The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Land-Use Classification Using Taxi GPS Traces
Abstract
Detailed land use, which is difficult to obtain, is an integral part of urban planning. Currently, GPS traces of vehicles are becoming readily available. It conveys human mobility and activity information, which can be closely related to the land use of a region. This paper discusses the potential use of taxi traces for urban land-use classification, particularly for recognizing the social function of urban land by using one year's trace data from 4000 taxis. First, we found that pick-up/set-down dynamics, extracted from taxi traces, exhibited clear patterns corresponding to the land-use classes of these regions. Second, with six features designed to characterize the pick-up/set-down pattern, land-use classes of regions could be recognized. Classification results using the best combination of features achieved a recognition accuracy of 95%. Third, the classification results also highlighted regions that changed land-use class from one to another, and such land-use class transition dynamics of regions revealed unusual real-world social events. Moreover, the pick-up/set-down dynamics could further reflect to what extent each region is used as a certain class.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
(1996) · Cited by 20,179
Object based image analysis for remote sensing
Blaschke, T. (2010) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2009.06.004 · Cited by 4,482
Use of normalized difference built-up index in automatically mapping urban areas from TM imagery
Zha, Y., Gao, J., & Ni, S. (2003) · International Journal of Remote Sensing · doi:10.1080/01431160304987 · Cited by 2,910
The factor of scale in remote sensing
Woodcock, C. E., & Strahler, A. H. (1987) · Remote Sensing of Environment · doi:10.1016/0034-4257(87)90015-0 · Cited by 1,306
T-drive: driving directions based on taxi trajectories
Yuan, J., Zheng, Y., Zhang, C., Xie, W., Xie, X., Sun, G., & Huang, Y. (2010) · Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems · doi:10.1145/1869790.1869807 · Cited by 1,105
Luck, M., & Wu, J. (2002) · Landscape Ecology · doi:10.1023/a:1020512723753 · Cited by 908
Wang, F.-Y. (2010) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2010.2060218 · Cited by 856
Deng, J. S., Wang, K., Hong, Y., & Qi, J. G. (2009) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2009.05.001 · Cited by 691
Seto, K. C., & Fragkias, M. (2005) · Landscape Ecology · doi:10.1007/s10980-005-5238-8 · Cited by 671
Use of impervious surface in urban land-use classification
Lu, D., & Weng, Q. (2006) · Remote Sensing of Environment · doi:10.1016/j.rse.2006.02.010 · Cited by 656
Cited by
Mobile Crowd Sensing and Computing: The Review of an Emerging Human-Powered Sensing Paradigm
Guo, B., Wang, Z., Yu, Z., Wang, Y., Yen, N. Y., Huang, R., & Zhou, X. (2015) · ACM Computing Surveys · doi:10.1145/2794400 · Cited by 719
Uncovering urban human mobility from large scale taxi GPS data
Tang, J., Liu, F., Wang, Y., & Wang, H. (2015) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2015.06.032 · Cited by 280
GOF-TTE: Generative Online Federated Learning Framework for Travel Time Estimation
Zhang, Z., Wang, H., Fan, Z., Chen, J., Song, X., & Shibasaki, R. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3190864 · Cited by 20
Metadata
- Authors
- Pan, G., Qi, G., Wu, Z., Zhang, D., & Li, S.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2013-03
- Volume
- 14
- Issue
- 1
- Pages
- 113-123
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2023596329
- Cited by
- 290
- References
- 41
- FWCI
- 33.28
- Type
- journal article
- Language
- en
- Open access
- closed
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Land-Use Classification Using Taxi GPS Traces. https://app.cochat.ai/papers/10.1109/tits.2012.2209201
- The paper
- Pan, G., Qi, G., Wu, Z., Zhang, D., & Li, S. (2013). Land-Use Classification Using Taxi GPS Traces. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/tits.2012.2209201
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
