Predicting NYC Taxi Demand Regions
Taxicabs are a popular mode of urban transportation because of its point-to-point service and 24-7 availability. However, the fast-growing online ride booking companies like Uber and Lyft have been adding immense pressure on the traditional taxicab industry. Often, the supply and demand of taxicabs in different regions of a city can be highly imbalanced. In some crowded regions, passengers find it difficult to get rides even after a long wait, while in some other regions, taxicabs are roaming without any passengers. A taxi demand prediction system can help solve this problem. This would allow taxicabs to compete with transportation network companies like Uber and help passengers to reduce wait times. The goal of this project is to predict high taxi demand regions in New York City. Using the taxi trip record dataset provided by NYC Taxi and Limousine commission we aim to develop a predictive model to predict the high taxi demand regions.
The New York City Taxi & Limousine Commission [1] provides a historical dataset covering over 1 billion individual taxi trips in the city from January 2009 through December 2017. Each trip record includes information such as pick-up and drop-off times, coordinates, trip duration, passenger count, payment, and so on.
[1] http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml
https://nycdatascience.com/blog/student-works/predict-new-york-city-taxi-demand/