The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Learning transportation mode from raw gps data for geographic applications on the web
Abstract
Geographic information has spawned many novel Web applications where global positioning system (GPS) plays important roles in bridging the applications and end users. Learning knowledge from users ’ raw GPS data can provide rich context information for both geographic and mobile applications. However, so far, raw GPS data are still used directly without much understanding. In this paper, an approach based on supervised learning is proposed to automatically infer transportation mode from raw GPS data. The transportation mode, such as walking, driving, etc., implied in a user’s GPS data can provide us valuable knowledge to understand the user. It also enables context-aware computing based on user’s present transportation mode and design of an innovative user interface for Web users. Our approach consists of three parts: a change point-
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Metadata
- Authors
- Zheng, Y., Liu, L., Wang, L., & Xie, X.
- Affiliations
- Microsoft Research Asia (China) (CN)
- Venue
- Proceedings of the 17th international conference on World Wide Web
- Publisher
- ACM
- Published
- 2008-04-21
- Pages
- 247-256
- OpenAlex
- W2143394441
- Cited by
- 616
- References
- 14
- Citations by year
- 2026: 10 · 2025: 27 · 2024: 27 · 2023: 38 · 2022: 29 · 2021: 44 · 2020: 43 · 2019: 46 · 2018: 51 · 2017: 36 · 2016: 56 · 2015: 41 · 2014: 38 · 2013: 33 · 2012: 34
- FWCI
- 80.79
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International World Wide Web Conference (WWW) (publishedVersion)
- Topics
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Computer Science › Information Systems › Data Management and Algorithms
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- GPS data, supervised learning, context-aware computing, change point detection, user activity recognition, user interface design
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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