Big Data for Sustainable Urban Transport

Stephen Jia Wang; Patrick Moriarty

doi:10.1007/978-3-319-73610-5_5

Big Data for Sustainable Urban Transport

Research output: Chapter in book / Conference proceeding › Chapter in an edited book (as author) › Academic research › peer-review

Abstract

This chapter re-examines the general solutions proposed to improve the environmental sustainability of transport discussed in Sect. 1.3.2, with a view to understanding the potential for big data in each of these approaches. How can big data be used to reduce transport energy and emissions in cities? Specifically, how can big data encourage modal shift from cars to more environmentally friendly modes, and reduce vehicular transport overall through better trip planning? The chapter also includes a case study of a ‘personal transport planner’ designed for use in Beijing, based on the idea of a monthly personal transport energy quota.

Original language	English
Title of host publication	Big Data for Urban Sustainability
Publisher	Springer International Publishing AG
Pages	81-103
ISBN (Electronic)	978-3-319-73610-5
ISBN (Print)	978-3-319-73608-2
DOIs	https://doi.org/10.1007/978-3-319-73610-5_5
Publication status	Published - Mar 2018
Externally published	Yes

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10.1007/978-3-319-73610-5_5

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Big Data for Sustainable Urban Transport

Abstract

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