New York City taxi trip duration prediction using MLP and XGBoost

• Published in: International journal of system assurance engineering and management Vol. 13; no. Suppl 1; pp. 16 - 27
• Main Authors: Poongodi, M, Malviya, Mohit, Kumar, Chahat, Hamdi, Mounir, Vijayakumar, V, Nebhen, Jamel, Alyamani, Hasan
• Format: Journal Article
• Language: English
• Published: New Delhi Springer India 01.03.2022; Springer Nature B.V
• Subjects: Airports; Algorithms; Engineering; Engineering Economics; Logistics; Marketing; Multilayer perceptrons; Multilayers; Organization; Original; Original Article; Pricing; Quality Control; Real time; Real variables; Reliability; Route selection; Safety and Risk; Taxicabs; Traffic congestion; Traffic planning; United States > US; New York City New York; Mini Batch; Xgboost; Clusters; Prediction algorithms; Multilayer perceptron; Entropy; Rectified neural network; Manhattan distance; Average rmse
• ISSN: 0975-6809; 0976-4348
• DOI: 10.1007/s13198-021-01130-x

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.