Short-Term Traffic Flow Prediction Based on CNN-BILSTM with Multicomponent Information
Source abstract
Abstract
Problem definition: The intelligent transportation system (ITS) plays a vital role in the construction of smart cities. For the past few years, traffic flow prediction has been a hot study topic in the field of transportation. Facing the rapid increase in the amount of traffic information, finding out how to use dynamic traffic information to accurately predict its flow has become a challenge. Methodology: Thus, to figure out this issue, this study put forward a multistep prediction model based on a convolutional neural network and bidirectional long short-term memory (BILSTM) model. The spatial characteristics of traffic data were considered as input of the BILSTM model to extract the time series characteristics of the traffic. Results: The experimental results validated that the BILSTM model improved the prediction accuracy in comparison to the support vector regression and gated recurring unit models. Furthermore, the proposed model was comparatively analyzed in terms of mean absolute error, mean absolute percentage error, and root mean square error, which were reduced by 30.4%, 32.2%, and 39.6%, respectively. Managerial implications: Our study provides useful insights into predicting the short-term traffic flow on highways and will improve the management of traffic flow optimization.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Traffic Prediction and Management Techniques · Traffic control and management · Transportation Planning and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Internet Economics and Business, Fujian University of Technology, Fuzhou 350014, China
School of Transportation, Fujian University of Technology, Fuzhou 350108, China
Funding
the Fujian Zhi-lian-yun Supply Chain Technology and Economy Integration Service Platform from the Fujian Association for Science and Technology · the Fujian-Kenya Silk Road Cloud Joint R&D Center · the Fujian Social Sciences Federation Planning Project · General program of Fujian Natural Science Foundation · the Development Fund of Scientific Research from Fujian University of Technology
References (31)
Show source-provided references
1. A hybrid deep learning model with 1DCNN-LSTM-Attention networks for short-term traffic flow prediction10.1016/j.physa.2021.126293 ↗
2. Hybrid model for prediction of real-time traffic flow
3. Improving traffic flow prediction with weather information in connected cars: A deep learning approach10.1109/TVT.2016.2585575 ↗
4. Combining Kohonen maps with Arima time series models to forecast traffic flow10.1016/S0968-090X(97)82903-8 ↗
5. Modeling and forecasting vehicular traffic flow as a seasonal Arima process: Theoretical basis and empirical results10.1061/(ASCE)0733-947X(2003)129:6(664) ↗
6. Short-term traffic flow prediction using seasonal Arima model with limited input data10.1007/s12544-015-0170-8 ↗
7. Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification10.1016/j.trc.2014.02.006 ↗
8. Traffic flow prediction using Kalman filtering technique10.1016/j.proeng.2017.04.417 ↗
9. k-Nearest neighbor model for multiple-time-step prediction of short-term traffic condition10.1061/(ASCE)TE.1943-5436.0000816 ↗
10. Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions10.1016/j.eswa.2008.07.069 ↗
11. Jan, F., Shah, I., and Ali, S. (2022). Short-Term Electricity Prices Forecasting Using Functional Time Series Analysis. Energies, 15.10.3390/en15093423 ↗
12. Functional Data Approach for Short-Term Electricity Demand Forecasting10.1155/2022/6709779 ↗
13. Ahmed, M.S., and Cook, A.R. (1979). Analysis of Freeway Traffic Time-Series Data by Using Box-Jenkins Techniques, Transportation Research Board.
14. Yu, Y., Wang, J., Song, M., and Song, J. (2010, January 13–14). Network traffic prediction and result analysis based on seasonal ARIMA and correlation coefficient. Proceedings of the 2010 International Conference on Intelligent System Design and Engineering Application, Changsha, China.10.1109/ISDEA.2010.335 ↗
15. A unified spatiotemporal model for short-term traffic flow prediction10.1109/TITS.2018.2873137 ↗
16. An improved k-nearest neighbor model for short-term traffic flow prediction10.1016/j.sbspro.2013.08.076 ↗
17. Real-time road traffic state prediction based on kernel-KNN
18. Short-term traffic forecasting: An adaptive ST-KNN model that considers spatial heterogeneity10.1016/j.compenvurbsys.2018.05.009 ↗
19. Deep and embedded learning approach for traffic flow prediction in urban informatics10.1109/TITS.2019.2909904 ↗
20. A fast learning algorithm for deep belief nets10.1162/neco.2006.18.7.1527 ↗
21. Deep belief networks10.4249/scholarpedia.5947 ↗
22. Deep architecture for traffic flow prediction: Deep belief networks with multitask learning10.1109/TITS.2014.2311123 ↗
23. Jin, Y., Xu, W., Wang, P., and Yan, J. (2018, January 16–19). SAE network: A deep learning method for traffic flow prediction. Proceedings of the 2018 5th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS), Hangzhou, China,.10.1109/ICCSS.2018.8572451 ↗
24. Short-term traffic flow prediction based on spatiotemporal analysis and CNN deep learning
25. Liu, Y., Zheng, H., Feng, X., and Chen, Z. (2017, January 11–13). Short-term traffic flow prediction with Conv-LSTM. Proceedings of the 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP), Nanjing, China.10.1109/WCSP.2017.8171119 ↗
26. Bruna, J., Zaremba, W., Szlam, A., and LeCun, Y. (2013). Spectral networks and locally connected networks on graphs. arXiv.
27. A graph convolutional method for traffic flow prediction in highway network10.1155/2021/1997212 ↗
28. Li, Z., Xiong, G., Chen, Y., Lv, Y., Hu, B., Zhu, F., and Wang, F.-Y. (2019, January 27–30). A hybrid deep learning approach with GCN and LSTM for traffic flow prediction. Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand.10.1109/ITSC.2019.8916778 ↗
29. Development and application of convolutional neural network model
30. Predicting human eye fixations via an LSTM-based saliency attentive model10.1109/TIP.2018.2851672 ↗
31. Li, F., Zhang, M., Fu, G., Qian, T., and Ji, D. (2016). A Bi-LSTM-RNN model for relation classification using low-cost sequence features. arXiv.