Long-short term traffic prediction under road incidents using deep learning networks
Issued Date
2022-01-01
Resource Type
Scopus ID
2-s2.0-85134349676
Journal Title
2022 8th International Conference on Control, Decision and Information Technologies, CoDIT 2022
Start Page
500
End Page
505
Rights Holder(s)
SCOPUS
Bibliographic Citation
2022 8th International Conference on Control, Decision and Information Technologies, CoDIT 2022 (2022) , 500-505
Suggested Citation
Wiwatanapataphee B., Khajohnsaksumeth N., Wu Y.H., Jacoby G., Zhang X. Long-short term traffic prediction under road incidents using deep learning networks. 2022 8th International Conference on Control, Decision and Information Technologies, CoDIT 2022 (2022) , 500-505. 505. doi:10.1109/CoDIT55151.2022.9803895 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/84387
Title
Long-short term traffic prediction under road incidents using deep learning networks
Author's Affiliation
Other Contributor(s)
Abstract
This paper investigates the effectiveness of multi-variate deep learning models for traffic flow prediction with road incidents. Multiple features of the data are considered in the analysis including traffic features, road incident features and cyclical features. Three multivariate deep learning models based on the stacked LSTM network, the CNN LSTM network, and the Autoencoders-LSTM network are developed for long-short term traffic forecasting under road incidents. The results obtained from the analysis are then compared to determine the best suitable approach.