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Cited by 3,000Traffic Prediction and Management TechniquesTraffic control and managementAir Quality Monitoring and Forecasting

Traffic Flow Prediction With Big Data: A Deep Learning Approach

Lv, Yisheng, Duan, Yanjie, Kang, Wenwen, Li, Zhengxi, Wang, Fei-Yue·2014·IEEE Transactions on Intelligent Transportation Systems
DOI 10.1109/tits.2014.2345663
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Abstract

Accurate and timely traffic flow information is important for the successful deployment of intelligent transportation systems. Over the last few years, traffic data have been exploding, and we have truly entered the era of big data for transportation. Existing traffic flow prediction methods mainly use shallow traffic prediction models and are still unsatisfying for many real-world applications. This situation inspires us to rethink the traffic flow prediction problem based on deep architecture models with big traffic data. In this paper, a novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently. A stacked autoencoder model is used to learn generic traffic flow features, and it is trained in a greedy layerwise fashion. To the best of our knowledge, this is the first time that a deep architecture model is applied using autoencoders as building blocks to represent traffic flow features for prediction. Moreover, experiments demonstrate that the proposed method for traffic flow prediction has superior performance.

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  • In [31], the SAE method outperformed Random Walk, Support Vector Machines, and Radial Basis Function for traffic flow predictions based on big data.

    “The results indicate that the SAE outperforms the compared techniques (Random Walk, Support Vector Machines, and Radial Basis Function).”

    From A Review of Machine Learning and IoT in Smart Transportation (2019) · cites this paper as [31]

  • DeepCham leverages edge master server and users to collaboratively train CNN for better object recognition.

    “DeepCham [55] leveraged an edge master server coordinated with several participating users to collaboratively train CNN model to achieve better object recognition accuracy.”

    From Deep learning for edge computing applications: A State-of-the-Art survey (2020) · cites this paper as [55]

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Authors
Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F.-Y.
Affiliations
  • Chinese Academy of Sciences (CN)
  • North China University of Technology (CN)
ORCID
Venue
IEEE Transactions on Intelligent Transportation Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2014
Pages
1-9
ISSN
  • 1524-9050 (ISSN-L)
  • 1558-0016
DOI
10.1109/tits.2014.2345663
OpenAlex
W2036785686
Cited by
3,000
References
60
Citations by year
2026: 78 · 2025: 220 · 2024: 251 · 2023: 268 · 2022: 329 · 2021: 374 · 2020: 498 · 2019: 414 · 2018: 320 · 2017: 153 · 2016: 77 · 2015: 17
FWCI
121.63
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations
IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
Funders
National Natural Science Foundation of China (61233001, 61203166, 71232006, 61104054, 61273326)
Topics
  • Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
  • Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
  • Physical Sciences › Environmental Science › Environmental Engineering › Air Quality Monitoring and Forecasting
Keywords
traffic flow prediction, big data, stacked autoencoder, deep learning, spatio-temporal correlation, intelligent transportation systems

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