Urban traffic flow prediction based on road network model

2012 ◽  
Author(s):  
Yanyan Xu ◽  
Qing-Jie Kong ◽  
Shu Lin ◽  
Yuncai Liu
Keyword(s):  
Traffic Flow ◽  
Network Model ◽  
Road Network ◽  
Urban Traffic ◽  
Traffic Flow Prediction ◽  
Flow Prediction

scholarly journals Short-term traffic flow prediction based on cuckoo search-wavelet neural network

2021 ◽  
Author(s):  
Haibo Lv ◽  
Yuheng Kang ◽  
Zhou Shen
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Network Model ◽  
Cuckoo Search ◽  
Wavelet Neural Network ◽  
Urban Traffic ◽  
Prediction Algorithm ◽  
Step Size ◽  
Traffic Flow Prediction ◽  
Flow Prediction

Abstract The nonlinear fluctuation and uncertainty that characterize urban traffic flow are well-known. An Improved Cuckoo Search-Wavelet Neural Network (ICS-WNN) prediction model for urban traffic flow is suggested in order to increase the accuracy of traffic flow predictions. After the original traffic flow data have been cleaned up and normalized, the traffic flow prediction network model is built by optimizing the wavelet neural network weights and wavelet shrinkage and translation factors based on the adaptive step size and discovery probability of the cuckoo algorithm, and then adding the neural network momentum factor. The traffic flow prediction network model is built in two stages. The results of the experimental simulations demonstrate that the ICS-WNN prediction algorithm has a better fit and accuracy than numerous common optimization prediction techniques, which is encouraging.

scholarly journals Multidimensional Scaling-Based Data Dimension Reduction Method for Application in Short-Term Traffic Flow Prediction for Urban Road Network

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yi Zhao ◽  
Satish V. Ukkusuri ◽  
Jian Lu
Keyword(s):  
Dimension Reduction ◽  
Traffic Flow ◽  
Reduction Method ◽  
Prediction Models ◽  
Urban Traffic ◽  
Dimension Reduction Method ◽  
Short Term ◽  
Urban Road ◽  
Traffic Flow Prediction ◽  
Flow Prediction

This study develops a multidimensional scaling- (MDS-) based data dimension reduction method. The method is applied to short-term traffic flow prediction in urban road networks. The data dimension reduction method can be divided into three steps. The first is data selection based on qualitative analysis, the second is data grouping using the MDS method, and the last is data dimension reduction based on a correlation coefficient. Backpropagation neural network (BPNN) and multiple linear regression (MLR) models are employed in four kinds of urban traffic environments to test whether the proposed method improves the prediction accuracy of traffic flow. The results show that prediction models using traffic data after dimension reduction outperform the same prediction models using other datasets. The proposed method provides an alternative to existing models for urban traffic 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.

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