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Jul 2, 2019
Prediction of Distribution of Traffic Congestion on High Traffic Density Region Based on Deep Learning
Authors: Li Zhang [email protected], Nan Ji [email protected], Sheng Li [email protected], Haiyang Yu [email protected], Yilong Ren [email protected], and Can Yang [email protected]Author Affiliations
Publication: CICTP 2019
Abstract
With the rapid development of China’s economy, traffic congestion has become a serious problem affecting the efficiency and safety of the traffic system, especially in urban regions with high traffic density. Due to the lack of effective forecasting methods, traffic congestion events seriously affect normal operation of the intracity traffic network. In order to achieve better prediction results, a type of gated recurrent neural network—long short-term memory neural networks—were used to build the model. The prediction accuracies for different tasks all approach 85%. Then, several different factors which may influence the congestion prediction were analyzed to find why LSTM could not fit the congestion change better. In order to have a comprehensive understanding of the model based on the LSTMs, several algorithms were studied by building models. As the result, the prediction accuracies of these new models are noticeably lower than those of the LSTM models.
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© 2019 American Society of Civil Engineers.
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Published online: Jul 2, 2019
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School of Transportation Science and Engineering, Beihang Univ., Xue Yuan Rd. No. 37, HaiDian District, Beijing 100191, China. E-mail: [email protected]
School of Transportation Science and Engineering, Beihang Univ., Xue Yuan Rd. No. 37, HaiDian District, Beijing 100191, China. E-mail: [email protected]
School of Transportation Science and Engineering, Beihang Univ., Xue Yuan Rd. No. 37, HaiDian District, Beijing 100191, China; Beijing Key Laboratory of Vehicle Road Coordination and Safety Control, Beijing 100191, China. E-mail: [email protected]
School of Transportation Science and Engineering, Beihang Univ., Xue Yuan Rd. No. 37, HaiDian District, Beijing 100191, China; Beijing Key Laboratory of Vehicle Road Coordination and Safety Control, Beijing 100191, China; Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang Univ., Beijing 100191, China. E-mail: [email protected]
School of Transportation Science and Engineering, Beihang Univ., Xue Yuan Rd. No. 37, HaiDian District, Beijing 100191, China; Beijing Key Laboratory of Vehicle Road Coordination and Safety Control, Beijing 100191, China; Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang Univ., Beijing 100191, China. E-mail: [email protected]
Hefei Innovation Research Institute, Beihang Univ., Xinzhan Hi-Tech District, Anhui 230013, China. E-mail: [email protected]
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