2018
DOI: 10.1186/s40537-018-0157-0
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Abstract: India is the second most dense and populated country in the world and one of the fastest growing economies. It is experiencing extreme congestion problems on road specifically on undivided two lane highways with mixed traffic. Facilitating infrastructure, imposing proper taxes to restrict personal vehicle growth and enhancing public transport facilities are long term solutions to this problem. These permanent solutions need government's involvement. The Indian government has spent a huge amount in the urban in… Show more

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Cited by 57 publications
(29 citation statements)
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“…In particular, SVR was proposed for short-term traffic flow forecasting in [3], which is an online learning weighted algorithm. Meanwhile, the work of [12] shows that ANN has been applied in short-term traffic flow forecasting and shown a superior performance than other traditional ML approaches.…”
Section: A Traditional Machine Learningmentioning
confidence: 99%
See 1 more Smart Citation
“…In particular, SVR was proposed for short-term traffic flow forecasting in [3], which is an online learning weighted algorithm. Meanwhile, the work of [12] shows that ANN has been applied in short-term traffic flow forecasting and shown a superior performance than other traditional ML approaches.…”
Section: A Traditional Machine Learningmentioning
confidence: 99%
“…Consequently, the robustness of model can be improved and the computational cost for traffic flow prediction can be reduced. We then combine results Y wide [11][12][13][14]. Finally, We select the root mean square prop (RMSProp) optimizer to minimize the square errors between the prediction value and the actual target value (line 15).…”
Section: Algorithm Analysismentioning
confidence: 99%
“…All the aforementioned work requires significant prior domain knowledge and feature engineering to achieve better performance. ANN model in [34] have the advantage over the previous algorithms due to its capability to work with multi-dimensional data without any feature engineering, and also due to its potential to perceive the non-linear relationship between input and output features to provide generalized solutions. However, due to its shallow depth in architecture, the accuracy of the model was not satisfactory, so the researchers shift their research directed toward the deep machine learning architecture.…”
Section: Related Workmentioning
confidence: 99%
“…The MLP model generally works well in the capture of complex and non-linear relations, but it usually requires a large volume of data and complex training. Many researchers, therefore, consider it as the most commonly implemented network topology [44][45][46]. Recently, in the study by Chen et al, they adapted a novel approach using dynamic graph hybrid automata for the modeling and estimation of density on an urban freeway in the city of Beijing, China [47].…”
Section: Related Workmentioning
confidence: 99%