Weather-Aware Long-Range Traffic Forecast Using Multi-Module Deep Neural Network
Source abstract
Abstract
This study proposes a novel multi-module deep neural network framework which aims at improving intelligent long-term traffic forecasting. Following our previous system, the internal architecture of the new system adds deep learning modules that enable data separation during computation. Thus, prediction becomes more accurate in many sections of the road network and gives dependable results even under possible changes in weather conditions during driving. The performance of the framework is then evaluated for different cases, which include all plausible cases of driving, i.e., regular days, holidays, and days involving severe weather conditions. Compared with other traffic predicting systems that employ the convolutional neural networks, k-nearest neighbor algorithm, and the time series model, it is concluded that the system proposed herein achieves better performance and helps drivers schedule their trips well in advance.
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 Electrical Engineering, Korea University, Seoul 02841, Korea
References (28)
Show source-provided references
1. Intelligent Traffic Control System Based on Cloud Computing and Big Data Mining10.1109/TII.2019.2929060 ↗
2. Zhu, L., Guo, F., Krishnan, R., and Polak, J.W. (2018, January 4–7). A Deep Learning Approach for Traffic Incident Detection in Urban Networks. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.10.1109/ITSC.2018.8569402 ↗
3. Barbancho, J., Ropero, J., Luque, J., Caraballo, A., and León, C. (2019). Social Parking: Applying the Citizens as Sensors Paradigm to Parking Guidance and Information. Sustainability, 11.10.3390/su11236549 ↗
4. LSTM Network: A Deep Learning Approach for Short-Term Traffic Forecast10.1049/iet-its.2016.0208 ↗
5. Ma, X., Dai, Z., He, Z., Ma, J., Wang, Y., and Wang, Y. (2017). Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction. Sensors, 17.10.3390/s17040818 ↗
6. Impedovo, D., Dentamaro, V., Pirlo, G., and Sarcinella, L. (2019). TrafficWave: Generative Deep Learning Architecture for Vehicular Traffic Flow Prediction. Appl. Sci., 9.10.3390/app9245504 ↗
7. An Evaluation of HTM and LSTM for Short-Term Arterial Traffic Flow Prediction10.1109/TITS.2018.2843349 ↗
8. Deng, D., Shahabi, C., Demiryurek, U., and Zhu, L. (2017, January 18–21). Situation Aware Multi-task Learning for Traffic Prediction. Proceedings of the 2017 IEEE International Conference on Data Mining (ICDM), New Orleans, LA, USA.10.1109/ICDM.2017.17 ↗
9. Pattern Sensitive Prediction of Traffic Flow Based on Generative Adversarial Framework10.1109/TITS.2018.2857224 ↗
10. Fouladgar, M., Parchami, M., Elmasri, R., and Ghaderi, A. (2017, January 14–19). Scalable Deep Traffic Flow Neural Networks for Urban Traffic Congestion Prediction. Proceedings of the 2017 IEEE International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.10.1109/IJCNN.2017.7966128 ↗
11. Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method10.1155/2017/6575947 ↗
12. Improving Traffic Flow Prediction With Weather Information in Connected Cars: A Deep Learning Approach10.1109/TVT.2016.2585575 ↗
13. Investigating Long-Term Vehicle Speed Prediction based on BP-LSTM Algorithms10.1049/iet-its.2018.5593 ↗
14. k-Nearest Neighbor Model for Multiple-Time-Step Prediction of Short-Term Traffic Condition10.1061/(ASCE)TE.1943-5436.0000816 ↗
15. Short-Term Traffic Flow Rate Forecasting based on Identifying Similar Traffic Patterns10.1016/j.trc.2015.08.017 ↗
16. Sun, B., Cheng, W., Goswami, P., and Bai, G. (2017, January 3–6). Flow-Aware WPT k-Nearest Neighbours Regression for Short-Term Traffic Prediction. Proceedings of the 2017 IEEE Symposium on Computers and Communications (ISCC), Heraklion, Greece.
17. Adaptive Multi-Kernel SVM With Spatial–Temporal Correlation for Short-Term Traffic Flow Prediction10.1109/TITS.2018.2854913 ↗
18. Mehta, S., Hajishirzi, H., and Rastegari, M. (2019). DiCENet: Dimension-wise Convolutions for Efficient Networks. arXiv.10.1109/TPAMI.2020.3041871 ↗
19. Hybrid Traffic Prediction Scheme for Intelligent Transportation Systems based on Historical and Real-Time Data
20. Long-Term Forecasting Oriented to Urban Expressway Traffic Situation10.1177/1687814016628397 ↗
21. Ryu, S., and Kim, D. (2019, January 15–19). Intelligent Highway Traffic Forecast Based on Deep Learning and Restructured Road Models. Proceedings of the 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), Milwaukee, WI, USA.10.1109/COMPSAC.2019.10192 ↗
22. Assessing the Impact of Weather on Traffic Intensity10.1175/2009WCAS1014.1 ↗
23. METANET: A Macroscopic Simulation Program for Motorway Networks
24. (2020, January 13). Korea Meteorological Administration. Available online: Http://kma.go.kr.
25. Kacprzyk, J., and Pedrycz, W. (2015). Deep and Modular Neural Networks. Springer Handbook of Computational Intelligence, Springer.10.1007/978-3-662-43505-2 ↗
26. Analysis of freeway traffic time-series data by using Box–Jenkins techniques
27. Kingma, D.P., and Ba, J. (2017). Adam: A Method for Stochastic Optimization. arXiv.
28. (2020, January 13). Korean Expressway Public Data Portal. Available online: Http://data.ex.co.kr.