Freeway Traffic Flow Model Based on Rough Sets and Elman Neural Network

Liang, Xun; Fan, Y. K.; Li, Jianye

doi:10.1109/iciecs.2009.5362604

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…Pang Ming-bao and HE Guo-guang [11] suggested chaos recognition model using rough set and neural network where rough set theory was used to obtain reduced feature vector. Xinrong Liang et al [12] proposed elman neural network based traffic flow prediction where original data is reduced using rough set theory.…”

Section: Minal Deshpande Preeti Bajajmentioning

confidence: 99%

Traffic Flow Prediction using Combination of Support Vector Machine and Rough Set

Deshpande¹,

Bajaj²

2019

IJITEE

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 Abstract: Congestion is the primary issue related to traffic flow. Avoiding congestion after getting into is not possible. So the only way is to make the informed decision by knowing the traffic situation in advance. This can be achieved with the help of traffic flow prediction. In the proposed work, short term traffic flow prediction is performed using support vector machine in combination with rough set. Traffic data used for analysis is collected from three adjacent intersections of Nagpur city and traffic flow is predicted at downstream junction. The work has attempted to study the effect of aggregation intervals and past samples on the prediction performance using MSE threshold variation. Rough set is used as a post processor to validate the prediction result. Accurate and timely prediction can provide reliability for optimized traffic control and guidance.

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Section: Minal Deshpande Preeti Bajajmentioning

confidence: 99%

Traffic Flow Prediction using Combination of Support Vector Machine and Rough Set

Deshpande¹,

Bajaj²

2019

IJITEE

View full text Add to dashboard Cite

show abstract

“…Pang Ming-bao and HE Guo-guang proposed recognition model of chaos using rough set and neural network where rough set theory was used to obtain reduced feature vector [11]. Xinrong Liang et al proposed elman neural network based traffic flow prediction where original data is reduced using rough set theory [12].…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Improvement of Traffic Flow Prediction Model using Combination of Support Vector Machine and Rough Set

Deshpande¹,

Bajaj²

2017

IJCA

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Short term traffic flow prediction has become one of the important research fields in intelligent transportation system. The prediction of this traffic flow information quickly and accurately is important for traffic control and guidance to initiate the measuring steps well in advance. It makes the transport users better informed and makes the transport network smarter, safer and more coordinated. It plays a crucial role in individual dynamic route guidance, advance traffic information system (ATIS) and advance traffic management system (ATMS). This paper discusses the implementation of traffic flow prediction model using support vector machine. Rough set is used as a post processing tool to validate the prediction result. The objective is to improve traffic flow prediction performance. Data near Perungudi toll plaza in IT corridor in Chennai, India is used for the analysis. It is found that the use of rough set results in satisfactory performance improvement which is evaluated using mean square error as the performance measures.

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Freeway Traffic Flow Model Based on Rough Sets and Elman Neural Network

Cited by 2 publications

References 3 publications

Traffic Flow Prediction using Combination of Support Vector Machine and Rough Set

Traffic Flow Prediction using Combination of Support Vector Machine and Rough Set

Performance Improvement of Traffic Flow Prediction Model using Combination of Support Vector Machine and Rough Set

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