Results 221 to 230 of about 154,459 (280)

Spatio-temporal transformer traffic prediction network based on multi-level causal attention. [PDF]

PLoS One
He H, Long Z, Zhang Y, Jiang X.
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Short-term traffic flow prediction in bike-sharing networks

Journal of Intelligent Transportation Systems, 2021
For station-based bike-sharing systems, the balance between user demand and bike allocation is critical for the operation.
Bo Wang 0121, Hai Le Vu, Inhi Kim, Chen Cai   +3 more
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Application of LSTM in Short-term Traffic Flow Prediction

2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE), 2020
As urbanization intensifies, the status of the traffic situation predict is becoming more and more prominent. The urban traffic flow is influenced by many factors and is characterized by strong randomness. This paper combines MSE and Adam to construct a linear LSTM to realize the prediction of short-term traffic flow based on time series.
Chuanli Kang, Zhenyu Zhang
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A Hybrid Method for Short-Term Traffic Flow Prediction

2020 12th International Conference on Advanced Computational Intelligence (ICACI), 2020
We analyze the problem of short-term traffic flow prediction using a hybrid approach composed of regression and optimization. First, the data preprocessing method is described for the scenario of traffic flow prediction. Second, extreme gradient boosting is used to generate boosted trees that are then used to transform the input of each record. Finally,
Wei Song 0004, Taolin Yin
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An interpretable model for short term traffic flow prediction

Mathematics and Computers in Simulation, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wei Wang 0140, Hanyu Zhang, Tong Li 0004, Jianhua Guo 0001, Wei Huang 0017, Yun Wei, Jinde Cao   +6 more
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Short-term traffic flow prediction: From the perspective of traffic flow decomposition

Neurocomputing, 2020
Abstract Some researchers treat traffic flow as an entirety while predicting short-term traffic flow. Through analyzing real-world traffic flow, we have found that urban traffic shows a stable changing process along with random disturbs. An alternative way is to decompose traffic flow into two components: periodicity and volatility.
Li Chen, Linjiang Zheng, Jie Yang 0044, Dong Xia, Weining Liu   +4 more
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An Innovative Approach for the Short-term Traffic Flow Prediction

Journal of Systems Science and Systems Engineering, 2021
Traffic flow prediction plays an important role in intelligent transportation applications, such as traffic control, navigation, path planning, etc., which are closely related to people’s daily life. In the last twenty years, many traffic flow prediction approaches have been proposed.
Xing Su, Minghui Fan, Minjie Zhang, Yi Liang, Limin Guo   +4 more
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An Aggregation Approach to Short-Term Traffic Flow Prediction

IEEE Transactions on Intelligent Transportation Systems, 2009
In this paper, an aggregation approach is proposed for traffic flow prediction that is based on the moving average (MA), exponential smoothing (ES), autoregressive MA (ARIMA), and neural network (NN) models. The aggregation approach assembles information from relevant time series. The source time series is the traffic flow volume that is collected 24 h/
Man-Chun Tan, Sze Chun Wong, Jian-Min Xu, Zhan-Rong Guan, Peng Zhang 0014   +4 more
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Short-term traffic flow prediction with Conv-LSTM

2017 9th International Conference on Wireless Communications and Signal Processing (WCSP), 2017
The accurate short-term traffic flow prediction can provide timely and accurate traffic condition information which can help one to make travel decision and mitigate the traffic jam. Deep learning (DL) provides a new paradigm for the analysis of big data generated by the urban daily traffic.
Yipeng Liu, Haifeng Zheng, Xinxin Feng, Zhonghui Chen   +3 more
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Short-Term Traffic Flow Prediction Based on SVR and LSTM

2021
To alleviate traffic congestion and support the development of real-time traffic and public transport, this paper conducts research on adopting support vector regression (SVR) and long short term memory (LSTM) to predict traffic flow of the lane, and then compares the results with that using the quadratic exponential smoothing.
Yi Wang, Jiahao Xu, Xianwu Cao, Ruiguan He, Jixiang Cao   +4 more
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