Performance Analysis of Bus Arrival Time Prediction Using Machine Learning Based Ensemble Technique

Abstract

Bus transport is an important means of communication in a modern world of smart cities. These smart cities require intelligent transportation systems. Such systems need effective techniques to be developed to meet customer requirements. Machine learning is one of those techniques for developing mathematical models to predict based on given data. Such techniques can be used to detect the arrival time of a bus at a given bus stop based on the historical data of the bus. In this paper Random Forest, Lasso and Ridge regression are used to train and analyze the performance over standard dataset in comparison with ensemble of Random Forest, Lasso and Ridge regression. Performance of ensemble techniques is better as compared used to Lasso, Ridge Regression, XGBoosting, and Gradiant Boosting.