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Big and Open Data for High-quality Transit Access
Design, Features, and Performance of Multi-modal Transit Catchment Areas
- 1st Edition - January 1, 2029
- Editor: Jiangping Zhou
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 5 4 8 0 - 8
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 5 4 8 1 - 5
Big and Open Data for High-quality Transit Access: Design, Features, and Performance of Multi-modal Transit Catchment Areas revisits the concept of "transit area" and the existing,… Read more
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Request a sales quoteBig and Open Data for High-quality Transit Access: Design, Features, and Performance of Multi-modal Transit Catchment Areas revisits the concept of "transit area" and the existing, normative, and future practice of transit area planning against the backdrop of increasing availability and usage of big and open data. Using empirical data and case studies, the book illustrates how transit area can be epitomized and defined in two dimensions: feature (form) and performance (function) and how big and open data and its combination with data from traditional sources can be used to characterize and quantify the two dimensions and to unravel their complex relationships.
This book synthesizes the state-of-the-art in how big and open data has been exploited to facilitate transit-area planning and proposes a normative framework for transit-area planning. In this framework, big and open data, alone and in combination with data from traditional sources, can play a role that data from traditional sources alone cannot. The author takes a mixed-method approach to present and convey the contents to the reader. Survey data collected by the author are used to show what kind of big and open data has been and should be used in the current/future transit-area planning practices.
- Introduces how new methods such as gradient boosting decision tree, PageRank, and graph convolutional neural networks can be used to unravel the relationships between transit area features and functions
- Uses a case study method to enhance quantitative analysis of transit areas, including comparative case studies wherever feasible
- Invites the reader to use desktop searches and text mining to explore and visualize the relationships between features and performance of transit areas described in the text, providing a some form of experiential learning
Planners, officials, students, and academics who are interested in the nexus of transit/transportation, land use, and sustainable development Members from the following associations: the International Association of Public Transport, World Conference on Transport Research, World Transport Convention, American Planning Association, Association of Collegiate Schools of Planning, International Association for China Planning, Urban Planning Society of China, Transportation Research Board, American Association of Geographers, Royal Town Planning Institute, Planning Institute Australia, Chartered Institute of Logistics and Transport, Hong Kong Society of Transport Studies, American Public Transportation Association, China Urban Public Transport Association, American Society of Civil Engineers, Institute of Transportation Engineers, and Chinese Overseas Transportation Association
1. Transit areas
2. A normative framework for transit-area planning
3. Big and open data in transit-area planning: Theory
4. Traditional approaches to transit-area planning
5. Big and open data meets transit-area planning: Practice
6. Identifying and measuring features of transit areas
7. Defining and measuring performance of transit areas
8. Transit areas: Linking features and performance Conclusion: A future of big and open data in transit-area planning
2. A normative framework for transit-area planning
3. Big and open data in transit-area planning: Theory
4. Traditional approaches to transit-area planning
5. Big and open data meets transit-area planning: Practice
6. Identifying and measuring features of transit areas
7. Defining and measuring performance of transit areas
8. Transit areas: Linking features and performance Conclusion: A future of big and open data in transit-area planning
- No. of pages: 200
- Language: English
- Edition: 1
- Published: January 1, 2029
- Imprint: Elsevier
- Paperback ISBN: 9780323954808
- eBook ISBN: 9780323954815
JZ
Jiangping Zhou
Dr. Jiangping Zhou is Associate Professor and Deputy Director of the Master of Urban Planning program at University of Hong Kong (HKU). He is Chief Examiner of Master of Transport Policy and Planning at HKU. He regularly delivers sessions on big data analytics on public transportation for HKU’s Master of Urban Analytics program. His research focuses on transport/transit systems and land use connections and how to improve their performance. He has published more than 70 articles in English in leading journals such as Proceedings of National Academy of Sciences of the United States of America, Urban Studies, and Journal of American Planning Association. He also has more than 60 refereed articles in the Chinese language. He is currently leading several research projects exploring the nexus of physical infrastructure, land use, and travel behaviors and how better/new data supply/analytics can produce new insights into it and can inform public policies.
Affiliations and expertise
Associate Professor, Department of Urban Planning and Design, Faculty of Architecture, The University of Hong Kong