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

Hejingyu Huang ()
Additional contact information
Hejingyu Huang: Beijing Forestry University, Geographic Information Science at

A chapter in Proceedings of the 2023 2nd International Conference on Public Service, Economic Management and Sustainable Development (PESD 2023), 2023, pp 686-698 from Springer

Abstract: 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.

Keywords: machine learning; taxi prediction; classification; regression (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-344-3_75

Ordering information: This item can be ordered from
http://www.springer.com/9789464633443

DOI: 10.2991/978-94-6463-344-3_75

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().