scholarly journals Traffic flow prediction models – A review of deep learning techniques

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Anirudh Ameya Kashyap ◽  
Shravan Raviraj ◽  
Ananya Devarakonda ◽  
Shamanth R Nayak K ◽  
Santhosh K V ◽  
...  
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Prediction Models ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Learning Techniques

scholarly journals A Research of Traffic Prediction using Deep Learning Techniques

2019 ◽  
Vol 8 (9S2) ◽  
pp. 725-728
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Transport Systems ◽  
Traffic Data ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Learning Techniques ◽  
Modern Technologies

Traffic data is very important in designing a smart city. Now –a day’smany intelligent transport systems use modern technologies to predict traffic flow, to minimize accidents on road, to predict speed of a vehicle and etc. The traffic flow prediction is an appealing study field. Many techniques of data mining are employed to forecast traffic. Deep learning techniques can be used with technological progress to prevent information from real time. Deep algorithms are discussed to forecast real-world traffic data. When traffic data becomes big data, some techniques to improve the accuracy of trafficprediction are also discussed.

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.

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