The DOI matches its Crossref record; 81 of the 81 references it cites were found in a registry or an index.
Road Traffic Forecasting: Recent Advances and New Challenges
Open accessAbstract
Due to its paramount relevance in transport planning and logistics, road traffic forecasting has been a subject of active research within the engineering community for more than 40 years. In the beginning most approaches relied on autoregressive models and other analysis methods suited for time series data. More recently, the development of new technology, platforms and techniques for massive data processing under the Big Data umbrella, the availability of data from multiple sources fostered by the Open Data philosophy and an ever-growing need of decision makers for accurate traffic predictions have shifted the spotlight to data-driven procedures. This paper aims to summarize the efforts made to date in previous related surveys towards extracting the main comparing criteria and challenges in this field. A review of the latest technical achievements in this field is also provided, along with an insightful update of the main technical challenges that remain unsolved. The ultimate goal of this work is to set an updated, thorough, rigorous compilation of prior literature around traffic prediction models so as to motivate and guide future research on this vibrant field.
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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
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Short-term traffic forecasting: where we are and where we’re going
Vlahogianni, E. I., Karlaftis, M. G., & Golias, J. C. (2014) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2014.01.005 · Cited by 1,132
Self-citationDynamic prediction of traffic volume through Kalman filtering theory
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Comparison of parametric and nonparametric models for traffic flow forecasting
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Incremental Learning of Concept Drift in Nonstationary Environments
Elwell, R., & Polikar, R. (2011) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2011.2160459 · Cited by 931
Short-term traffic flow prediction using seasonal ARIMA model with limited input data
Kumar, S. V., & Vanajakshi, L. (2015) · European Transport Research Review · doi:10.1007/s12544-015-0170-8 · Cited by 839
A gradient boosting method to improve travel time prediction
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Self-citationANALYSIS OF FREEWAY TRAFFIC TIME-SERIES DATA BY USING BOX-JENKINS TECHNIQUES
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Cited by
Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions
Muhammad, K., Ullah, A., Lloret, J., Ser, J. D., & de Albuquerque, V. H. C. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3032227 · Cited by 659
Miglani, A., & Kumar, N. (2019) · Vehicular Communications · doi:10.1016/j.vehcom.2019.100184 · Cited by 295
Deep learning for edge computing applications: A State-of-the-Art survey
Wang, F., Zhang, M., Wang, X., Ma, X., & Liu, J. (2020) · IEEE Access · doi:10.1109/access.2020.2982411 · Cited by 259
Ahmad, S., Shakeel, I., Mehfuz, S., & Ahmad, J. (2023) · Computer Science Review · doi:10.1016/j.cosrev.2023.100568 · Cited by 197
Metadata
- Authors
- Lana, I., Del Ser, J., Velez, M., & Vlahogianni, E. I.
- Venue
- IEEE Intelligent Transportation Systems Magazine
- Published
- 2018
- Volume
- 10
- Issue
- 2
- Pages
- 93-109
- ISSN
- 1939-1390 (ISSN-L)
- OpenAlex
- W2799109291
- Cited by
- 367
- References
- 81
- FWCI
- 27.39
- Type
- journal article
- Language
- en
- Open access
- green
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- Lana, I., Del Ser, J., Velez, M., & Vlahogianni, E. I. (2018). Road Traffic Forecasting: Recent Advances and New Challenges. IEEE Intelligent Transportation Systems Magazine. https://doi.org/10.1109/mits.2018.2806634
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