Urban Traffic Flow Prediction Using a Spatio-Temporal Random Effects Model

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.

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CCTV Video Handling and Traffic Network Modeling Technique for Urban Traffic Flow Prediction

KIISE Transactions on Computing Practices ◽

10.5626/ktcp.2021.27.6.289 ◽

2021 ◽

Vol 27 (6) ◽

pp. 289-294

Author(s):

Hanbyul Yeon ◽

Seongbum Seo ◽

Hyein Hong ◽

Yun Jang

Keyword(s):

Traffic Flow ◽

Network Modeling ◽

Urban Traffic ◽

Modeling Technique ◽

Traffic Network ◽

Traffic Flow Prediction ◽

Flow Prediction

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A deep-learning model for urban traffic flow prediction with traffic events mined from twitter

World Wide Web ◽

10.1007/s11280-020-00800-3 ◽

2020 ◽

Cited By ~ 2

Author(s):

Aniekan Essien ◽

Ilias Petrounias ◽

Pedro Sampaio ◽

Sandra Sampaio

Keyword(s):

Deep Learning ◽

Traffic Flow ◽

Learning Model ◽

Urban Traffic ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Deep Learning Model

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Urban traffic flow prediction based on road network model

Proceedings of 2012 9th IEEE International Conference on Networking, Sensing and Control ◽

10.1109/icnsc.2012.6204940 ◽

2012 ◽

Cited By ~ 6

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

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

Wireless Communications and Mobile Computing ◽

10.1155/2020/8871998 ◽

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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