The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
An Interactive-Voting Based Map Matching Algorithm
Abstract
Matching a raw GPS trajectory to roads on a digital map is often referred to as the Map Matching problem. However, the occurrence of the low-sampling-rate trajectories (e.g. one point per 2 minutes) has brought lots of challenges to existing map matching algorithms. To address this problem, we propose an Interactive Voting-based Map Matching (IVMM) algorithm based on the following three insights: 1) The position context of a GPS point as well as the topological information of road networks, 2) the mutual influence between GPS points (i.e., the matching result of a point references the positions of its neighbors; in turn, when matching its neighbors, the position of this point will also be referenced), and 3) the strength of the mutual influence weighted by the distance between GPS points (i.e., the farther distance is the weaker influence exists). In this approach, we do not only consider the spatial and temporal information of a GPS trajectory but also devise a voting-based strategy to model the weighted mutual influences between GPS points. We evaluate our IVMM algorithm based on a user labeled real trajectory dataset. As a result, the IVMM algorithm outperforms the related method (ST-Matching algorithm).
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
Mining interesting locations and travel sequences from GPS trajectories
Zheng, Y., Zhang, L., Xie, X., & Ma, W.-Y. (2009) · Proceedings of the 18th international conference on World wide web · doi:10.1145/1526709.1526816 · Cited by 1,928
Self-citationHidden Markov map matching through noise and sparseness
Newson, P., & Krumm, J. (2009) · Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems · doi:10.1145/1653771.1653818 · Cited by 1,019
Quddus, M. A., Ochieng, W. Y., & Noland, R. B. (2007) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2007.05.002 · Cited by 917
Map-matching for low-sampling-rate GPS trajectories
Lou, Y., Zhang, C., Zheng, Y., Xie, X., Wang, W., & Huang, Y. (2009) · Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems · doi:10.1145/1653771.1653820 · Cited by 797
Self-citationData Structures for Range Searching
Bentley, J. L., & Friedman, J. H. (1979) · ACM Computing Surveys · doi:10.1145/356789.356797 · Cited by 622
Learning transportation mode from raw gps data for geographic applications on the web
Zheng, Y., Liu, L., Wang, L., & Xie, X. (2008) · Proceedings of the 17th international conference on World Wide Web · doi:10.1145/1367497.1367532 · Cited by 616
Self-citationOn map-matching vehicle tracking data
Brakatsoulas, S., Pfoser, D., Salas, R. Z., & Wenk, C. (2005) · Cited by 534
Capturing the Uncertainty of Moving-Object Representations
Pfoser, D., & Jensen, C. S. (1999) · Lecture Notes in Computer Science · doi:10.1007/3-540-48482-5_9 · Cited by 481
MATCHING GPS OBSERVATIONS TO LOCATIONS ON A DIGITAL MAP
Greenfeld, J. (2002) · Cited by 408
An Introduction to Map Matching for Personal Navigation Assistants
Bernstein, D. H., & Kornhauser, A. L. (1998) · Cited by 255
Cited by
Urban Computing: Concepts, Methodologies, and Applications
Zheng, Y., Capra, L., Wolfson, O., & Yang, H. (2014) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2629592 · Cited by 1,507
Self-citationU-Air: when urban air quality inference meets big data
Zheng, Y., Liu, F., & Hsieh, H.-P. (2013) · Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2487575.2488188 · Cited by 916
Self-citationDriving with knowledge from the physical world
Yuan, J., Zheng, Y., Xie, X., & Sun, G. (2011) · Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2020408.2020462 · Cited by 849
Self-citationT-Finder: A Recommender System for Finding Passengers and Vacant Taxis
Yuan, N. J., Zheng, Y., Zhang, L., & Xie, X. (2013) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.153 · Cited by 432
Self-citationA Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation
Yuan, H., & Li, G. (2021) · Data Science and Engineering · doi:10.1007/s41019-020-00151-z · Cited by 389
Metadata
- Authors
- Yuan, J., Zheng, Y., Zhang, C., Xie, X., & Sun, G.-Z.
- Venue
- 2010 Eleventh International Conference on Mobile Data Management
- Publisher
- IEEE
- Published
- 2010-05
- Pages
- 43-52
- OpenAlex
- W2094283130
- Cited by
- 318
- References
- 21
- FWCI
- 15.07
- Type
- conference paper
- 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: An Interactive-Voting Based Map Matching Algorithm. https://app.cochat.ai/papers/10.1109/mdm.2010.14
- The paper
- Yuan, J., Zheng, Y., Zhang, C., Xie, X., & Sun, G.-Z. (2010). An Interactive-Voting Based Map Matching Algorithm. 2010 Eleventh International Conference on Mobile Data Management. https://doi.org/10.1109/mdm.2010.14
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.
