taxi_trip_pricing: Taxi Trip Pricing Dataset

In TFM: Sparse Online Principal Component for Truncated Factor Model

Taxi Trip Pricing Dataset

Description

A dataset containing various factors related to taxi trips and their corresponding prices.

Usage

taxi_trip_pricing

Format

A data frame with 'n' observations (rows, where 'n' is the number of taxi trips) and 11 variables:

Trip_Distance_km

Trip distance in kilometers. This variable measures how far the taxi has traveled.

Time_of_Day

A categorization of the time of the day (e.g., 1 might represent morning, 2 afternoon, etc.). It can potentially affect pricing due to demand patterns.

Day_of_Week

The day of the week (0 - 6, where 0 could represent Sunday). Weekend vs. weekday trips might have different pricing considerations.

Passenger_Count

Number of passengers in the taxi. It could influence the pricing structure in some taxi systems.

Traffic_Conditions

A measure of traffic conditions (e.g., 1 for light traffic, 4 for heavy traffic). Traffic can impact trip duration and thus price.

Weather

A classification of weather conditions (e.g., 1 for clear, 3 for rainy). Weather might have an impact on demand and thus pricing.

Base_Fare

The base fare amount for the taxi trip. This is a fixed component of the price.

Per_Km_Rate

The rate charged per kilometer traveled.

Per_Minute_Rate

The rate charged per minute of the trip (usually applicable when the taxi is idling or in slow - moving traffic).

Trip_Duration_Minutes

The duration of the trip in minutes.

Trip_Price

The final price of the taxi trip.

Examples

data(taxi_trip_pricing)
summary(taxi_trip_pricing)
if (requireNamespace("ggplot2", quietly = TRUE)) {
  ggplot2::ggplot(taxi_trip_pricing, ggplot2::aes(x = Trip_Distance_km, y = Trip_Price)) +
    ggplot2::geom_point() +
    ggplot2::labs(x = "Trip Distance (km)", y = "Trip Price")
}

TFM documentation built on June 9, 2025, 9:08 a.m.