This analysis shows ride-sharing data for different city types. It's goal is to show the differences between city types, and use that data to inform future business decisions at PyBer.
The following graph shows the total fares for the first 4 months of 2019 by city type. It clearly shows that urban areas produce the most fares, and as you get farther from the city, the total fares decrease.
Additionally, the average fare, and average fare per driver decreases as you move from rural towards urban areas. For the same 4 month period in 2019, the following chart depicts by city type:
- The total number of rides,
- The total number of drivers,
- The total fares,
- The average fare per ride,
- the average fare per driver
Based on this analysis of ride and driver data, I would make the following recommendations on how to address the disparity in rides, drivers, and fares in each city type.
- The average fare per ride and per driver in suburan areas is much closer together than in the other city types. Using this as a basis for the best ratio of drivers to rides, we should aim for having total drivers be at 80% of the total rides. Suburan areas only need an increase in drivers of about 2% to reach that number.
- Rides in urban areas will generally be shorter than those in suburban or rural areas. Meaning the average fare per ride will be lower. But, by reducing the number of available drivers by as much as 46% in urban areas, we can increase the average fare per driver. Currently there are far more drivers available, then there are rides requested.
- Conversely, rides in the rural areas tend to be farther, and have a higher fare. But, due to a low number of drivers the average fare per ride is far lower than the average fare per driver. In order to bring these closer together, and decrease the wait times for a ride in rural areas i would increase the number of available drivers by about 28%.