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NYC Taxi Passenger Anomaly Data
Data Science and Analytics
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About
This dataset is designed for time-series analysis and anomaly detection, focusing on the number of taxi passengers in New York City. It highlights five distinct anomalies associated with significant events: the NYC marathon, Thanksgiving, Christmas, New Year's Day, and a snow storm. The raw data, originally from the NYC Taxi and Limousine Commission, has been aggregated into 30-minute buckets to provide a clear view of passenger traffic patterns.
Columns
- timestamp: Represents the specific time when the passenger count was calculated. The data spans from 1st July 2014 to 1st February 2015, with 10.3k valid entries.
- value: Indicates the total number of taxi passengers recorded during each 30-minute interval. Values range from 8 to 39.2k, with a mean of 15.1k and a standard deviation of 6.94k. There are 10.3k valid entries for this column.
Distribution
The dataset is provided in a CSV file named
dataset.csv, with a size of 316.58 kB. It contains 3 columns, including the timestamp and value described above, and aggregates taxi passenger counts into 30-minute periods. The dataset includes 10.3k records.Usage
This dataset is ideal for various analytical purposes, including time-series analysis, identifying unusual patterns in passenger numbers, and developing anomaly detection models. It can also be used for broader studies related to urban transportation and the impact of holidays or extreme weather on public transit.
Coverage
The dataset covers taxi passenger activity specifically within New York City. The time range extends from 1st July 2014 to 1st February 2015. It explicitly captures unusual spikes or drops in passenger numbers during specific events such as the NYC marathon, Thanksgiving, Christmas, New Year's Day, and a snow storm, offering insights into their impact on transportation.
License
CC0: Public Domain
Who Can Use It
This dataset is particularly useful for data scientists, analysts, urban planners, researchers in transportation and logistics, and anyone interested in studying urban mobility patterns or developing predictive models for passenger demand and anomaly detection.
Dataset Name Suggestions
- NYC Taxi Passenger Anomaly Data
- New York City Taxi Traffic Anomalies
- NYC Taxi Ridership Time Series with Events
- Urban Taxi Passenger Anomaly Detection Dataset
Attributes
Original Data Source: NYC Taxi Passenger Anomaly Data
QUALITY