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

Abstract

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.

Statistical Model in Predicting Traffic Congestion Among Selected Routes in Metro Manila

“…Predicting traffic congestion is a very challenging task since it has a lot of time-varying characteristics together with many uncertain factors like weather, time, and day (Deshpande and Bajaj, 2017). Hence, a predictive model incorporating all the given factors should be developed.…”

A Comparative Study of Parking Occupancy Prediction Methods considering Parking Type and Parking Scale

“…e results indicated that both model types are useful for accurate and cost-effective estimation of traffic volumes in Wyoming roads. Deshpande and Bajaj [15] discussed the implementation of the traffic flow prediction model using SVM based on the traffic data obtained near the Perungudi toll plaza in the IT corridor in Chennai, India. ey used a rough set to validate the prediction result.…”