scholarly journals Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier

Sensors ◽  
2018 ◽  
Vol 18 (6) ◽  
pp. 1696 ◽  
Author(s):  
Hesham El-Sayed ◽  
Sharmi Sankar ◽  
Yousef-Awwad Daraghmi ◽  
Prayag Tiwari ◽  
Ekarat Rattagan ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Transport System ◽  
Vehicular Networks ◽  
Intelligent Transport System ◽  
Intelligent Transport ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Parametric Classifier ◽  
Non Parametric

scholarly journals Machine Learning and Deep Learning Models for Traffic Flow Prediction: A Survey

2020 ◽  
Author(s):  
Ayele Gobezie ◽  
Marta Sintayehu Fufa
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Traffic Flow ◽  
Series Data ◽  
Intelligent Transport System ◽  
Learning Approaches ◽  
Traffic Flow Prediction ◽  
Traffic Regulation ◽  
Flow Prediction ◽  
The Impact

Abstract Traffic congestion is one of the problems for cities around the world due to the rapid increasing of vehicles in urbanization. Traffic flow prediction is of a great importance for Intelligent Transport System (ITS) which helps to optimize the traffic regulation of a transportation in the city. Nowadays, several researches have been studied so far on traffic flow prediction, accurate prediction has not yet been exploited by most of existing studies due to the impact of inability to effectively deal with spatial temporal features of the times series data. Traffic information in transportation system will also be affected by different factors. In this research we intended to study various models for Traffic flow prediction on the basis machine learning and deep learning approaches. Factors affecting the performance of traffic flow prediction intensity are studied as well. Benchmark performance evaluation metrics are also reviewed. Generally, this manuscript covers relevant methods and approaches, review the state-of-art works with respect to different traffic flow prediction technique help researchers in exploring future directions so as to realize robust traffic flow prediction.

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.

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