Results 231 to 240 of about 154,707 (286)

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/
Guan, ZR, Zhang, P, Wong, SC, Xu, JM, Tan, MC   +4 more
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Short-Term Traffic Flow Prediction Based on XGBoost

2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS), 2018
Fast and accurate short-term traffic flow prediction is an important precondition for traffic analysis and control. Due to the fact that the short-term traffic flow has nonlinear characteristic and changes randomly, concurrent computation is difficult for traditional machine learning algorithms.
Xuchen Dong, Ting Lei, Shangtai Jin, Zhongsheng Hou   +3 more
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Highway Short-Term Traffic Flow Prediction with Traffic Flows from Multi Entry Stations

SAE Technical Paper Series, 2020
<div class="section abstract"><div class="htmlview paragraph">As an important component of the Intelligent Transportation System (ITS), short-term traffic flow prediction is a key step to assess the traffic situation. It provides suggestions for travellers and helps the administrators manage the traffic effectively.
Housong Ruan, BangYu Wu, Bin Li, Zhu Chen, Han Zhang   +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 ANFIS

2009 International Conference on Communication Software and Networks, 2009
Accurate short-term traffic flow prediction has become a critical problem in intelligent transportation systems (ITS). In the paper, a kind of adaptive prediction method for short-term traffic flow based on ANFIS (adaptive-network-based fuzzy interference system) model was presented.
Chen Bao-ping, Ma Zeng-qiang
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Short Term Traffic Flow Prediction Based on LSTM

2018 Ninth International Conference on Intelligent Control and Information Processing (ICICIP), 2018
Traffic flow prediction is important in modern traffic control and induction. Short-term traffic flow prediction plays an important role in urban traffic navigation planning and traffic optimization control. Due to the advantage in processing of time series data, LSTM is very suitable for predicting short-term traffic flow.
Jinhong Li, Lei Gao, Wei Song, Lu Wei, Yaxing Shi   +4 more
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Short-term traffic flow prediction using different techniques

IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society, 2011
Due to the complexity of traffic flow characteristics and the drawbacks of the traditional methods, the short-term predictions using the existing individual methods generally lack accuracy and robustness during all the time periods of the day. In order to overcome the drawbacks of traditional methods, the present paper proposes a fuzzy rule-based ...
Caixia Li, Sreenatha Gopalarao Anavatti, Tapabrata Ray   +2 more
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Short-term Traffic Flow Prediction Based on ConvLSTM Model

2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC), 2020
This paper proposes a estimation model based on Convolutional Long Short Term Memory (ConvLSTM) model to estimate short-term traffic flow. ConvLSTM is an improved algorithm based on Long Short Term Memory (LSTM) Network. It not only establishes timing characteristics like traditional LSTM models, but also depicts local spatial features like ...
Xiaoyu Chen, Xingsheng Xie, Da Teng
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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 Combination Predicted Model of Short Term Traffic Flow

2006 International Conference on Management Science and Engineering, 2006
In order to increase the precision of forecast, this paper proposes a combination forecasting model in short term traffic flow based on wavelet neural network. The model consists of the following stages: first, the relevant forecasting variable to the traffic flow is selected by use data mining technology such as the genetic algorithm; second, training
Liu Bin-sheng, Xing Zhan-wen, Yang Hai-tao, Hou Yu-peng   +3 more
openaire   +1 more source