Open Access
Issue |
E3S Web Conf.
Volume 477, 2024
International Conference on Smart Technologies and Applied Research (STAR'2023)
|
|
---|---|---|
Article Number | 00049 | |
Number of page(s) | 14 | |
DOI | https://doi.org/10.1051/e3sconf/202447700049 | |
Published online | 16 January 2024 |
- W. Zeng et al., “Traffic Flow Prediction Based on Hybrid Deep Learning Models Considering Missing Data and Multiple Factors,” Sustainability 2023, Vol. 15, Page 11092, vol. 15, no. 14, p. 11092, Jul. 2023, doi: 10.3390/SU151411092. [Google Scholar]
- H. F. Yang, T. S. DIllon, and Y. P. P. Chen, “Optimized Structure of the Traffic Flow Forecasting Model with a Deep Learning Approach,” IEEE Trans Neural Netw Learn Syst, vol. 28, no. 10, pp. 2371–2381, Oct. 2017, doi: 10.1109/TNNLS.2016.2574840. [CrossRef] [PubMed] [Google Scholar]
- B. L. Smith and M. J. Demetsky, “Traffic Flow Forecasting: Comparison of Modeling Approaches,” J Transp Eng, vol. 123, no. 4, pp. 261–266, Jul. 1997, doi: 10.1061/(ASCE)0733-947X(1997)123:4(261). [CrossRef] [Google Scholar]
- N. Zafar, I. U. Haq, J. U. R. Chughtai, and O. Shafiq, “Applying Hybrid Lstm-Gru Model Based on Heterogeneous Data Sources for Traffic Speed Prediction in Urban Areas,” Sensors 2022, Vol. 22, Page 3348, vol. 22, no. 9, p. 3348, Apr. 2022, doi: 10.3390/S22093348. [Google Scholar]
- M. Pan et al., “Water Level Prediction Model Based on GRU and CNN,” IEEE Access, vol. 8, pp. 60090–60100, 2020, doi: 10.1109/ACCESS.2020.2982433. [CrossRef] [Google Scholar]
- B. Yang, S. Sun, J. Li, X. Lin, and Y. Tian, “Traffic flow prediction using LSTM with feature enhancement,” Neurocomputing, vol. 332, pp. 320–327, Mar. 2019, doi: 10.1016/J.NEUCOM.2018.12.016. [CrossRef] [Google Scholar]
- Y. Tian, K. Zhang, J. Li, X. Lin, and B. Yang, “LSTM-based traffic flow prediction with missing data,” Neurocomputing, vol. 318, pp. 297–305, Nov. 2018, doi: 10.1016/J.NEUCOM.2018.08.067. [CrossRef] [Google Scholar]
- B. L. Smith, B. M. Williams, and R. Keith Oswald, “Comparison of parametric and nonparametric models for traffic flow forecasting,” Transp Res Part C Emerg Technol, vol. 10, no. 4, pp. 303–321, Aug. 2002, doi: 10.1016/S0968-090X(02)00009-8. [CrossRef] [Google Scholar]
- A. Ait Ouallane, A. Bakali, A. Bahnasse, S. Broumi, and M. Talea, “Fusion of engineering insights and emerging trends: Intelligent urban traffic management system,” Information Fusion, vol. 88, pp. 218–248, Dec. 2022, doi: 10.1016/J.INFFUS.2022.07.020. [CrossRef] [Google Scholar]
- T. A. Haddad, D. Hedjazi, and S. Aouag, “An IoT-Based Adaptive Traffic Light Control Algorithm for Isolated Intersection,” Lecture Notes in Networks and Systems, vol. 199 LNNS, pp. 107–117, 2021, doi: 10.1007/978-3-030-69418-0_10/COVER. [CrossRef] [Google Scholar]
- S. M. Odeh, A. M. Mora, M. N. Moreno, and J. J. Merelo, “A hybrid fuzzy genetic algorithm for an adaptive traffic signal system,” Advances in Fuzzy Systems, vol. 2015, Jan. 2015, doi: 10.1155/2015/378156. [Google Scholar]
- T. Mao, A. S. Mihaita, F. Chen, and H. L. Vu, “Boosted Genetic Algorithm Using Machine Learning for Traffic Control Optimization,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 7112–7141, Jul. 2022, doi: 10.1109/TITS.2021.3066958. [CrossRef] [Google Scholar]
- A. Agga, A. Abbou, M. Labbadi, Y. El Houm, and I. H. Ou Ali, “CNN-LSTM: An efficient hybrid deep learning architecture for predicting short-term photovoltaic power production,” Electric Power Systems Research, vol. 208, p. 107908, Jul. 2022, doi: 10.1016/J.EPSR.2022.107908. [CrossRef] [Google Scholar]
- L. Li, Y. Lv, and F. Y. Wang, “Traffic signal timing via deep reinforcement learning,” IEEE/CAA Journal of Automatica Sinica, vol. 3, no. 3, pp. 247–254, Jul. 2016, doi: 10.1109/JAS.2016.7508798. [CrossRef] [Google Scholar]
- X. Zheng et al., “Big Data for Social Transportation,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 3, pp. 620–630, Mar. 2016, doi: 10.1109/TITS.2015.2480157. [CrossRef] [Google Scholar]
- X. Wang, X. Zheng, Q. Zhang, T. Wang, and D. Shen, “Crowdsourcing in ITS: The State of the Work and the Networking,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 6, pp. 1596–1605, Jun. 2016, doi: 10.1109/TITS.2015.2513086. [CrossRef] [Google Scholar]
- A. J. Khattak and B. Wali, “Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles,” Transp Res Part C Emerg Technol, vol. 84, pp. 48–73, Nov. 2017, doi: 10.1016/J.TRC.2017.08.004. [CrossRef] [Google Scholar]
- B. Mao et al., “Routing or Computing? the Paradigm Shift Towards Intelligent Computer Network Packet Transmission Based on Deep Learning,” IEEE Transactions on Computers, vol. 66, no. 11, pp. 1946–1960, Nov. 2017, doi: 10.1109/TC.2017.2709742. [CrossRef] [Google Scholar]
- B. M. Williams and L. A. Hoel, “Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results,” J Transp Eng, vol. 129, no. 6, pp. 664–672, Nov. 2003, doi: 10.1061/(ASCE)0733-947X(2003)129:6(664). [CrossRef] [Google Scholar]
- G. Bao, Z. Zeng, and Y. Shen, “Region stability analysis and tracking control of memristive recurrent neural network,” Neural Networks, vol. 98, pp. 51–58, Feb. 2018, doi: 10.1016/J.NEUNET.2017.11.005. [CrossRef] [Google Scholar]
- “Predicting Short-Term Traffic Flow by Long Short-Term Memory Recurrent Neural Network | IEEE Conference Publication | IEEE Xplore.” Accessed: Oct. 01, 2023. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/7463717 [Google Scholar]
- Y. Zhang and Y. Liu, “Traffic forecasting using least squares support vector machines,” Transportmetrica, vol. 5, no. 3, pp. 193–213, 2009, doi: 10.1080/18128600902823216. [CrossRef] [Google Scholar]
- L. Zhang, Q. Liu, W. Yang, N. Wei, and D. Dong, “An Improved K-nearest Neighbor Model for Short-term Traffic Flow Prediction,” Procedia Soc Behav Sci, vol. 96, pp. 653–662, Nov. 2013, doi: 10.1016/J.SBSPRO.2013.08.076. [CrossRef] [Google Scholar]
- H. Chang, Y. Lee, B. Yoon, and S. Baek, “Dynamic near-term traffic flow prediction: System-oriented approach based on past experiences,” IET Intelligent Transport Systems, vol. 6, no. 3, pp. 292–305, Sep. 2012, doi: 10.1049/IET-ITS.2011.0123/CITE/REFWORKS. [CrossRef] [Google Scholar]
- I. Moumen, J. Abouchabaka, and N. Rafalia, “Adaptive traffic lights based on traffic flow prediction using machine learning models,” International Journal of Electrical and Computer Engineering (IJECE), vol. 13, no. 5, pp. 5813–5823, Oct. 2023, doi: 10.11591/IJECE.V13I5.PP5813-5823. [CrossRef] [Google Scholar]
- B. Hussain, M. K. Afzal, S. Ahmad, and A. M. Mostafa, “Intelligent traffic flow prediction using optimized GRU model,” IEEE Access, vol. 9, pp. 100736–100746, 2021, doi: 10.1109/ACCESS.2021.3097141. [CrossRef] [Google Scholar]
- Y. Lv, Y. Duan, W. Kang, Z. Li, and F. Y. Wang, “Traffic Flow Prediction with Big Data: A Deep Learning Approach,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 2, pp. 865–873, Apr. 2015, doi: 10.1109/TITS.2014.2345663. [Google Scholar]
- Y. Wu, H. Tan, L. Qin, B. Ran, and Z. Jiang, “A hybrid deep learning based traffic flow prediction method and its understanding,” Transp Res Part C Emerg Technol, vol. 90, pp. 166–180, May 2018, doi: 10.1016/J.TRC.2018.03.001. [CrossRef] [Google Scholar]
- K. Cho et al., “Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation,” EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, pp. 1724–1734, Jun. 2014, doi: 10.3115/v1/d14-1179. [Google Scholar]
- G. Lin, A. Lin, and D. Gu, “Using support vector regression and K-nearest neighbors for short-term traffic flow prediction based on maximal information coefficient,” Inf Sci (N Y), vol. 608, pp. 517–531, Aug. 2022, doi: 10.1016/J.INS.2022.06.090. [CrossRef] [Google Scholar]
- Y. Tian, K. Zhang, J. Li, X. Lin, and B. Yang, “LSTM-based traffic flow prediction with missing data,” Neurocomputing, vol. 318, pp. 297–305, Nov. 2018, doi: 10.1016/J.NEUCOM.2018.08.067. [CrossRef] [Google Scholar]
- X. Feng, X. Ling, H. Zheng, Z. Chen, and Y. Xu, “Adaptive multi-kernel SVM with spatial-temporal correlation for short-term traffic flow prediction,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 6, pp. 2001–2013, Jun. 2019, doi: 10.1109/TITS.2018.2854913. [CrossRef] [Google Scholar]
- L. N. N. Do, H. L. Vu, B. Q. Vo, Z. Liu, and D. Phung, “An effective spatial-temporal attention based neural network for traffic flow prediction,” Transp Res Part C Emerg Technol, vol. 108, pp. 12–28, Nov. 2019, doi: 10.1016/J.TRC.2019.09.008. [CrossRef] [Google Scholar]
- X. Ma, Z. Dai, Z. He, J. Ma, Y. Wang, and Y. Wang, “Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction,” Sensors 2017, Vol. 17, Page 818, vol. 17, no. 4, p. 818, Apr. 2017, doi: 10.3390/S17040818. [Google Scholar]
- Y. Wu, H. Tan, L. Qin, B. Ran, and Z. Jiang, “A hybrid deep learning based traffic flow prediction method and its understanding,” Transp Res Part C Emerg Technol, vol. 90, pp. 166–180, May 2018, doi: 10.1016/J.TRC.2018.03.001. [CrossRef] [Google Scholar]
- F. Kong, J. Li, B. Jiang, T. Zhang, and H. Song, “Big data-driven machine learning-enabled traffic flow prediction,” Transactions on Emerging Telecommunications Technologies, vol. 30, no. 9, p. e3482, Sep. 2019, doi: 10.1002/ETT.3482. [Google Scholar]
- I. Moumen, J. Abouchabaka, and N. Rafalia, “Enhancing urban mobility: integration of IoT road traffic data and artificial intelligence in smart city environment,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 32, no. 2, pp. 985–993, Nov. 2023, doi: 10.11591/IJEECS.V32.I2.PP985-993. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.