Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

10-2019

Abstract

Urban commuting data has long been a vital source of understanding population mobility behaviour and has been widely adopted for various applications such as transport infrastructure planning and urban anomaly detection. While individual-specific transaction records (such as smart card (tap-in, tap-out) data or taxi trip records) hold a wealth of information, these are often private data available only to the service provider (e.g., taxicab operator). In this work, we explore the utility in harnessing publicly available, albeit noisy, transportation datasets, such as noisy “Estimated Time of Arrival" (ETA) records (commonly available to commuters through transit Apps or electronic signages). We first propose a framework to extract accurate individual bus trajectories from such ETA records, and present results from both a primary city (Singapore) and a secondary city (London) to validate the techniques. Finally, we quantify the upper bound on the spatiotemporal resolution, of the reconstructed trajectory outputs, achieved by our proposed technique

Keywords

Smart Transportation, Urban Mobility

Discipline

Numerical Analysis and Scientific Computing | Software Engineering | Transportation

Research Areas

Software and Cyber-Physical Systems

Publication

2019 22nd IEEE Intelligent Transportation Systems Conference ITSC: Auckland, October 27-30: Proceedings

First Page

4517

Last Page

4524

ISBN

9781538670248

Identifier

10.1109/ITSC.2019.8916939

Publisher

IEEE

City or Country

Piscataway, NJ

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/ITSC.2019.8916939

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