Travel Time Estimation Using Support Vector Regression on Model with 8 Features - Kosasih, Rifki; Mardhiyah, Iffatul (Scientific Journal of Informatics, 2022)
Purpose: In travelling, we need to predict travel time so that itinerary is as expected. This paper proposes Support Vector Regression (SVR) to build a prediction model. In this case, we will estimate travel time in the Bali area. We propose to use a regression model with 8 features, i.e., time, weather, route, wind speed, day, precipitation, temperature and humidity information.Methods: In this study, we collect real-time data from Global Positioning System (GPS) and weather applications. We divide our data into two types: training dataset consisting of 177 data and testing dataset comprising 51 data. The Support Vector Regression (SVR) method is used in the training stage to build a model representing data. To validate the model, error measurements were carried out by calculating the values of R2, Accuracy, MAE (Mean Absolute Error), RMSE (Root Mean Square Error) and Accuracy.Result: From the research results, the model obtained is the SVR model with parameters γ=0.125, ε=0.1 and C = 1000, which has a value of R2= 0.9860528612283006. Later, we predict travel times on testing data using the SVR model that has been obtained. Based on the result of the research, our model has a 0.8008 MAE (Mean Absolute Error), 1.2817 RMSE (Root Mean Square Error) and 95.3369% Accuracy.Novelty: In this study, we use 8 features to estimate travel time in the Bali area. Furthermore, we will compare the KNN regression method (previous studies) with Support Vector Regression (SVR) (proposed method) on a model with 8 features to estimate travel time.