Results 231 to 240 of about 154,459 (280)

Short-Term Traffic Flow Prediction Based on Hybrid Model

2020
Accurate and reliable short-term traffic flow prediction can provide effective help for people’s travel and road planning. In order to improve the accuracy of short-term traffic flow prediction, this paper proposes a hybrid model of improve long-term short-term memory (LSTM) and radial basis function neural network (RBFNN).
Yong Hu, Meng Yu 0005, Guanxiang Yin, Fei Du, Meng Wang 0032, Yuejin Zhang   +5 more
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Prediction of Short-Term Traffic Flow Based on Similarity

Journal of Highway and Transportation Research and Development (English Edition), 2016
AbstractTo improve the precision of short-term traffic flow prediction and to enhance the accuracy of programming as well as of traffic flow management, a novel short-term traffic flow prediction m...
Chun-xia Yang, Rui Fu, Yi-qin Fu
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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
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Short-Term Traffic Flow Prediction: Using LSTM

2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3), 2020
Traffic data is being exploded in past few years and that is because of the increasing number of vehicles. People get struck in the traffic for hours so, accurate flow of traffic is really important for both the traveler and intelligent transportation system. Existing models somehow fails to provide accurate information of flow and that is because they
Pregya Poonia, V. K. Jain
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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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The Short-Term Traffic Flow Prediction Based on MapReduce

2016
Short-term traffic volume forecasting represents a critical need for Intelligent Transportation Systems. In this paper, we propose an improved K-Nearest Neighbor model, named I-KNN, in a general MapReduce framework of distributed modeling on a Hadoop platform, to enhance the accuracy and efficiency of short-term traffic flow forecasting.
Suping Liu, Dongbo Zhang 0001
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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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A Short-term Traffic Flow Prediction Model Based on AutoEncoder and GRU

2020 12th International Conference on Advanced Computational Intelligence (ICACI), 2020
To solve the problem of low prediction accuracy and poor robustness due to the short-term prediction only adopts the time series of current link traffic flow and fails to consider the spatial relationship in traffic flow data, this paper proposes a hybrid deep learning method considering the spatialtemporal correlation of traffic flow called ...
Dejun Chen, Hao Wang, Ming Zhong 0004
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Broad Learning for Optimal Short-Term Traffic Flow Prediction

2019
In this work, we explore the use of a Broad Learning System (BLS) as a way to replace deep learning architectures for traffic flow prediction. BLS is shown to not only outperforms standard learning algorithms (Least absolute shrinkage and selection operator (LASSO), shallow and deep neural networks, stacked autoencoders) in terms of training time, but ...
Di Liu 0001, Wenwu Yu, Simone Baldi
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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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