Analysis of Bike-Share Systems

Martin Ntonjira, P.E/CDT, Engineering Supervisor / Project Manager 3

Bike-sharing systems (bike-shares) are becoming increasingly popular in towns and cities around the world. These are viewed as cheap, efficient, and healthy means of navigating dense urban environments. Bike-shares attract a range of users from commuting professionals, to students grabbing lunch, residents running errands, and even leisure riders and tourists.

My research was based on a need to assess bike share use in the Kansas Metro area. Area of Interest was in downtown Kansas City, Missouri and the greater Jackson County, Missouri. The methodology and results of the analysis could be leveraged in the Countywide mobility plans for Wyandotte County and Kansas City, Kansas.

As a civil engineer working for the City’s Planning Department, this project provides a great example of using data analysis that could be used for transportation planning i.e. data driven planning. Data came from the RideKC bicycles On-board GPS. More than a million records with 10 unique variables from KCMO’s 2012–2017 bike share dataset was cleaned and analyzed using multi-variate statistical methods and big data techniques. RideKC Bike is a partnership of the Kansas City Area Transportation Authority, BikeWalkKC and Drop Mobility.

The objective of the study was twofold. First, the need to identify the relationship between bike users and their billing address. Secondly, finding the relationship between rental kiosk and return kiosk. The analysis examined the beginning and end point of bike-share trips to determine if there are significant relationships between certain stations. The bike-share raw data variables used in the analysis were; Membership Type, User ID, Trip ID, Billing Zip, Rental Kiosk, Rental date, Rental Time, Return Kiosk, Return Date, Return Time, Distance, Latitude & Longitude.

From the analysis, the results showed that Zip Code 64105 has the highest number of records. This includes the street networks of 12th, 14th and Main Street which indicated high bike-share activity. Not too far behind were the Zip Codes 64106,64108 and 64111 which are also in downtown KCMO.

Looking at the first objective, areas of high bike-share activity was as expected, and the analysis confirmed it.

The second objective indicated that the most common bike-share trips begin or end at Union Station, Crown Center, 12th and Wyandotte,40th and Broadway or Westport and Main. Trips are 1.15 times more likely to begin at Union Station or 40th and Broadway.

Additional analysis was carried out to find out how users pay for the service. Membership Types could fall under the following categories: 24 Hour (Kiosk), Walk-up pass, Annual, 7-day, or 30-day. From the Membership Type analysis, 24-Hour (Kiosk) had the highest sum of distance covered at almost 50,000 miles. Annual Membership had the lowest sum of distance covered.

Distance of trips taken analysis illustrated that most bike users take trips of less than 5 miles. We can infer that bike share is most popular for short-distance travel.

From the study done, the analysis identified popular routes and it appear there may be a connection between the start and end points of heavy bike-share use. This analysis could inform where future resources may need to be allocated to ensure the continuity and improvement of existing mobility and transportation needs.

Across the state line, the Unified government of Wyandotte and Kanas City, Kansas through the Department of Urban Planning and Design has an ongoing partnership with RideKC bike to expand the bike infrastructure in the Rosedale area. Towards this end an order has been placed for 50 electric assist bikes, racks and signage kiosks to be deployed in the area.

Future studies should try to determine more characteristics of bike-share users to help influence how a system could be expanded through Kansas City, Kansas. By surveying cyclists, both bike-share users and those that own their own bikes, the implementation of a bike-share system can be designed to best serve populations that are most likely to use it.

Citations

(www.elsevier.com/locate/jtrangeo)

(www.ridekcbike.com) http://www.marc2.org/tr_psp/projectdetails.aspx?PID=29 )

(Countywide Mobility plan for the UG)

Analysis and text by Martin Ntonjira P.E, CDT — Engineering Supervisor / Project Manager 3 Urban Planning and Design Dept.

Illustrations by Tom Meyer, AmeriCorps VISTA, unless otherwise noted

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