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

2012 ◽  
Vol 48 (3) ◽  
pp. 250-271 ◽  
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

Traffic flow prediction based on combination of support vector machine and data denoising schemes

2019 ◽  
Vol 534 ◽  
pp. 120642 ◽  
Author(s):  
Jinjun Tang ◽  
Xinqiang Chen ◽  
Zheng Hu ◽  
Fang Zong ◽  
Chunyang Han ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Traffic Flow ◽  
Support Vector ◽  
Traffic Flow Prediction ◽  
Flow Prediction

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.

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

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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