The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Sensing Data Supported Traffic Flow Prediction via Denoising Schemes and ANN: A Comparison
Abstract
Short-term traffic flow prediction plays a key role of Intelligent Transportation System (ITS), which supports traffic planning, traffic management and control, roadway safety evaluation, energy consumption estimation, etc. The widely deployed traffic sensors provide us numerous and continuous traffic flow data, which may contain outlier samples due to expected sensor failures. The primary objective of the study was to evaluate the use of various smoothing models for cleaning anomaly in traffic flow data, which were further processed to predict short term traffic flow evolution with artificial neural network. The wavelet filter, moving average model, and Butterworth filter were carefully tested to smooth the collected loop detector data. Then, the artificial neural network was introduced to predict traffic flow at different time spans, which were quantitatively analyzed with commonly-used evaluation metrics. The findings of the study provide us efficient and accurate denoising approaches for short term traffic flow prediction.
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
Traffic Flow Prediction With Big Data: A Deep Learning Approach
Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F.-Y. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2345663 · Cited by 3,000
Ma, X., Tao, Z., Wang, Y., Yu, H., & Wang, Y. (2015) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2015.03.014 · Cited by 2,191
Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y., & Wang, Y. (2017) · Sensors · doi:10.3390/s17040818 · Cited by 1,409
Guo, J., Huang, W., & Williams, B. M. (2014) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2014.02.006 · Cited by 643
Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks
Yu, H., Wu, Z., Wang, S., Wang, Y., & Ma, X. (2017) · Sensors · doi:10.3390/s17071501 · Cited by 618
Long-term time series prediction with the NARX network: An empirical evaluation
Menezes, J. M. P., & Barreto, G. A. (2008) · Neurocomputing · doi:10.1016/j.neucom.2008.01.030 · Cited by 446
An Improved K-nearest Neighbor Model for Short-term Traffic Flow Prediction
Zhang, L., Liu, Q., Yang, W., et al. (2013) · Procedia - Social and Behavioral Sciences · doi:10.1016/j.sbspro.2013.08.076 · Cited by 321
Short-term traffic flow rate forecasting based on identifying similar traffic patterns
Habtemichael, F. G., & Cetin, M. (2015) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2015.08.017 · Cited by 290
Spatiotemporal Traffic Flow Prediction with KNN and LSTM
Luo, X., Li, D., Yang, Y., & Zhang, S. (2019) · Journal of Advanced Transportation · doi:10.1155/2019/4145353 · Cited by 285
Self-citationTraffic flow prediction based on combination of support vector machine and data denoising schemes
Tang, J., Chen, X., Hu, Z., Zong, F., Han, C., & Li, L. (2019) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2019.03.007 · Cited by 220
Self-citation
Cited by
Bus Arrival Time Prediction: A Comprehensive Review
Kumar, B. A., Singh, R., Shaji, H. E., & Vanajakshi, L. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3545695 · Cited by 13
Sustainability Education in L2 Writing: AI-Based Multimodal Awareness and Engagement
Aydın Yıldız, T. (2025) · Sustainability · doi:10.3390/su17219376 · Cited by 11
Metadata
- Authors
- Chen, X., Wu, S., Shi, C., Huang, Y., Yang, Y., Ke, R., & Zhao, J.
- Venue
- IEEE Sensors Journal
- Published
- 2020-12-01
- Volume
- 20
- Issue
- 23
- Pages
- 14317-14328
- ISSN
- 1530-437X (ISSN-L)
- 1558-1748
- 2379-9153
- OpenAlex
- W3041866381
- Cited by
- 282
- References
- 32
- FWCI
- 16.27
- 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: Sensing Data Supported Traffic Flow Prediction via Denoising Schemes and ANN: A Comparison. https://app.cochat.ai/papers/10.1109/jsen.2020.3007809
- The paper
- Chen, X., Wu, S., Shi, C., Huang, Y., Yang, Y., Ke, R., & Zhao, J. (2020). Sensing Data Supported Traffic Flow Prediction via Denoising Schemes and ANN: A Comparison. IEEE Sensors Journal. https://doi.org/10.1109/jsen.2020.3007809
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.
