Journal
NEUROCOMPUTING
Volume 318, Issue -, Pages 297-305Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2018.08.067
Keywords
Traffic flow prediction; Intelligent transportation systems; Deep learning; LSTM
Categories
Funding
- National Natural Science Foundation of China [61602407, 61472363]
- Opening Foundation of Engineering Research Center of Intelligent Transport of Zhejiang Province [2016ERCITZJ-KF02, 2017ERCITZJ-KF04]
Ask authors/readers for more resources
Traffic flow prediction plays a key role in intelligent transportation systems. However, since traffic sensors are typically manually controlled, traffic flow data with varying length, irregular sampling and missing data are difficult to exploit effectively. To overcome this problem, we propose a novel approach that is based on Long Short-Term Memory (LSTM) in this paper. In addition, the multiscale temporal smoothing is employed to infer lost data and the prediction residual is learned by our approach. We demonstrate the performance of our approach on both the Caltrans Performance Measurement System (PeMS) data set and our own traffic flow data set. According to the experimental results, our approach obtains higher accuracy in traffic flow prediction compared with other approaches. (C) 2018 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available