The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
Survey on traffic prediction in smart cities
Abstract
Abstract The rapid development in machine learning and in the emergence of new data sources makes it possible to examine and predict traffic conditions in smart cities more accurately than ever. This can help to optimize the design and management of transport services in a future automated city. In this paper, we provide a detailed presentation of the traffic prediction methods for such intelligent cities, also giving an overview of the existing data sources and prediction models.
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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
The scaling laws of human travel
Brockmann, D., Hufnagel, L., & Geisel, T. (2006) · Nature · doi:10.1038/nature04292 · Cited by 2,205
Williams, B. M., & Hoel, L. A. (2003) · Journal of Transportation Engineering · doi:10.1061/(asce)0733-947x(2003)129:6(664 · Cited by 2,164
International Conference on Artificial Neural Networks
(1990) · Neural Networks · doi:10.1016/0893-6080(90)90065-s · Cited by 2,088
Data-Driven Intelligent Transportation Systems: A Survey
Zhang, J., Wang, F.-Y., Wang, K., Lin, W.-H., Xu, X., & Chen, C. (2011) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2011.2158001 · Cited by 1,817
Using LSTM and GRU neural network methods for traffic flow prediction
Fu, R., Zhang, Z., & Li, L. (2016) · 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac.2016.7804912 · Cited by 1,434
Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y., & Wang, Y. (2017) · Sensors · doi:10.3390/s17040818 · Cited by 1,409
Deep learning for short-term traffic flow prediction
Polson, N. G., & Sokolov, V. O. (2017) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2017.02.024 · Cited by 1,015
Comparison of parametric and nonparametric models for traffic flow forecasting
Smith, B. L., Williams, B. M., & Keith Oswald, R. (2002) · Transportation Research Part C: Emerging Technologies · doi:10.1016/s0968-090x(02)00009-8 · Cited by 1,006
Exposure to Traffic and the Onset of Myocardial Infarction
Peters, A., von Klot, S., Heier, M., Trentinaglia, I., Hörmann, A., Wichmann, H. E., & Löwel, H. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa040203 · Cited by 915
Cited by
Traffic Flow Prediction via Spatial Temporal Graph Neural Network
Wang, X., Ma, Y., Wang, Y., Jin, W., Wang, X., Tang, J., Jia, C., & Yu, J. (2020) · Proceedings of The Web Conference 2020 · doi:10.1145/3366423.3380186 · Cited by 644
Zhang, C., Zhang, S., Yu, J. J. Q., & Yu, S. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3055283 · Cited by 194
Metadata
- Authors
- Nagy, A. M., & Simon, V.
- Venue
- Pervasive and Mobile Computing
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 50
- Pages
- 148-163
- ISSN
- 1574-1192 (ISSN-L)
- OpenAlex
- W2884128153
- Cited by
- 312
- References
- 65
- FWCI
- 23.47
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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