New York City taxi trip duration prediction using MLP and XGBoost

M Poongodi (), Mohit Malviya (), Chahat Kumar (), Mounir Hamdi (), V Vijayakumar (), Jamel Nebhen () and Hasan Alyamani ()
Additional contact information
M Poongodi: Hamad Bin Khalifa University, Qatar Foundation
Mohit Malviya: Department of CTO 5G, Wipro Limited
Chahat Kumar: CFO Technology, Enterprise Risk Function Technology, Bank of America
Mounir Hamdi: Hamad Bin Khalifa University, Qatar Foundation
V Vijayakumar: University of South Wales
Jamel Nebhen: Prince Sattam bin Abdulaziz University
Hasan Alyamani: King Abdulaziz University

International Journal of System Assurance Engineering and Management, 2022, vol. 13, issue 1, No 2, 16-27

Abstract: Abstract New York City taxi rides form the core of the traffic in the city of New York. The many rides taken every day by New Yorkers in the busy city can give us a great idea of traffic times, road blockages, and so on. Predicting the duration of a taxi trip is very important since a user would always like to know precisely how much time it would require of him to travel from one place to another. Given the rising popularity of app-based taxi usage through common vendors like Ola and Uber, competitive pricing has to be offered to ensure users choose them. Prediction of duration and price of trips can help users to plan their trips properly, thus keeping potential margins for traffic congestions. It can also help drivers to determine the correct route which in-turn will take lesser time as accordingly. Moreover, the transparency about pricing and trip duration will help to attract users at times when popular taxi app-based vendor services apply surge fares. Thus in this research study, we used real-time data which customers would provide at the start of a ride, or while booking a ride to predict the duration and fare. This data includes pickup and drop-off point coordinates, the distance of the trip, start time, number of passengers, and a rate code belonging to the different classes of cabs available such that the rate applied is based on a regular or airport basis. Hereafter, we applied XGBoost and Multi-Layer Perceptron models to find out which one of them provides better accuracy and relationships between real-time variables. At last, a comparison of the two mentioned algorithms facilitates us to decide that XGBoost is more fitter and efficient than Multi-Layer Perceptron for taxi trip duration-based predictions.

Keywords: Prediction algorithms; Xgboost; Multilayer perceptron; Entropy; Manhattan distance; Clusters; Rectified neural network; Mini Batch; Average rmse (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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DOI: 10.1007/s13198-021-01130-x

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