In this blog, we use data visualization and data analytics to provide some basic understandings of the origin-destination (O-D) flows between stations in a bike-sharing system. Part of this blog is adapted from the following research paper:

• X. Xie and Z. Wang, “Examining travel patterns and characteristics in a bikesharing network and implications for data-driven decision supports: Case study in the Washington DC area,” Journal of Transport Geography, vol. 71, pp. 84-102, 2018.
@Article{Xie2018Bike,
Title = {Examining travel patterns and characteristics in a bikesharing network and implications for data-driven decision supports: Case study in the {Washington DC} area},
Author = {Xiao-Feng Xie and Zun-Jing Wang},
Journal = {Journal of Transport Geography},
Volume = {71},
Pages = {84--102},
PDF={http://www.wiomax.com/team/xie/paper/JTRG18Pre.pdf},
Doi = {10.1016/j.jtrangeo.2018.07.010},
Year = {2018}
}

We use the system data of Capital Bikeshare between 2012-2016. The top 20 bike stations with the most pickups are listed in the following table, and the O-D flows between these bike stations are shown in Fig. 1 (The interactive diagram is built based on the code in D3). For the link each O-D pair, the thickness at each end encodes the normalized proportion of bikeshare pickups between two stations (the numbers will be shown if moving mouse on each link), and the color follows the station with more pickups. It is easy to find the flows between O-D pairs are often asymmetric. For example, the proportions are respectively 2.4% and 1.2% for 31258 → 31249 and 31249 → 31258. Some bikeshare stations (e.g., 31247 and 31248, both are located in the National Mall) have significant “loop” trips, i.e., the trips of pickup and drop-off at the same station.

 Id Name Id Name 31101 14th & V St NW 31238 14th & G St NW 31104 Adams Mill & Columbia Rd NW 31241 Thomas Circle 31110 20th St & Florida Ave NW 31247 Jefferson Dr & 14th St SW 31200 Massachusetts Ave & Dupont Circle NW 31248 Smithsonian-National Mall / Jefferson Dr & 12th St SW 31201 15th & P St NW 31249 Jefferson Memorial 31203 14th & Rhode Island Ave NW 31258 Lincoln Memorial 31214 17th & Corcoran St NW 31600 5th & K St NW 31222 New York Ave & 15th St NW 31613 Eastern Market Metro / Pennsylvania Ave & 7th St SE 31228 8th & H St NW 31623 Columbus Circle / Union Station 31229 New Hampshire Ave & T St NW 31624 North Capitol St & F St NW

Figure 1: Diagram of the O-D Flows between the Top 20 Bike Stations with the Highest Pickups.

To better understand a bikesharing network, we analyze the top O-D pairs in the ranking of the highest O-D flows respectively by casual and member users (see Fig. 2). As shown in Fig. 2a, the top 50 O-D pairs form one main cluster by casual users in and around a famous recreational area — the National Mall and Memorial Parks, while form two clusters by member users respectively at the east and north neighborhoods of central business district. The top 6 origin stations in the ranking of the highest O-D flows are separated into three clusters (see the black dots in Fig. 2a). Among the 6 origin stations, one at the east cluster is the Union Station, three at the north cluster are near the triangle of Dupont Circle, Logan Circle and Thomas Circle Park, and two are in National Mall area. To analyze how a bikeshare network structure grows with the increase in the number of O-D links, we show the top 50, 500, and 5000 highest-ranking O-D pairs in O-D flows respectively by casual users (see Fig. 2b) and by member users (see Fig. 2c). Fig. 2c indicates that the trip network formed by member users covers the neighborhoods for commuters — the areas that feature high densities of workplaces or homes. In contrast, casual users take more long-distance trips (see Fig. 2b). From Figs. 2b and 2c, community structure can be clearly identified, where three clusters appear showing densely connected links of O-D pairs. The formation of such a community structure is quite common in real-world self-organized networks. The polycentricity at a metropolitan scale is an interesting feature of modern urban landscapes. Most users prefer to bike on a short-time trip, thus most regions far from core areas of existing bikeshare clusters are not reached by users, even though some of these regions have sufficiently high densities of workplace or residence for generating a large trip demand. To increase bikeshare ridership of these regions, it is important to foster the formation of new clusters with densely connected O-D links in the bikesharing network, which may be considered by system operators while new bikeshare stations need to be added or by city planners while building environment need to be improved.

Fig. 3 shows the empirical PDFs of use time for the top 20 O-D pairs in the ranking of the highest O-D flows by member users. Fig. 4 gives the comparison of average daily counts of the trips between weekdays (blue) and weekends (red) for the top 20 O-D pairs. Although the distributions of use time by member users for all the top 20 O-D pairs express a similar pattern on a utilitarian purpose — with a single sharp peak that can be fitted into the lognormal distribution (see Fig. 3), their daily trip counts show diverse patterns (see Fig. 4, especially the blue curves for weekdays on the same trip purpose). These patterns give us additional information on the land use near origin and destination stations (such as workplace or high-density residence) as well as more detailed trip purpose of an O-D pair (such as for commuting or for non-commuting).

Here we present a few decision rules based on associated knowledge. First, a dominant AM or PM peak of trip counts in weekday indicates that the trip purpose between the O-D pair is for commuting. Let R, W, and T respectively be a residence, workplace, and transportation hub. Basic commuting trip segments include H → W, H → T, T → T, and T → W during the AM period, and W → H, W → T, T → T, and T → H during the PM period. If one and only one station (either or ) is T, we can define the following {\em commuting-related} rule (): is H if is T and is W if is T given a dominant AM peak, whereas is W if is T and is H if is T given a dominant PM peak.

Let us take the O-D pairs in Fig. 4 as an example to illustrate the usage of the rule. The Stations 31121, 31200, 31613, and 31623 are known respectively adjacent to four transportation hubs (Metrorail and railway stations) of Woodley Park, Dupont Circle, Eastern Market, and Union Station. Based on the rule and the patterns shown in Fig. 4, we can identify that the eight stations, i.e. Stations 31201, 31229, 31611, 31612, 31614, 31619, 31622, and 31631, are located in primary residence neighborhoods. This is consistent with the result in CENSUS data analytics, where the LODES data by residence confirmed that seven and one of the eight stations are respectively located in the high-density residential census tracts with in and . Among the top 20 O-D pairs in the ranking of the highest O-D flows by member users, 17 are the O-D links between the four T stations and the eight H stations, i.e. all the top O-D pairs in Fig. 4 are T → H or H → T except for Figs. 4d, 4f and 4p. It indicates that bikesharing plays an important role in providing first- and last-mile connections between residence places and transportation hubs.

We can also define decision rules to identify the land use related to non-commuting trips. In the {nightlife-related} rule (), or is likely located near a nightlife area, if there is a nontrivial trip rate during 0–4 AM in weekend. For example, in Fig.  4d, the station 31104 is at Adams Morgan, which is a major night life area with many bars and restaurants.

By Xiao-Feng Xie, Ph.D.  —— The WIOMAX Blog

WIOMAX is a research and consulting team to explore real-time, smart and scalable solutions especially for applications to achieve Smart Mobility and Smart Cities by integrating cutting-edge DOAI [Data Analytics + Optimization + Artificial Intelligence (AI) + Internet of Things (IoT)] technologies, from basic research to industry transition and product development and support.

Using Data Visualization and Analytics for Understanding O-D Flows in Bike-Sharing System