scholarly journals An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning

2020 ◽  
Vol 12 (20) ◽  
pp. 8298
Author(s):  
Zhanzhong Wang ◽  
Ruijuan Chu ◽  
Minghang Zhang ◽  
Xiaochao Wang ◽  
Siliang Luan
Keyword(s):  
Prediction Model ◽  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Permutation Entropy ◽  
Support Vector ◽  
Gray Wolf ◽  
Highway Traffic ◽  
Traffic Flow Prediction ◽  
Flow Prediction

For intelligent transportation systems (ITSs), reliable and accurate real-time traffic flow prediction is an important step and a necessary prerequisite for alleviating traffic congestion and improving highway operation efficiency. In this paper, we propose an improved hybrid predicting model including two steps: decomposition and prediction to predict highway traffic flow. First, we adopted the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method to adaptively decompose the original nonlinear, nonstationary, and complex highway traffic flow data. Then, we used the improved weighted permutation entropy (IWPE) to obtain new reconstructed components. In the prediction step, we used the gray wolf optimizer (GWO) algorithm to optimize the least-squares support vector machine (LSSVM) prediction model established for each reconstruction component and integrate the prediction results of each subsequence to obtain the final prediction result. We experimentally validated the effectiveness of the proposed approach. The research results reveal that the proposed model is useful for predicting traffic flow and its changing trends and also allowing transportation officials to make more effective traffic decisions.

scholarly journals Spatiotemporal Traffic Flow Prediction with KNN and LSTM

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Xianglong Luo ◽  
Danyang Li ◽  
Yu Yang ◽  
Shengrui Zhang
Keyword(s):  
Support Vector Regression ◽  
Traffic Flow ◽  
Prediction Models ◽  
Short Term Memory ◽  
Transportation Systems ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Proposed Model

The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.

scholarly journals Urban Traffic Flow Prediction Model with CPSO/SSVM Algorithm under the Edge Computing Framework

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Fengkai Liu ◽  
Xingmin Ma ◽  
Xingshuo An ◽  
Guangnan Liang
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Traffic Flow ◽  
Urban Traffic ◽  
Support Vector ◽  
Swarm Optimization ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Chaotic Particle Swarm Optimization ◽  
Smooth Support Vector Machine

Urban traffic flow prediction has always been an important realm for smart city build-up. With the development of edge computing technology in recent years, the network edge nodes of smart cities are able to collect and process various types of urban traffic data in real time, which leads to the possibility of deploying intelligent traffic prediction technology with real-time analysis and timely feedback on the edge. In view of the strong nonlinear characteristics of urban traffic flow, multiple dynamic and static influencing factors involved, and increasing difficulty of short-term traffic flow prediction in a metropolitan area, this paper proposes an urban traffic flow prediction model based on chaotic particle swarm optimization algorithm-smooth support vector machine (CPSO/SSVM). The prediction model has built a new second-order smooth function to achieve better approximation and regression effects and has further improved the computational efficiency of the smooth support vector machine algorithm through chaotic particle swarm optimization. Simulation experiment results show that this model can accurately predict urban traffic flow.

Pruned Fast Learning Fuzzy Approach for Data-Driven Traffic Flow Prediction

2016 ◽  
Vol 20 (7) ◽  
pp. 1181-1191 ◽  
Author(s):  
Chengdong Li ◽  
◽  
Yisheng Lv ◽  
Jianqiang Yi ◽  
Guiqing Zhang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Fuzzy Rule ◽  
Transportation Systems ◽  
Fuzzy Rules ◽  
Data Driven ◽  
Fuzzy Approach ◽  
Traffic Flow Prediction ◽  
Fast Learning ◽  
Flow Prediction

Traffic flow prediction plays an important role in intelligent transportation systems. With the rapid growth of traffic flow data, fast and accurate traffic flow prediction methods are now required. In this paper, we propose a novel fast learning data-driven fuzzy approach for the traffic flow prediction problem. In the proposed approach, to achieve fast learning, an extreme learning machine is utilized to optimize the consequent parameters of the fuzzy rules. Further, a fuzzy rule pruning strategy that involves measuring the firing levels of the fuzzy rules is presented to obtain reduced fuzzy inference systems. To evaluate the performance of the proposed approach, it was experimentally applied to traffic flow prediction and its results compared with those of widely used methods. The experimental results verify that the proposed approach can achieve satisfactory performance. The comparisons show that the proposed approach can obtain better (sometimes similar) performances, but with a simpler structure, fewer parameters, and much faster learning speed than the other methods.

scholarly journals Short-Term Traffic Flow Prediction of Highway Network Based on Network-Constrained Lasso and NN

2018 ◽  
Vol 160 ◽  
pp. 07003
Author(s):  
Cong Wu ◽  
Zhaozheng Chen ◽  
Xiaofei Li
Keyword(s):  
Neural Networks ◽  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Back Propagation ◽  
Transportation Systems ◽  
Time Data ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Highway Network

Accurate and timely traffic flow prediction is important for the successful deployment of intelligent transportation systems. Most of existing methods have not made good use of information from adjacent sections to analyse the trends of the object section. A new method for traffic flow prediction of highway network, namely network-constrained Lasso (Least absolute shrinkage and selection operator) and Neural Networks, was proposed. Unlike existing methods, our approach incorporated all the spatial and temporal information available in a highway network to carry our short-term traffic flow prediction for the objective section. To capture the spatial correlation of traffic network, the Laplacian matrix was introduced to describe the highway network structure. Subsequently, a network-constrained Lasso method was applied for sparse variable selection. With the extracted historic and real-time data, the back propagation neural networks were implemented to predict traffic flow at different time intervals in future. The experimental results verified that the proposed method could achieve above 90% average accuracy in the 30-minutes speed predictions for 78 road sections.

scholarly journals Short-time traffic flow prediction based on improved LSSVM

2021 ◽  
Author(s):  
K. Ueda ◽  
S. Abe ◽  
Z. Shen
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Traffic Flow ◽  
Support Vector ◽  
Local Optimum ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Update Strategy ◽  
Short Time ◽  
Prediction Capability

Abstract In order to improve the accuracy of short-time traffic flow prediction, an improved LSSVM-based short-time traffic flow prediction model is proposed. To address the problem that the traditional hybrid frog-jumping algorithm (SFLA) easily falls into local optimum, an improved hybrid frog-jumping algorithm (ISFLA) based on a new local update strategy is proposed, which is combined with the least squares support vector machine (LSSVM) to improve the prediction capability of LSSVM by using this algorithm to optimize the key parameters of LSSVM. The model and algorithm are simulated and analyzed with examples to prove the feasibility of the model and the effectiveness of the algorithm.

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