Citi bike Ridership Pattern in December 2020
Same bikes, same city, same month, just last year- what we can learn from it?
With increasing popularity, bike-sharing systems are getting deployed in more and more cities around the world, offering alternative transport methods in cities with busy traffic and increasing public health. Since 2013, Citi Bike operates in New York City and publishes usage data of the system online, offering insights into the system.
In the last couple of years, bike-sharing has taken a major role in the transportation network of big cities as an alternative way of getting around the city. It consists of a network of docking stations from which bikes can be borrowed and returned, where it does not matter from which station a bike is borrowed or where it is returned.
Considering the positive effects on the environment, public health and overall traffic, bike-sharing offers a way of traveling around the city which relieves pressure from the congestion and results in less impact on the wear of roads. This makes the incorporation of a bike-sharing system attractive for many big cities facing those issues, including the city of New York.
To that end, this research is aimed to answer the question of: How does the Citibike ridership pattern look like in December 2020?
We will begin to discuss about the overall trends of Citibike ridership in December 2020. This particular time interests me the most since it is the latest collective data recorded during the holiday season in New York City as well as the first of one being situated in the Covid-19 pandemic. To see the case study of comparison between before and after the pandemic, check out my last post of “Data and The City: A Look Into The Citibike Ridership in NYC.”
To begin with, the infographic below depicts basic information about the Citibike Ridership in December 2020:
The number of routes represents how many connections between one single start station to another end station. This figure was identified by identifying trips unique to their start points (stations) and their ends. For example, let’s just say that in December there were 1 bike trip made from “Central Park West & W 72 St” to “E 141 St & Jackson Ave” and 2 trips from “Central Park West & W 72 St” to “W 82 St & Central Park West.” Therefore, we can conclude that there were 3 (three) trips and 2 (two) routes made in that month. While number of dock stations were counted as all station points (start and end) in the city that were used for bike docks.
From the figure above, we can see that the overall number of trips made in a month reached up to 1,088,929 trips, which means there were approximately 35,126 bike trips per day made by users. This number is relatively huge given the time of pandemic was still going on at that time, and compared to last year where the number for Citibike ridership was around 955,211 trips in December 2019, which means that it was 133,718 increased than last year.
As we can see, the Citi bike usage trends throughout the week were somewhat as expected. Disruption of a pandemic may have affected this trend where bike usage in the weekend does not necessarily mean that it would be higher than on weekdays. While biking lifestyle getting more and more popular as an alternative and reliable public transportation in the pandemic and post-pandemic life, the average number of trips in working days were showing higher rather than those in the weekend during two weeks at the end of December 2020.
The Citibike Network in December 2020
In this part of the discussion, we will be taking a look into the pattern of Citibike network in December 2020. We will be using a measure called degree of centrality to explain what kind of tendency that this ridership possibly could make in order to better understand the relationship of Citibike stations usage and its placement in the city in general.
For those who may not familiar with the term “degree of centrality” of a node, it is basically a degree or the number of edges it has (Source: Golbeck, 2015). In this case, our edges are the Citibike stations. The higher the degree, the more central the node is. In other words, the higher degree that a point of the station could have, the more influential that station could be to another station surrounding it. This can be an effective measure, since many nodes with high degrees also have high centrality by other measures.
Based on our degree centrality, the above-mentioned stations are the edges that have higher value, meaning that those stations, as a node, are more central than the other. For the degree of centrality, we defined measures by their in-degree and out-degree centrality. Accordingly, in-degree is a count of the number of ties directed to the node, while outdegree is the number of ties that the node directs to others (Source: Bisht, 2019). When ties are associated, we can interpret that these stations are generally more popular to be used among the city’s bike users.
As we can see from the Eigen centrality as well as betweenness centrality, the top stations resulted remains similar. It is noticeable that 1 Ave & E 68th St as one of the most influential stations in terms of usage popularity compared to other stations available in the city. If we take closer to the top list above, most of them were located in Manhattan.
Based on this information, we can visualize this network based on degree of centrality.
Looking at New York City and especially Manhattan, which doubles its population to 3.9 Millions during workdays due to commuters, the city’s Department of City Planning in 2008 determined several points of action as it was investigating into transport alternatives for the city. In order to decrease traffic-related issues, their plan involved the expansion of bike lanes throughout the city and the installation of bike racks. The findings determined through this research could be used to communicate the technical locational setting of bike stations as well as the future development of the bike-sharing system in New York City. Furthermore, in order to further elaborate this key information, it is suggested for research related to a bicycle-sharing system based on user destination choice preferences.
Degree Centrality (Centrality Measure) — GeeksforGeeks. (n.d.). Retrieved December 20, 2021, from https://www.geeksforgeeks.org/degree-centrality-centrality-measure/
List of named colors — Matplotlib 3.5.1 documentation. (n.d.). Retrieved December 20, 2021, from https://matplotlib.org/stable/gallery/color/named_colors.html
Valera, M., Guo, Z., Kelly, P., Matz, S., Cantu, V. A., Percus, A. G., Hyman, J. D., Srinivasan, G., & Viswanathan, H. S. (2018). Machine learning for graph-based representations of three-dimensional discrete fracture networks. Computational Geosciences, 22(3), 695–710. https://doi.org/10.1007/s10596-018-9720-1
This article is published in partial fulfillment of assignments in the Introduction to Urban Informatics and Data I course, term Fall 2021, taught by Professor Boyeong Hong — Urban Planning program, Graduate School of Architecture, Planning and Preservation at Columbia University.