City Bike Analytics

City Bike Analytics - Tableau

New York city bike program is an affordable way to get around the city. This is a program that grows in popularity and it's important to analyze these data to see the trends and find answers to the questions. The data set includes the New York city bike data for the summer months (July, August and September) of 2019 consisting over 100,000 data points.

I have focused the study on four key questions:
  • What are the rush hours?
  • What bikes need servicing/repairs?
  • How does the bike usage depend on gender?
  • Why some stations are more popular?


Rush Hours
Bikes are not used much during midnight-5am, based on the start times. Most trips start from 7-9am or 5-7pm. Least number of trips start from 3-4am. The busiest hours are 7-9am and 5-8pm. This is not a surprising result at all. However, those who use bikes from 3-5am, use them for longer average time periods compared to other users. Their average trip time is 4521 seconds. Most of the rest of the day, the average trip time stays below 1000 seconds. But there are very few trips during 3-5am. The total daily trip duration is highest between 4pm-8pm. This indicates, that this is the time most people use bikes. The next highest trip duration is between 7am-9am. Based on the ending time, trips end at the midnight have the longest average trip time. Overall most trips are short trips that are less than 2 hours. Also there are some trips that are very long. (more than 8 hours)

Most used bikes
The top 5 most used bikes (based on the total trip time) are 29459, 26173, 29674, 29299 and 29551. These bikes are the ones that most likely need servicing or repairs. On the other hand, there is one bike, 25421 stands out as the one with the highest average trip duration. The average trip durations for this bike is about 9 hours. By digging deeper, it revealed that most of the trips started between 4pm-5pm.

Gender
There are three gender groups in the data set. Males (indicated by ‘1’), Females (ind1cated by ‘2’) and Unknowns (indicated by ‘0’). They all are very similar in terms of the trip start time. Majority of bike users are males. A more effective gender outreach program is required to attract more females.

Popular Bike Stations By analyzing the top 10 starting and ending stations, it revealed that top 5 stations for starting and ending are the same. This is an interesting result and needed more digging to find out why. After looking at several parameters without much success, I looked at the map for any clues. The map shows four of the top five stations located near subway stations. The other bike station is located near the Hamilton park. Hamilton park seems to be a popular place among the bikers. This shows where the bike stations should be located.


Used: Python, Pandas, Tableau, Dashboards
Github: https://github.com/lumindak/City_bike-Analytics