Traffic flow prediction based on hybrid model using double exponential smoothing and 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.

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Short-term traffic flow prediction method based on grey adaptive particle swarm support vector machine

2016 2nd IEEE International Conference on Computer and Communications (ICCC) ◽

10.1109/compcomm.2016.7924913 ◽

2016 ◽

Author(s):

Mei Duo ◽

E. Xu ◽

Yan Qi

Keyword(s):

Support Vector Machine ◽

Traffic Flow ◽

Particle Swarm ◽

Prediction Method ◽

Support Vector ◽

Short Term ◽

Traffic Flow Prediction ◽

Flow Prediction

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Short-term traffic flow prediction with optimized Multi-kernel Support Vector Machine

2017 IEEE Congress on Evolutionary Computation (CEC) ◽

10.1109/cec.2017.7969326 ◽

2017 ◽

Cited By ~ 6

Author(s):

Xianyao Ling ◽

Xinxin Feng ◽

Zhonghui Chen ◽

Yiwen Xu ◽

Haifeng Zheng

Keyword(s):

Support Vector Machine ◽

Traffic Flow ◽

Support Vector ◽

Short Term ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Kernel Support Vector Machine

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Applying multiple kernel learning and support vector machine for solving the multicriteria and nonlinearity problems of traffic flow prediction

Journal of Advanced Transportation ◽

10.1002/atr.1217 ◽

2012 ◽

Vol 48 (3) ◽

pp. 250-271 ◽

Cited By ~ 6

Author(s):

Chenyun Yu ◽

Ka Chi Lam

Keyword(s):

Support Vector Machine ◽

Traffic Flow ◽

Multiple Kernel Learning ◽

Kernel Learning ◽

Support Vector ◽

Traffic Flow Prediction ◽

Multiple Kernel ◽

Flow Prediction

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Traffic Flow Prediction using Combination of Support Vector Machine and Rough Set

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.k2455.0981119 ◽

2019 ◽

Vol 8 (11) ◽

pp. 3334-3338

Keyword(s):

Support Vector Machine ◽

Traffic Flow ◽

Rough Set ◽

Traffic Control ◽

Support Vector ◽

Traffic Data ◽

Short Term ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Threshold Variation

Congestion is the primary issue related to traffic flow. Avoiding congestion after getting into is not possible. So the only way is to make the informed decision by knowing the traffic situation in advance. This can be achieved with the help of traffic flow prediction. In the proposed work, short term traffic flow prediction is performed using support vector machine in combination with rough set. Traffic data used for analysis is collected from three adjacent intersections of Nagpur city and traffic flow is predicted at downstream junction. The work has attempted to study the effect of aggregation intervals and past samples on the prediction performance using MSE threshold variation. Rough set is used as a post processor to validate the prediction result. Accurate and timely prediction can provide reliability for optimized traffic control and guidance.

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Short-Term Traffic Flow Prediction of Expressway: A Hybrid Method Based on Singular Spectrum Analysis Decomposition

Advances in Civil Engineering ◽

10.1155/2021/4313970 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Chunyan Shuai ◽

Zhengyang Pan ◽

Lun Gao ◽

HongWu Zuo

Keyword(s):

Traffic Flow ◽

Hybrid Model ◽

Spectrum Analysis ◽

Short Term Memory ◽

Singular Spectrum Analysis ◽

Support Vector ◽

Short Term ◽

Traffic Flow Prediction ◽

Singular Spectrum ◽

Flow Prediction

Real-time expressway traffic flow prediction is always an important research field of intelligent transportation, which is conducive to inducing and managing traffic flow in case of congestion. According to the characteristics of the traffic flow, this paper proposes a hybrid model, SSA-LSTM-SVR, to improve forecasting accuracy of the short-term traffic flow. Singular Spectrum Analysis (SSA) decomposes the traffic flow into one principle component and three random components, and then in terms of different characteristics of these components, Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) are applied to make prediction of different components, respectively. By fusing respective forecast results, SSA-LSTM-SVR obtains the final short-term predictive value. Experiments on the traffic flows of Guizhou expressway in January 2016 show that the proposed SSA-LSTM-SVR model has lower predictive errors and a higher accuracy and fitting goodness than other baselines. This illustrates that a hybrid model for traffic flow prediction based on components decomposition is more effective than a single model, since it can capture the main regularity and random variations of traffic flow.

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