: Paperback / softback, 306 pages, kõrgus x laius: 229x152 mm, kaal: 450 g
  • Ilmumisaeg: 05-Oct-2021
  • Kirjastus: Elsevier - Health Sciences Division
  • ISBN-10: 0323901697
  • ISBN-13: 9780323901697
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780323901697.html
Märksõnad:
Big Data and Mobility as a Service explores MaaS platforms that can be adaptable to the ever-evolving mobility environment. It looks at multi-mode urban crowd data to assess urban mobility characteristics, their shared transportation potential, and their performance conditions and constraints. The book analyzes the roles of multimodality, travel behavior, urban mobility dynamics and participation. Combined with insights on using big data to analyze market and policy decisions, this book is an essential tool for urban transportation management researchers and practitioners.
  • Summarizes current fundamental MaaS technologies
  • Shows how to utilize anonymous big data for transportation analysis and problem-solving
  • Illustrates, with data-enabled shared transportation service examples from different countries, the similarities and differences within a global urban mobility framework
Contributors xi
Introduction xiii
Shreyas Bharule
Haoran Zhang
Ryosuke Shibasaki
1 MaaS system development and APPs
1(24)
Wenjing Li
Ryosuke Shibasaki
Haoran Zhang
Jinyu Chen
1 The development history of MaaS
1(4)
1.1 The conception
1(1)
1.2 The early application
1(1)
1.3 MaaS alliance
2(1)
1.4 Development
3(1)
1.5 Revolution and innovation
4(1)
2 The category of MaaS system
5(2)
2.1 Level 0: No integration
6(1)
2.2 Level 1: Information integration
6(1)
2.3 Level 2: Integration of booking and payment
6(1)
2.4 Level 3: Integration of the service offering
6(1)
2.5 Level 4: Integration of societal goals
7(1)
3 Study case
7(14)
3.1 UbiGo
10(2)
3.2 Whim
12(3)
3.3 Moovit
15(2)
3.4 Uber
17(4)
4 Future development trend of MaaS system
21(4)
4.1 Data-integrated
21(1)
4.2 Future-oriented
22(1)
4.3 Sustainable
23(1)
References
23(2)
2 Spatio-temporal data preprocessing technologies
25(52)
Jinyu Chen
Haoran Zhang
Wenjing Li
Ryosuke Shibasaki
1 Introduction
25(1)
2 Raw GPS data and workflow of data preprocessing
26(1)
3 Key technologies and corresponding application
27(8)
3.1 Outlier removement
27(2)
3.2 Stay location detection
29(1)
3.3 Travel segmentation
30(1)
3.4 Travel mode detection
31(2)
3.5 Map matching
33(2)
3.6 Summary
35(1)
4 Case study
35(37)
4.1 Stay location detection: Life pattern analysis
35(13)
4.2 Travel segmentation and mode detection: Ride-sharing potential analysis
48(12)
4.3 Map matching: Estimation of urban scale PM emission
60(12)
5 Conclusion
72(5)
References
72(5)
3 Travel similarity estimation and clustering
77
Yuhao Yao
Ryosuke Shibasaki
Haoran Zhang
1 Introduction
77(2)
2 Trajectory similarity
79(11)
2.1 Point-to-point distance metric
80(2)
2.2 Similarity function of trajectory
82(5)
2.3 Trajectory clustering
87(3)
3 Travel pattern similarity
90(3)
3.1 Travel pattern extraction
91(1)
3.2 Travel pattern expression
92(1)
3.3 Travel pattern clustering
93(1)
4 Origin-destination matrix similarity
93(10)
4.1 Volume difference focused OD similarity measure
95(1)
4.2 Image-based OD similarity measure
96(1)
4.3 Transforming distance-based OD similarity measure
97(1)
4.4 OD tableau similarity measure: Mobsimilarity
98(5)
5 Case study
103(3)
5.1 CDR-based travel estimation accuracy analysis
103(3)
5.2 Metro usage pattern clustering
106(1)
6 Conclusion and future directions
106
References
108
4 Data fusion technologies for MaaS 11
3(140)
Yi Sui
Haoran Zhang
Wenxiao Jiang
Rencheng Sun
Fengjing Shao
1 Introduction
113(2)
2 Data formula
115(4)
2.1 Attribute and event data
115(1)
2.2 Trajectory data
116(1)
2.3 Origin-destination (OD) trip data
117(1)
2.4 Correlation network
117(1)
2.5 Environmental data
118(1)
3 Categories of data fusion methods in MaaS
119(3)
4 Data fusion based on deep learning
122(13)
4.1 Fundamental building units of deep learning network
122(8)
4.2 Fusion strategy
130(5)
5 Decomposition-based methods
135(2)
6 Challenging problems of data fusion in MaaS
137(1)
6.1 Data quality
137(1)
6.2 Model complexity
137(1)
6.3 Data fusion in comparative analysis
138(1)
7 Conclusions
138(5)
Acknowledgments
138(1)
References
138(5)
5 Data-driven optimization technologies for MaaS
143(34)
Haoning Xi
1 Overview of data-driven optimization for the urban mobility system
143(6)
1.1 Data-driven dispatching methods for on-demand ridesharing
143(4)
1.2 Data-driven scheduling methods for public transit
147(1)
1.3 Data-driven rebalancing methods for bicycle-sharing
148(1)
2 Overview of the general concept in MaaS System
149(3)
2.1 Overview of the MaaS systems
149(1)
2.2 Overview of data in MaaS systems
150(2)
3 Mobility resource allocation in MaaS system
152(5)
3.1 Mobility resource allocation framework in MaaS
152(5)
3.2 Data-driven online stochastic resource allocation problems
157(1)
4 Data-driven optimization technologies for resource allocation in MaaS
157(7)
4.1 Sample average approximation
158(1)
4.2 Robust optimization
159(2)
4.3 Predictive analysis and prescriptive analysis
161(1)
4.4 Machine learning-based robust optimization
162(2)
5 Real-world application and case study
164(6)
5.1 Problem description
164(1)
5.2 Methodology
165(1)
5.3 Results and discussion
165(5)
6 Conclusions
170(7)
References
171(6)
6 Data-driven estimation for urban travel shareability
177(26)
Qing Yu
Weifeng Li
Dongyuan Yang
1 Introduction
177(2)
1.1 The emergence of sharing transportation
177(1)
1.2 The significance of shareability estimation
178(1)
1.3
Chapter organization
178(1)
2 Emerging sharing transportation mode
179(4)
2.1 Bicycle sharing
180(1)
2.2 Ride sharing and taxi sharing
181(1)
2.3 Customized bus
182(1)
2.4 Characteristics of sharing transportation modes
182(1)
3 Background to traditional data and their limitations
183(1)
4 New and emerging source of data
183(4)
4.1 Track and trace data
184(1)
4.2 Geographic information data
185(1)
4.3 Advantages and disadvantages of new data sources
186(1)
5 Emerging form of key technologies
187(3)
5.1 Agent-based modeling
187(1)
5.2 How ABM can be applied in shareability estimation
188(2)
6 Case study of ABM in urban shareability estimation
190(7)
6.1 Dynamic electric fence for bicycle sharing
190(1)
6.2 ABM simulation
191(1)
6.3 Data and study area
192(1)
6.4 Result of simulation
192(2)
6.5 Evaluation of the result
194(3)
7 Opportunities and challenges
197(2)
7.1 Data acquisition
197(1)
7.2 Demand prediction
198(1)
7.3 Design improvement of ABM
198(1)
7.4 Acceleration of large-scale ABM
198(1)
8 Conclusions
199(4)
Acknowledgment
200(1)
References
200(3)
7 Data mining technologies for Mobility-as-a-Service (MaaS) `
203(26)
Wen-Long Shang
Haoran Zhang
Yi Sui
1 Introduction of data mining technologies in MaaS system
203(1)
2 Data mining technologies in MaaS system
204(5)
2.1 What is data mining?
204(1)
2.2 Object of data mining
205(1)
2.3 Classical steps of data mining
205(2)
2.4 Types of transportation data
207(2)
3 Methodologies of data mining technologies used in MaaS system
209(14)
3.1 Support vector machine
209(4)
3.2 Linear regression
213(3)
3.3 Decision tree
216(3)
3.4 Clustering analysis
219(4)
4 Case study of data mining for MaaS: Bike sharing in Beijing during Covid-19 pandemic
223(4)
5 Summary of chapter
227(2)
References
228(1)
8 MaaS and loT: Concepts, methodologies, and applications
229(16)
Hongbin Xie
Xuan Song
Haoran Zhang
1 Introduction
229(1)
2 Overview of the concept
230(1)
2.1 Overview of the general concept
230(1)
2.2 Challenges of loT application in MaaS
231(1)
3 Key technologies and methodologies
231(7)
3.1 Intelligent transportation equipment
231(1)
3.2 Communication protocols for the Internet of Things
232(1)
3.3 Microservices based on the Internet of Things
232(2)
3.4 Cloud computing based on the Internet of Things
234(1)
3.5 Edge computing
235(1)
3.6 Security technologies for the Internet of Things
236(2)
4 Application and case study
238(2)
4.1 Background introduction
238(1)
4.2 System framework
238(1)
4.3 Core function
239(1)
5 Conclusion and future directions
240(5)
References
241(4)
9 MaaS system visualization
245(20)
Chuang Yang
Renhe Jiang
Ryosuke Shibasaki
1 Overview of the general concept
245(2)
2 The key visualization technologies in MaaS for different stakeholders
247(7)
2.1 The perspective of demanders of mobility
247(1)
2.2 The perspective of supplier of transportation service
248(4)
2.3 The perspective of city manager
252(2)
3 Real-world application and case study
254(7)
3.1 Case for demanders of mobility
254(1)
3.2 Case for supplier of transportation service
255(2)
3.3 Case for city manager
257(1)
3.4 Open-source visualization tools and libraries
258(3)
4 Conclusion and future directions
261(4)
References
262(3)
10 MaaS for sustainable urban development
265(16)
Xiaoya Song
Rong Cuo
Haoran Zhang
1 Introduction
265(1)
2 MaaS interacted with urban traffic and space
266(3)
2.1 Urban traffic structure
267(2)
2.2 Urban spatial structure
269(1)
3 Strategies for MaaS in urban sustainable development at multiple scales
269(3)
3.1 Macroscale: Synergy between urban agglomerations and metropolitan areas
270(1)
3.2 Mesoscale: Optimization of internal resources in cities
271(1)
3.3 Microscale: The refinement of urban streets
271(1)
4 Case study
272(4)
5 Conclusion
276(5)
References
278(3)
Index 281
Haoran (Ronan) Zhang is Assistant Professor in the Center for Spatial Information Science at the University of Tokyo, a Researcher at the School of Business Society and Engineering at Mälardalen University in Sweden, and Senior Scientist at Locationmind Inc. in Japan. His research includes smart supply chain technologies, GPS data in shared transportation, urban sustainable performance, GIS technologies in renewable energy systems, and smart cities. He is author of numerous journal articles and Editorial Board Member of several international academic journals. He has Ph.D.s in both Engineering and Sociocultural Environment and was awarded Excellent Young Researcher by Japans Ministry of Education, Culture, Sports, Science and Technology. Xuan Song is currently an Excellent Young Researcher of Japan MEXT, and an Associate Professor at The University of Tokyo. He received the Ph.D. degree in signal and information processing from Peking University, China, in 2010. From 2010 to 2012, he worked in Center for Spatial Information Science, The University of Tokyo as a post-doctoral researcher. From 2012 to 2015, he worked in Center for Spatial Information Science, The University of Tokyo as a Project Assistant Professor. In 2015, he was promoted to Project Associate Professor with the Center for Spatial Information Science, The University of Tokyo. In 2018, he joined in Artificial Intelligence Research Center (AIRC) of AIST as a tenured Senior Researcher. In the past five years, he led and participated in many important projects as principal investigator or primary actor in Japan, such as DIAS/GRENE Grant of MEXT, Japan; Japan/US Big Data and Disaster Project of JST, Japan; Young Scientists Grant and Scientific Research Grant of MEXT, Japan; Research Grant of MLIT, Japan; CORE Project of Microsoft; Grant of JR EAST Company and Hitachi Company, Japan. He served as Associate Editor, Guest Editor, Program Chair, Area Chair, Program Committee Member or reviewer for many famous journals and top-tier conferences, such as IEEE Transactions on Multimedia, WWW Journal, Big Data Journal, ACM TIST, IEEE TKDE, UbiComp, ICCV, CVPR, ICRA and etc. His main research interest are AI and its related research areas, such as data mining, intelligent system, computer vision, and robotics, especially on intelligent surveillance and information system design, mobility and spatio-temporal data mining, sensor fusion, and machine learning algorithms development. By now, he has published more than 50 technical publications in journals, book chapter, and international conference proceedings, including more than 30 high-impact papers in top-tier publications for computer science and robotics, such as ACM TOIS, ACM TIST, IEEE TPAMI, IEEE Intelligent System, KDD, UbiComp, IJCAI, AAAI, ICCV, CVPR, ECCV, ICRA and etc. His research was featured in many Japanese and international media, including United Nations, the Discovery Channel, and Fast Company Magazine. He received Honorable Mention Award in UbiComp 2015. Dr. Ryosuke Shibasaki Professor, Center for Spatial Information Science The University of Tokyo, Japan

Dr. Shibasaki's research interest covers mobile big data analysis for development, satellite/aerial imagery and sensor data analysis including automated mapping with deep learning, human behavior understanding and modeling and data assimilation of discrete moving objects.

He has published around 280 academic journal papers and 900 proceeding papers. He is also promoting use of personal information under the control of individuals. "Information bank" is a concept proposed by him (https://www.tedxtokyo.com/tedxtokyo_talk/information-bank/) .

He is a former President of Asian GIS Association, a former President of the GIS Association of Japan. He also served as a board member of Infrastructure Implementation Board of GEO (Group of Earth Observations), a steering committee member of World Data System of ICSU (International Council for Science). Now, He works as a committee member on space policy of Cabinet Office of Japan, with the expertise of geospatial information technology and application development leveraged by space technology.

Dr. Ryosuke Shibasaki obtained a Ph.D. in remote sensing/GIS from the University of Tokyo in 1986. His working experiences include Research engineer at Public Works Research Institute, Ministry of Construction, Japan (1982-1988), Associate Professor at the Department of Civil Engineering (1988-1991) and the Institute of Industrial Science, the University of Tokyo (1991-98), Professor at the Center for Spatial Information Science (1998-present. Director 2005-2010), the University of Tokyo.