Results 261 to 270 of about 11,119,065 (317)

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
openaire   +2 more sources

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
openaire   +2 more sources

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
openaire   +4 more sources

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
openaire   +2 more sources

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
openaire   +2 more sources

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
openaire   +1 more source

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

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
openaire   +1 more source

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
openaire   +1 more source

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
openaire   +1 more source