The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Hidden Markov map matching through noise and sparseness
Abstract
The problem of matching measured latitude/longitude points to roads is becoming increasingly important. This paper describes a novel, principled map matching algorithm that uses a Hidden Markov Model (HMM) to find the most likely road route represented by a time-stamped sequence of latitude/longitude pairs. The HMM elegantly accounts for measurement noise and the layout of the road network. We test our algorithm on ground truth data collected from a GPS receiver in a vehicle. Our test shows how the algorithm breaks down as the sampling rate of the GPS is reduced. We also test the effect of increasing amounts of additional measurement noise in order to assess how well our algorithm could deal with the inaccuracies of other location measurement systems, such as those based on WiFi and cell tower multilateration. We provide our GPS data and road network representation as a standard test set for other researchers to use in their map matching work.
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Cites
IEEE Transactions on Industrial Electronics
(2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2016.2593650 · Cited by 1,240
On map-matching vehicle tracking data
Brakatsoulas, S., Pfoser, D., Salas, R. Z., & Wenk, C. (2005) · Cited by 534
Inferring High-Level Behavior from Low-Level Sensors
Patterson, D. J., Liao, L., Fox, D., & Kautz, H. (2003) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-39653-6_6 · Cited by 517
MATCHING GPS OBSERVATIONS TO LOCATIONS ON A DIGITAL MAP
Greenfeld, J. (2002) · Cited by 408
Adaptive fuzzy-network-based C-measure map-matching algorithm for car navigation system
Kim, S., & Kim, J.-H. (2001) · IEEE Transactions on Industrial Electronics · doi:10.1109/41.915423 · Cited by 118
Cited by
(2019) · NORA - Norwegian Open Research Archives · Cited by 291
VidLoc: A Deep Spatio-Temporal Model for 6-DoF Video-Clip Relocalization
Clark, R., Wang, S., Markham, A., Trigoni, N., & Wen, H. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.284 · Cited by 249
A predictive-control framework to address bus bunching
Andres, M., & Nair, R. (2017) · Transportation Research Part B: Methodological · doi:10.1016/j.trb.2017.06.013 · Cited by 95
Hippocampal replay of experience at real-world speeds
Denovellis, E. L., Gillespie, A. K., Coulter, M. E., Sosa, M., Chung, J. E., Eden, U. T., & Frank, L. M. (2021) · eLife · doi:10.7554/elife.64505 · Cited by 89
SaferCross: Enhancing Pedestrian Safety Using Embedded Sensors of Smartphone
Won, M., Shrestha, A., Park, K.-J., & Eun, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2980085 · Cited by 27
Metadata
- Authors
- Newson, P., & Krumm, J.
- Venue
- Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
- Publisher
- ACM
- Published
- 2009-11-04
- Pages
- 336-343
- OpenAlex
- W2135822894
- Cited by
- 1,019
- References
- 5
- FWCI
- 13.00
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Hidden Markov map matching through noise and sparseness. https://app.cochat.ai/papers/10.1145/1653771.1653818
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- Newson, P., & Krumm, J. (2009). Hidden Markov map matching through noise and sparseness. Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. https://doi.org/10.1145/1653771.1653818
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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.
