Prediction of New York taxi tip behavior based on machine learning classification and regression methods

DOI

10.2991/978-94-6463-344-3_75How to use a DOI?

Keywords

machine learning; taxi prediction; classification; regression

Abstract

In the context of machine learning, this study employs a learning method to process big data and predict and analyze taxi tip behavior. Basic variables such as trip time, trip distance, and number of passengers are added to the dataset, as well as special variables related to geographic location. Using these variables, a two-stage model is created in which a random forest classification model with an accuracy rate of 98.3% in the first stage and a Lasso regression model with an MSE value of 0.007294 in the second stage are used to predict taxi tip behavior, resulting in better fitting than a single model prediction.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Hejingyu Huang
PY  - 2024
DA  - 2024/01/10
TI  - Prediction of New York taxi tip behavior based on machine learning classification and regression methods
BT  - Proceedings of the 2023 2nd International Conference on Public Service, Economic Management and Sustainable Development (PESD 2023)
PB  - Atlantis Press
SP  - 686
EP  - 698
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-344-3_75
DO  - 10.2991/978-94-6463-344-3_75
ID  - Huang2024
ER  -