NYC Yellow Taxi Data Usage and Profitability

Posted on Oct 18, 2019
The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

NYC Yellow Taxi Data Usage and Profitability

Purpose of the Data Study

The study’s main objective is to understand some of the characteristics of the yellow cab industry and how some data variables such as distance, speed or traffic affect profitability.

 

Overview

During 2019, the NYC Yellow Taxi industry will have revenue of about $1.6 billion ($4.5 million per day) and will transport more than 140 million passengers (~400 k/day). Profits are estimated to be around $300 million (~$900k/day).The total amount of distance traveled (with and without passengers) in a day is ~1.2 million miles. To put distance in perspective, it would take a single person about 20 years to travel it.

NYC Yellow Taxi Data Usage and Profitability

Data on Characteristics of Yellow Taxi Usage

We can characterize the usage of NYC taxi as a small distance and mid fare service. About 80% of the trips have a distance of 5 miles or less and a fare of $18 or less.

For example, trips with longer distances such as Manhattan to and from JFK Airport only represent a small percentage of the total.

NYC Yellow Taxi Data Usage and Profitability
NYC Yellow Taxi Data Usage and Profitability

Fig. 1  Concentration of trips by Distance and Fare (random sample)

Purpose of Trips

Even though we don’t have much information regarding the purpose of the trips, we can make some assumptions by looking at the most traveled routes and cross-check it with the hour. In the following heatmap we can identify how the demand is concentrated in particular hours of the day.

Fig. 2 Heatmap of trips by day and hour

Takeaway 1: People use taxis when tired, especially after a long day at work

From the previous graph we can see that most of the trips are generated between 4 and 8pm. Even though there is slighter more demand from Thursday to Saturday, we see that in general the demand is still high at that particular time of the day any day of the week.

Now, by looking at the routes with more trips, we can tell that much of the traffic is generated within the Upper East Side. 

Typical Fare Composition vs Operating Costs

On average, a NYC trip costs the users $17.30 of which $14 goes directly to pay for the direct costs. At the end, the taxi’s profit per trip is under $4.

Fig. 3 Average Revenue and Profit per trip

As the two main factors that affect cost depend on time or distance, the costs can be classified according to how they are incurred in the following way:

  • Time related: costs that fluctuate with distance
    • Variable: labor (driver’s salary)
  • Distance related: Costs that depend directly by miles traveled
    • Variable: gas and maintenance (tires, oil, breaks and other preventive maintenance)
    • Fixed and Semifixed: car depreciation, replacement car cost and insurance.

Sensibility Analysis: if we increase each of the main costs independently (keeping the rest of the variables constant) by 10% we can identify how sensible is each cost in the total profit.

Cost Variable Increase Profit/trip Cost Increase Profit  
Insurance 10%  $              3.73   $              0.02  27% From 3,500 to $$3,850/yr
Gas Price 10%  $              3.67   $              0.09  26% From 3.5 to $3.85/gallon
Car Cost 10%  $              3.64   $              0.12  26% From 20k to $27.5k per car
Labor 10%  $              2.98   $              0.78  21% From 20 to $22/hr

From the previous table we can see that labor cost is one of the main factors that influences profit of the taxi business. To show this with more detail, lets look at the following example:

            Origin: Upper East Side South                                    Destination: JFK Airport

            Distance: 18 miles                                                      Fare: $52

  Time of Day Minutes Cost Profit Profit %
Average Time All Hours / Add Days 49  $ 42   $ 10  19%
Fastest Thursday 12pm-4am 26  $ 29   $ 23 44%
Slowest Friday between pm4-8pm 70  $ 54   ($ 2) -3%

Takeaway 2: Speed is the most important factor in determining profit because costs associated with time (such as labor) have the biggest impact on cost

As seen in the previous example, just the decreased speed that is caused by heavier traffic (early morning vs peak hour) can render a profitable route unproductive. It is because basically the drivers lose so much time in moving so slowly that they miss out on picking up more fares (generating more revenue).

Conclusions

So with all this information, can we determine which is the best shift or the best time to work as a taxi driver? It is not that easy. Since drivers are not allowed to reject customers based on their destination, it is very difficult for the driver to control where they have to go.

It is also not possible to optimize the results by driving only when traffic is light. If taxis want to operate only when the average speed is greater (maybe from 10pm to 6am), they could lose out on revenue because the demand for trips at that time is relatively low.

If you see the information from a regulation or traffic perspective, it can help NYC regulate the amount of taxis that can be at service at a particular time of the day or see if there are bus routes that need to be created. In the long run this perspective can help the city reduce carbon monoxide emissions.

Cost Model

The cost model is available online (https://mavaladezt.shinyapps.io/taxis/) and can be modified in order to make simulations and understand how different variables might affect the profitability of the taxi business.

About the Data

Data of Jan-Jun 2019 trips was obtained from NYC Taxi and Limousine Commission (https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page). Only transactions paid with credit card or cash were used and it was assumed that 1 passenger was traveling when the driver was not able to introduce the number of passengers when the trip started.

About Author

Mario Valadez Trevino

Mario Valadez Trevino is a NYC Data Science Fellow with a B.S. in Industrial Engineering with minor in Systems Engineering and an MBA. Mario has relevant experience in demand forecasting, production and transportation planning, warehouse management systems and...
View all posts by Mario Valadez Trevino >

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