Uber and Lyft Cab Prices Data Analysis and Visualization
Both Uber and Lyft are ride-hailing services that allow users to hire vehicles with drivers through websites or mobile apps. Uber is a global company available in 69 countries and around 900 cities worldwide. Lyft, on the other hand, operates in about 644 cities in the US and 12 cities in Canada only. Yet, in the US, it’s the second-largest ridesharing company with a 31% market share.
From booking the cab to paying the bill, both services have almost similar core features. But there are some unique cases where the two ride-hailing services come neck to neck. One such is pricing, especially dynamic pricing called “surge” in Uber and “Prime Time” in Lyft.
We have an interesting dataset with data from Boston (US), which we will analyze to understand the factors affecting the dynamic pricing and the difference between Uber and Lyft’s special prices.
The datasets used in this article have been imported from:
Temp: Temperature in F
Distance: The Distance Between Source and Destination
Time_stamp: Epoch Time When Data was Queried
Pressure: Pressure in MB
Destination: Destination of the Ride
Rain: Rain in Inches for the Last Hr.
Source: The Starting Point of the Ride
Time_stamp: Epoch Time When Row Data was Collected
Price: Price Estimate for the Ride in USD
Humidity: Humidity in %
Surge_multiplier: The Multiplier by Which Price was Increased, Default 1
Wind: Wind Speed in MPH
Id: Unique Identifier
Product_id: Uber/Lyft Identifier for Cab-type
Name: Visible Type of the Cab Eg: Uber Pool, Uberxl
*The above information is copied from the Kaggle dataset.
The data has been analyzed using Python and visualized with Power BI.
Factors Affecting Cab Prices
Distance and Price Correlation
The first graph shows Lyft vs Price Correlation whereas the second graph shows Uber Distance Vs. Price Correlation.
*In the graph above, the color yellow signifies a strong positive correlation, and the color purple indicates a low correlation
For Lyft: surge multiplier and distance are weakly correlated.
For Lyft: surge multiplier and price are more correlated.
For Uber: price and distance are weakly correlated
Surge Correlation with Days (only for Lyft)
*We do not have surge values for Uber
A surge of 1.25x the regular price is most common.
On the weekdays, Tuesday is most likely to experience a surge during rush hours, while Wednesday experiences the least.
A surge in the price of 3.0x happens rarely.
Surge Correlation with the Time of Day (only for Lyft)
Lyft’s Prime Timing happens the most during nighttime.
Morning rush hours also contribute to the surge.
A surge is less likely to happen during the afternoon and evening.
Surge Correlation with Source and Destination
The routes from Back Bay to Boston University experience a higher surge in cab prices.
Haymarket Square to Beacon Hill experiences the least surge in prices.
Uber vs. Lyft
Distance vs. Price
Uber is riders’ first choice irrespective of distance.
Number of Shared Rides with Time of Day
Number of Shared Rides with Distance and Price
People most likely have shared rides during the nighttime.
As the distance increases, the price also increases.
The number of shared rides is not growing with the increase in distance.
Apparently, people prefer to have shared rides for smaller distances. People like taking shared rides over the courses of short passages, roughly lying between 1 to 3 kilometers.
Rides correlation with Weather
Who gets more ride when it rains
The number of rides increases with an increase in temperature.
People tend to avoid rides when it rains.
Lyft gets slightly more rides than Uber, but this cannot be generalized as we have very little data (only 3,866).
If you were a Business Analyst working for either Uber or Lyft, you could draw the following conclusions:
Uber is more economical; however, Lyft also provides fair competition.
People prefer to have shared rides during the nighttime.
People avoid taking rides when it rains.
When traveling longer distances, the price does not increase linearly. However, based on the time and demand, a surge can affect the cost.
Uber can be the first choice for long distances.
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