The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Identification of Road Surface Conditions using IoT Sensors and Machine Learning
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The results indicate that all the classifiers performed adequately with over 99% accuracy.”
From A Review of Machine Learning and IoT in Smart Transportation (2019) · cites this paper as [35]
In the citing paper
“Last but not least, in [35] the authors compare the methods k-NN, RF, and SVM for the task of road abnormalities detection.”
From A Review of Machine Learning and IoT in Smart Transportation (2019) · cites this paper as [35]
In the citing paper
“All the classifiers presented an accuracy over 99% based on the CCR measurements.”
From A Review of Machine Learning and IoT in Smart Transportation (2019) · cites this paper as [35]
Citations
Cites
Nericell: rich monitoring of road and traffic conditions using mobile smartphones
Mohan, P., Padmanabhan, V. N., & Ramjee, R. (2008) · Proceedings of the 6th ACM conference on Embedded network sensor systems · doi:10.1145/1460412.1460444 · Cited by 1,267
CarTel: a distributed mobile sensor computing system
Hull, B., Bychkovsky, V., Zhang, Y., Chen, K., Goraczko, M., Miu, A., Shih, E., Balakrishnan, H., & Madden, S. (2006) · Proceedings of the 4th international conference on Embedded networked sensor systems · doi:10.1145/1182807.1182821 · Cited by 1,030
Wolverine: Traffic and road condition estimation using smartphone sensors
Bhoraskar, R., Vankadhara, N., Raman, B., & Kulkarni, P. (2012) · doi:10.1109/comsnets.2012.6151382 · Cited by 249
Nericell: using mobile smartphones for rich monitoring of road and traffic conditions
Mohan, P., Padmanabhan, V. N., & Ramjee, R. (2008) · Proceedings of the 6th ACM conference on Embedded network sensor systems · doi:10.1145/1460412.1460450 · Cited by 245
A public transport system based sensor network for road surface condition monitoring
Zoysa, K. D., Keppitiyagama, C., Seneviratne, G. P., & Shihan, W. W. A. T. (2007) · doi:10.1145/1326571.1326585 · Cited by 189
Distributed Road Surface Condition Monitoring Using Mobile Phones
Perttunen, M., Mazhelis, O., Cong, F., Kauppila, M., Leppänen, T., Kantola, J., Collin, J., Pirttikangas, S., Haverinen, J., Ristaniemi, T., et al. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23641-9_8 · Cited by 182
A Mobile Application for Road Surface Quality Control: UNIquALroad
Astarita, V., Caruso, M. V., Danieli, G. A., Festa, D. C., Giofrè, V. P., Iuele, T., & Vaiana, R. (2012) · Procedia - Social and Behavioral Sciences · doi:10.1016/j.sbspro.2012.09.828 · Cited by 120
Embedded solution for road condition monitoring using vehicular sensor networks
Mednis, A., Elsts, A., & Selavo, L. (2012) · International Conference on Application of Information and Communication Technologies · doi:10.1109/icaict.2012.6398502 · Cited by 49
On the analysis of road surface conditions using embedded smartphone sensors
Alqudah, Y. A., & Sababha, B. H. (2017) · International Conference on Information and Communication Systems (ICICS) · doi:10.1109/iacs.2017.7921967 · Cited by 27
A Novel Road Monitoring Approach Using Wireless Sensor Networks
Collotta, M., Pau, G., Salerno, V. M., & Scatà, G. (2012) · ... International Conference on Complex, Intelligent and Software Intensive Systems · doi:10.1109/cisis.2012.37 · Cited by 18
Cited by
A Review of Machine Learning and IoT in Smart Transportation
Zantalis, F., Koulouras, G., Karabetsos, S., & Kandris, D. (2019) · Future Internet · doi:10.3390/fi11040094 · Cited by 625
Machine Learning for Internet of Things-Based Smart Transportation Networks
Khawar, H., Soomro, T. R., & Kamal, M. A. (2022) · Advances in Human and Social Aspects of Technology · doi:10.4018/978-1-6684-4045-2.ch006 · Cited by 31
Metadata
- Authors
- Ng, J. R., Wong, J. S., Goh, V. T., Yap, W. J., Yap, T. T. V., & Ng, H.
- Affiliations
- Multimedia University (MY)
- Corresponding author
- Goh, Vik Tor
- Venue
- Lecture Notes in Electrical Engineering
- Publisher
- Springer Singapore
- Published
- 2018-08-28
- Pages
- 259-268
- ISSN
- 1876-1100 (ISSN-L)
- 1876-1119
- OpenAlex
- W2889279447
- Cited by
- 43
- References
- 11
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 5 · 2023: 6 · 2022: 11 · 2021: 4 · 2020: 4 · 2019: 5
- FWCI
- 15.42
- Type
- book chapter
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in electrical engineering (publishedVersion)
- Siti Hasmah Digital Library-MMU Institutiona Repository (Multimedia University) (acceptedVersion)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Transport Systems and Technology
- Keywords
- road surface condition classification, IoT sensors, road roughness, potholes, speed bumps, rumble strips, feature selection, particle swarm optimization, k-nearest neighbors, random forest, support vector machine, road maintenance decision-making
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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Identification of Road Surface Conditions using IoT Sensors and Machine Learning. https://app.cochat.ai/papers/10.1007/978-981-13-2622-6_26
- The paper
- Ng, J. R., Wong, J. S., Goh, V. T., Yap, W. J., Yap, T. T. V., & Ng, H. (2019). Identification of Road Surface Conditions using IoT Sensors and Machine Learning. Lecture Notes in Electrical Engineering. https://doi.org/10.1007/978-981-13-2622-6_26
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.
