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E-raamat: Public Transport Planning with Smart Card Data

Edited by (Gifu University, Japan), Edited by (Kyoto University, Japan)
  • Formaat: 274 pages
  • Ilmumisaeg: 17-Feb-2017
  • Kirjastus: CRC Press Inc
  • ISBN-13: 9781315353333
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  • Formaat: 274 pages
  • Ilmumisaeg: 17-Feb-2017
  • Kirjastus: CRC Press Inc
  • ISBN-13: 9781315353333
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For the first time smart materials have been categorized per taxonomical classifications, which are used in cybernetics systems. This approach enables smart materials (both developed and yet to be invented) to be systemized and to justify the three-stage process of the materials’ creation: detection of the material structure change during operation, selection of the mechanism for expedient correction of materials structure using the feedback channel, and realization of the selected correction mechanism on the base of the achievements in natural sciences.


A phenomenological model of smart materials is proposed. Based on this model, the initial structure change is corrected by the feedback mechanism, which initiates the secondary physical-chemical reactions resulting in changes in the internal energy of the material. Attention is paid to modeling expedient reactions of smart materials to external influence by methods of mechanics, as well as to forecast new heterogeneous structures with feedback. Major classes of smart materials and systems are reviewed which are currently used (or in development) in such areas as machines, semiconductor industry, medical devices, and others.


The book will be of interest for material scientists and engineers developing new materials. It can also be used as additional reading in materials science courses.

Preface v
1 An Overview on Opportunities and Challenges of Smart Card Data Analysis
1(14)
1 Introduction
1(1)
2 Smart Card Systems and Data Features
2(3)
3 Analysis Challenges
5(2)
4 Categorization of Potential Analysis using Smart Card Data
7(2)
5 Book Overview, What is Missing and Conclusion
9(2)
References
11(1)
Author Biography
11(4)
Part 1 Estimating Passenger Behavior
2 Transit Origin-Destination Estimation
15(22)
1 Introduction
15(2)
2 General Principles
17(1)
3 Inference of Destinations
18(6)
4 O-D Matrix Methods
24(1)
5 Journey and Tour Pattern Analysis
25(4)
6 Areas for Future Research
29(1)
References
30(5)
Author Biography
35(2)
3 Destination and Activity Estimation
37(18)
1 Smart Card Use in Trip Destination and Activity Estimation
38(1)
2 Smart Card Data Structure in Seoul
39(2)
3 Methodology for Trip Destination Estimation
41(2)
4 Trip Purpose Imputation using Household Travel Survey
43(5)
5 Results and Discussion
48(2)
6 Illustration of Results with MATSim
50(1)
7 Conclusion
51(1)
References
52(1)
Author Biography
53(2)
4 Modelling Travel Choices on Public Transport Systems with Smart Card Data
55(18)
1 Introduction
55(1)
2 Theoretical Background
56(3)
3 Modelling Behaviour with Smart Card Data
59(4)
4 Case Study: Santiago, Chile
63(5)
5 Conclusion
68(1)
Acknowledgements
68(1)
References
68(2)
Author Biography
70(3)
Part 2 Combining Smart Card Data with other Databases
5 Combination of Smart Card Data with Person Trip Survey Data
73(20)
1 Introduction
73(4)
2 Model
77(5)
3 Empirical Analysis
82(8)
4 Conclusion
90(1)
References
91(1)
Author Biography
92(1)
6 A Method for Conducting Before-After Analyses of Transit Use by Linking Smart Card Data and Survey Responses
93(20)
1 Introduction
94(1)
2 Literature Review
94(2)
3 Background
96(1)
4 Data Collection
96(3)
5 Methodology
99(4)
6 Evaluation of the Intervention
103(5)
7 Areas for Improvement and Future Research
108(1)
8 Conclusion
109(1)
Acknowledgements
109(1)
References
110(1)
Author Biography
110(3)
7 Multipurpose Smart Card Data: Case Study of Shizuoka, Japan
113(20)
1 Introduction
113(2)
2 Multipurpose Smart Cards
115(1)
3 Case Study Area and Smart Card Data Overview
115(3)
4 Overview of Collected Data
118(1)
5 Stated Preference Survey on Sensitivity to Point System
119(10)
6 Conclusion
129(1)
References
130(1)
Author Biography
130(3)
8 Using Smart Card Data for Agent--Based Transport Simulation
133(30)
1 Introduction
133(2)
2 User Equilibrium and Public Transport in MATSim
135(1)
3 CEPAS
136(2)
4 Method
138(9)
5 Validation and Performance
147(7)
6 Application
154(3)
7 Conclusion
157(1)
Acknowledgements
158(1)
References
158(1)
Author Biography
159(4)
Part 3 Smart Card Sata for Evaluation
9 Smart Card Data for Wider Transport System Evaluation
163(18)
1 Introduction
163(1)
2 Level of Service Indicators
164(2)
3 Application to Santiago
166(10)
4 Conclusion
176(1)
Acknowledgements
177(1)
References
177(1)
Authors Biography
178(3)
10 Evaluation of Bus Service Key Performance Indicators using Smart Card Data
181(16)
1 Introduction
181(1)
2 Background
182(1)
3 Information System
183(1)
4 KPI Assessment
184(2)
5 Some Examples
186(7)
6 Conclusion
193(1)
Acknowledgements
194(1)
References
194(2)
Author Biography
196(1)
11 Ridership Evaluation and Prediction in Public Transport by Processing Smart Card Data: A Dutch Approach and Example
197(28)
1 Introduction
197(2)
2 Smart Cards and Data
199(4)
3 Predicting Ridership by Smart Card Data
203(10)
4 Case Study: The Tram Network of The Hague
213(6)
5 Conclusion
219(2)
Acknowledgements
221(1)
References
221(2)
Author Biography
223(2)
12 Assessment of Traffic Bottlenecks at Bus Stops
225(20)
1 Introduction
225(1)
2 Background of this Study
226(1)
3 Development of Evaluation Measures
227(7)
4 Saitama City Case Study
234(8)
5 Conclusion
242(1)
Acknowledgements
242(1)
References
242(1)
Author Biography
243(2)
13 Conclusions: Opportunities Provided to Transit Organizations by Automated Data Collection Systems, Challenges and Thoughts for the Future
245(18)
1 Background
246(1)
2 Automated Data Collection Systems (ADCS)
247(2)
3 A Conceptual Framework for ADCS in a Transit Organization
249(5)
4 Challenges
254(2)
5 An Unexplored Area for Research Using Smart Card Data: Elasticities and Pricing Strategy
256(3)
6 Conclusions: Looking to the Future
259(1)
Author Biography
260(3)
Index 263
Fumitaka Kurauchi, Jan-Dirk Schmöcker