Bike sharing services continue to gain popularity with people living in densely populated cities who looking for practical means of transportation. Riders can unlock bikes from hundreds of stations across their city and return them to other stations for a fee.
As analytics geeks, we wanted to run the data through SAP Analytics Cloud to see what insights we could uncover about bike sharing in New York City.
Bikes are collecting your data
Each bike station is an internet-enabled data collection device. They capture and collect all sorts of data such as:
- Trip times
- Start and stop date/time
- Start and stop station
- Bike ID
Looking at Citi Bike data for New York City, usage has increased each year.
Riders even ride in the winter.
Which stations get used the most?
If we look at the map, we can see that the most popular pick-up and drop-off locations are in mid-town and lower Manhattan. If we drill down into the map, we can see that the largest bubbles are around Grand Central Station.
Tracking popular routes
If we filter on the top station (Pershing Square), we can see that the typical routes are about 10-15 blocks away. This makes sense since the average duration is around 15 minutes.
When do people ride?
Wednesday is the most common day to ride, but people ride longer on the weekends.
Weekday afternoon and evenings are the most popular, but people ride longer in the morning/dawn and afternoons on weekdays.
While there are limited data on rider demographics, the data does capture age and gender. The majority of the riders are Millennial males. However, as with any data set, there’s always a question around data quality. Subscribers can enter fake information about themselves, such as their gender and age.
Based on the answers above, we can draw some conclusions about rider behavior. The typical citibike rider is:
- Young male who live and work around the city
- Short haul ride to/from the train station to work
- Prefer to ride home – where they have more time, maybe more energy, or want to burn more calories
What Does This All Mean?
The power of analytics is that it gives us more of the story behind the data, which can help us validate our thought process or gain new insights into our business. After analyzing the Citi Bike data, we can form a few key takeaways.
- How people use your product is just as important (if not more important) as who is using it. Citi Bike riders provide little information about themselves, but their usage speaks volumes. The fact that the numbers or riders have grown dramatically and that people using these bikes to ride to/from work tells us that they value the cost and convenience of these bikes.
- Citi Bikes is targeting the right customers. Their tag line “Faster than walking, cheaper than a taxi, and more fun than the subway” with pictures of young adults is the exact demographic that’s using their bikes. The only exception is that it seems to be a mostly young men that are riding.
- People need analytics, not data. While New York Citi Bikes do an excellent job providing timely and updated “raw data“, they provide little in terms of analytics and insight into this data. This type of analytics let’s us see the whole story in the data.