Mobility Patterns, Big Data and Transport Analytics: Tools and Applications for Modeling 1st Edition – Ebook PDF Instant Delivery – ISBN(s): 9780128129708,0128129700,9780128129715, 0128129719
Product details:
- ISBN-10 : 0128129700
- ISBN-13 : 978-0128129708
- Author: Constantinos Antoniou, Loukas Dimitriou
Mobility Patterns, Big Data and Transport Analytics provides a guide to the new analytical framework and its relation to big data, focusing on capturing, predicting, visualizing and controlling mobility patterns – a key aspect of transportation modeling. The book features prominent international experts who provide overviews on new analytical frameworks, applications and concepts in mobility analysis and transportation systems. Users will find a detailed, mobility ‘structural’ analysis and a look at the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications and transportation systems analysis that are related to complex processes and phenomena.
This book bridges the gap between big data, data science, and transportation systems analysis with a study of big data’s impact on mobility and an introduction to the tools necessary to apply new techniques.
Table contents:
1 Introduction
2 Big Textual Data, Text Sources, and Text Mining
2.1 Meaning of Text in the Context of Computational Linguistics
2.2 Text Mining
2.3 Text Mining Process Model
2.4 Textual Data Sources in Transportation
3 Fundamental Concepts and Techniques in Literature
3.1 Topic Modeling
3.2 Word2Vec—Text Embeddings With Deep Learning
4 Application Examples of Big Textual Data in Transportation
4.1 Developing Transportation and Logistics Performance Classifiers Using NLTK and Naı¨ve Bayes
4.2 Understanding the Public Opinion Toward Driverless Cars With Topic Modeling
4.3 Predicting Taxi Demand in Special Events With Text Embeddings and Deep Learning
5 Conclusions
References
Further Reading
Part II: Applications
9. Statewide Comparison of Origin-Destination Matrices Between California Travel Model and Twitter
Jae Hyun Lee, Adam Davis, Elizabeth McBride and Konstadinos G. Goulias
1 Introduction
2 California Statewide Travel Demand Model
3 Twitter Data
4 Trip Extraction Methods
5 Models for Matrix Conversion
5.1 Tobit Regression Model
5.2 Latent Class Regression Model
6 Summary and Conclusion
References
10. Transit Data Analytics for Planning, Monitoring, Control, and Information
Haris N. Koutsopoulos, Zhenliang Ma, Peyman Noursalehi and Yiwen Zhu
1 Introduction
2 Measuring System Performance From the Passenger’s Point of View
2.1 The Individual Reliability Buffer Time (IRBT)
2.2 Denied Boarding
3 Decision Support With Predictive Analytics
3.1 Framework
3.2 Application: Provision of Crowding Predictive Information
4 Optimal Design of Transit Demand Management Strategies
4.1 Framework and Problem Formulation
4.2 Application: Prepeak Discount Design
5 Conclusion
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