Quickly, economically, usefully and with a breeze!!! You better not think. Let's take a closer look
This project was to create a bike sharing business proposal for the city of Des Moines, Iowa area. For example, we analyzing the Citibike NYC data for August, since it is the hottest month of the year among vacationers and locals. We should have touched on the following questions:
- How long bikes are checked out for all riders and genders.
- How many trips are taken by the hour for each day of the week, for all riders and genders.
- How many trips are taken by the hour for each day of the week, for all riders and genders.
- Dataset
- From CitiBike link download the .csv file (201908-citibike-tripdata.csv)
- Software
- Python
- Pandas
- Tableau Public
After analyzing the data and cleaning it up a bit, I created a visualization to show the trends and opportunities that would be available for both Des Moines, Iowa area and other major cities.
During august, the most profitable month of the year, the most popular hours in which people used the bikes were 5 and 6 pm during the afternoon and 8 am during the morning. This uncovers the trend that people usually use it to mobilize to and from their jobs as the peak hours are before and after work time.
Here we can see the beginning and end of the trip in New York. Even though this is a map for New York, we can expect similar behavior in other cities. There is always a more populated area (Manhattan in this case) where the most bikes will be used.
he service is very appealing to those who want a hassle free way to travel to work.
The largest demand for the bikeshare service corresponds with the "rush hour" work commute:
- 6AM-10AM Mon - Fri
- 4PM-8PM Mon-Fri
- Thursday is the busiest weekday
While weekday demand is largest, weekends are in demand as well:
- Saturday 8AM-8PM
- Sunday 9AM-8PM
Male subscribers make up the largest group of service users, followed by female subscribers and finally unknown customers.
The highest demand for subscribers is from Monday to Friday, with Thursday being the busiest day.
We also see here the total number of customers. And the graph of the curve helps to consider that the travel time basically does not exceed 40-50 minutes
Here again we see confirmation that the majority of customers are men with a market share of approximately 65%, then women with 25% and finally an unknown gender with 10%. They all share the same heatmap, which shows how they behave in the same way when using bikes at the same time (7-9am and 4-7pm for the most part).
As it can be seen, all genders usually use the bikes for 5 minutes approximately. After 5 minutes, the trip durations start to slope down.
Here we can consider the average duration of the trip in relation to the age of the clients. We can say with confidence that the younger our client is, the longer he can travel. Ideal border - 20-22 years. But also we can see a jump in approximately in 49-52 years old too!
Bike maintenance and repairs is a cost to the service that must be managed well. The circle chart highlights the utilization rate for each bike. The larger the blue spot, the sooner the unit should be taken into service. For further understanding - how long can this bike still last or it's time to change it!
We have now done a good job of analyzing data in New York on the distribution of bike sharing. But New York is not an ordinary city at all, especially Manhattan - it is very expensive and full of life! There may be many pitfalls here! The concept of a business in Des Moines, Iowa, or any other major city requires additional analysis:
- age of the client in the installation area
- purpose of using the bike
- condition on the roads (how things are with traffic jam during rush hours)
- number of visitors, their gender and age
This whole difficult path is worth going through to fully understand whether potential users will like the service. On which the success of the enterprise directly depends!!!
Denis Antonov
Contact: antonov.resu@gmail.com