The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Travel time prediction with LSTM neural network
Abstract
Travel time is one of the key concerns among travelers before starting a trip and also an important indicator of traffic conditions. However, travel time acquisition is time delayed and the pattern of travel time is usually irregular. In this paper, we explore a deep learning model, the LSTM neural network model, for travel time prediction. By employing the travel time data provided by Highways England, we construct 66 series prediction LSTM neural networks for the 66 links in the data set. Through model training and validation, we obtain the optimal structure within the setting range for each link. Then we predict multi-step ahead travel times for each link on the test set. Evaluation results show that the 1-step ahead travel time prediction error is relatively small, the median of mean relative error for the 66 links in the experiments is 7.0% on the test set. Deep learning models considering sequence relation are promising in traffic series data prediction.
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Cites
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Self-citationAn Empirical Exploration of Recurrent Network Architectures
Józefowicz, R., Zaremba, W., & Sutskever, I. (2015) · Cited by 1,459
Travel-Time Prediction With Support Vector Regression
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Cited by
Zhang, Y., Xiong, R., He, H., & Pecht, M. G. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2805189 · Cited by 1,326
Cui, Z., Ke, R., Pu, Z., & Wang, Y. (2020) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2020.102674 · Cited by 486
Bandara, K., Bergmeir, C., & Smyl, S. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112896 · Cited by 435
DeepPF: A deep learning based architecture for metro passenger flow prediction
Liu, Y., Liu, Z., & Jia, R. (2019) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2019.01.027 · Cited by 426
Machine learning for next-generation intelligent transportation systems: A survey
Yuan, T., Da Rocha Neto, W., Rothenberg, C. E., Obraczka, K., Barakat, C., & Turletti, T. (2022) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.4427 · Cited by 179
Metadata
- Authors
- Duan, Y., L.V., Y., & Wang, F.-Y.
- Venue
- 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)
- Publisher
- IEEE
- Published
- 2016-11
- Pages
- 1053-1058
- OpenAlex
- W2564701384
- Cited by
- 336
- References
- 25
- FWCI
- 47.61
- Type
- conference paper
- Language
- en
- Open access
- closed
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