Abstract
This paper investigates the application of a set of machine learning algorithms to predict the time that will be spent inside a vehicle between any two locations. The ride duration is estimated by analyzing data collected from historical traces of taxis, i.e., Jan 2015 New York City (NYC) Yellow Cab trip record data. Moreover, the taxi data is integrated with Uber dataset to estimate time accurately taking into account a set of semantic variables. Nevertheless, these semantic variables are selected through outlier detection and feature selection using Chi-Square scores. Features such as pick-up latitude and longitude, drop-off latitude and longitude, pick-up date, pick-up time, etc., are considered for prediction purposes in NYC dataset. Mainly, the forecasting effectiveness is compared for three machine learning models, namely, Decision Tree Regression (DTR), Random Forest Regression (RFR), and K-Nearest Neighbor Regression (KNNR). It is found that RFR and KNNR are the favorites for this travel time prediction. Also, our result supersedes the best performance of Kaggle competition.
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Roy, B., Rout, D. (2022). Predicting Taxi Travel Time Using Machine Learning Techniques Considering Weekend and Holidays. In: Abraham, A., et al. Proceedings of the 13th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2021). SoCPaR 2021. Lecture Notes in Networks and Systems, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-030-96302-6_24
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