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Open AccessArticle

Short-Term Traffic State Prediction Based on the Spatiotemporal Features of Critical Road Sections

by 1, 1,2, 1,2,*, 1,2,3 and 4
1
School of Transportation Science and Engineering, Beijing Key Laboratory for Cooperative Vehicle Infrastructure System and Safety Control, Beihang University, Beijing 100191, China
2
Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing 100191, China
3
Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Sipailou 2, Nanjing 210096, China
4
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(7), 2287; https://doi.org/10.3390/s18072287
Received: 17 May 2018 / Revised: 2 July 2018 / Accepted: 10 July 2018 / Published: 14 July 2018
(This article belongs to the Section Intelligent Sensors)
Recently, short-term traffic prediction under conditions with corrupted or missing data has become a popular topic. Since a road section has predictive power regarding the adjacent roads at a specific location, this paper proposes a novel hybrid convolutional long short-term memory neural network model based on critical road sections (CRS-ConvLSTM NN) to predict the traffic evolution of global networks. The critical road sections that have the most powerful impact on the subnetwork are identified by a spatiotemporal correlation algorithm. Subsequently, the traffic speed of the critical road sections is used as the input to the ConvLSTM to predict the future traffic states of the entire network. The experimental results from a Beijing traffic network indicate that the CRS-ConvLSTM outperforms prevailing deep learning (DL) approaches for cases that consider critical road sections and the results validate the capability and generalizability of the model when predicting with different numbers of critical road sections. View Full-Text
Keywords: short-term traffic prediction; structural missing data; deep learning; critical road sections; spatiotemporal correlation short-term traffic prediction; structural missing data; deep learning; critical road sections; spatiotemporal correlation
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MDPI and ACS Style

Yang, G.; Wang, Y.; Yu, H.; Ren, Y.; Xie, J. Short-Term Traffic State Prediction Based on the Spatiotemporal Features of Critical Road Sections. Sensors 2018, 18, 2287. https://doi.org/10.3390/s18072287

AMA Style

Yang G, Wang Y, Yu H, Ren Y, Xie J. Short-Term Traffic State Prediction Based on the Spatiotemporal Features of Critical Road Sections. Sensors. 2018; 18(7):2287. https://doi.org/10.3390/s18072287

Chicago/Turabian Style

Yang, Gang; Wang, Yunpeng; Yu, Haiyang; Ren, Yilong; Xie, Jindong. 2018. "Short-Term Traffic State Prediction Based on the Spatiotemporal Features of Critical Road Sections" Sensors 18, no. 7: 2287. https://doi.org/10.3390/s18072287

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