Travel Time Estimation for Destination In Bali Using kNN-Regression Method with Tensorflow

Abstract

On a tour activity, travel time estimation is needed so that the travel itinerary goes according to the plan. Travel time estimation is very important so we can estimate the time needed to arrive at the destinations in the travel itinerary. Therefore we need a method that can estimate travel time from one place to another. In this study, we propose the k-Nearest Neighbors Regression (kNN-Regression) method with Tensorflow to construct an estimation model. The proposed number of features in our estimation model is 8 features, i.e. zone information, time information, day information, weather information, temperature information, wind speed information, humidity information, and precipitation information. The data obtained from travel information from Ngurah Rai airport to Kuta Beach using GPS and weather information using weather application in real-time. We divide our data into two groups: a historical group consisting of 177 data and a testing group consisting of 51 data. In the testing stage, kNN-Regression will find the historical data closest to the testing data, so that the estimation value of the travel time of some testing data is not much different from the value of the nearest historical data. As a result, our proposed model gives the Mean Absolute Error (MAE) of 2.196078, Root Mean Square Error (RMSE) of 2.977036294 and accuracy rate 88.1819%.

Travel Time Estimation Using Support Vector Regression on Model with 8 Features

“…Then the temperature information, i.e., if the temperature in your area is extreme, can make people stay at home, which causes the volume of the vehicle to be less crowded and speed up travel time. The last factor is humidity, if the humidity in your area is high then it can cause the environment to feel hot so it can make people not want to leave the house which will cause the vehicle volume to be less crowded so that travel time becomes faster [2]. After the 8 features are determined, then we will predict the travel time with cases in the Bali area.…”

“…The data used is vehicle data which consists of 4 features, i.e., personal, traffic, temporal and spatial information [9]. Furthermore, [2] uses a regression model consisting of 8 features/variables, i.e., zone, time, day, weather, temperature, wind speed, humidity and rainfall information using the KNN regression method to predict travel time. From the results of his research, it was found that the prediction Accuracy rate of 88.19% [2].…”

“…Time is a factor that can affect travel time because there is a pattern of rush hours, i.e., in the morning, many people will move to work, which can cause the volume of vehicles on the road to be more crowded and affect travel time [2]. Furthermore, weather can also affect travel time, i.e., when it rains, a lot of vehicles will slow down their speed vehicle so that it can cause the travel time to be longer.…”

“…Routes information can also affect travel times, such as during peak hours on certain routes there will be congestion, so alternative routes are needed, so that travel times are faster. Later wind speed can affect the travel time, i.e., if the wind speed is fast, it will cause the vehicle's speed to slow so that the travel time becomes longer [2].…”

Klasifikasi Tingkat Kematangan Pisang Berdasarkan Ekstraksi Fitur Tekstur dan Algoritme KNN

“…Setelah dilakukan ekstraksi fitur tekstur berdasarkan histogram, tahapan selanjutnya adalah melakukan klasifikasi tingkat kematangan buah pisang dengan menggunakan algoritme KNN. Algoritme KNN merupakan algoritme klasifikasi yang tidak memerlukan pengetahuan tentang distribusi data [12]. Jadi, tanpa melihat distribusi data, metode KNN dapat digunakan untuk melakukan klasifikasi.…”