Data Case Study: How the effect of city biking is improved with a shorter borrow period

One of the many real ways that UIP is using data analysis to make bike sharing better.

When Oslo City Bike’s new scheme was launched in April 2016, users were given an entirely new experience with bike sharing in Oslo: lighter, more comfortable bikes, a “one click” app for locating and unlocking them, and a streamlined approach to customer service. One thing that remained from the old system, however, was the length of the borrow period. Users could borrow a bike and keep it for up to three hours. Multiple late deliveries could result in penalties.

The new Oslo City Bike was extremely well-utilized in its first season (ridership increased 110% compared with the previous year), but it was also a period of carefully measuring KPIs and patterns of usage to fine-tune operations. As a tech-driven mobility company, we at UIP built our bike share model to be easily adaptable to changes in technology and user behavior, and for using ongoing data analysis to improve and fully optimize the effectiveness of the system.

During the first season, users would often borrow a bike and ride it to a park or café, where the bike would then be left in the grass or standing on the sidewalk for hours at a time, inaccessible to the rest of the city. When the bikes are borrowed but not used, this creates less availability, equating to more users encountering empty stations, and, overall, a less effective mobility solution.

The Oslo system therefore suggests that an optimal system design requires shorter rental periods. At the launch of the 2017 season in Oslo, we reduced subscribers’ included lending period to 45 minutes, with an option for adding “extended rental” time for a small fee. This decision was based on careful analyses of trip and user data, and the objective was to increase the number of trips, thereby optimizing the overall functioning of the system. Our data shows that in both 2016 and 2017 in Oslo, the most common trip lasted for only about 5 minutes, and the median trip length during both seasons was about 9 minutes.

The reduction in time for the rental period showed great results. The average trip length was reduced by 42%, while the median trip was only reduced by 5%. This implies that the vast majority of trips were unaffected, while the number of extra-long trips was reduced. This prevents bikes from sitting borrowed but unused for long periods of time. Now, when users are done with their trip, they’re encouraged to return it to a station sooner, making it accessible to the rest of the city.

The figure below shows the distribution of trips in 2017, by trip length in minutes. In the figure are all trips are capped at 47 minutes.

Distribution of trips in 2017, by trip length in minutes.

By selecting the “extended rental” option, subscribed users in Oslo can now borrow a bike for up to 6 hours and 45 minutes; the additional time after the free 45 minutes can be added for a small fee of €0.50 for each additional 15 minutes.

The “extended rental” option adds 15 minutes to the trip for only €0.50.

The number of trips longer than two hours have been reduced by more than 90%, suggesting that users are generally not interested in long rental periods. We therefore consider the reduction of the free rental period from 3 hours down to 45 minutes to be a great success; the system produces more trips, the general availability of bikes has increased, and very few users are negatively affected.

Our stations do fill up, especially during rush hour periods, so we’ve also added a feature where city bikers can add more time to their trip for free if they arrive at a full station. This increases system availability by encouraging users to return a bike when they’re finished riding, while helping to prevent any stress or frustration that might be associated with the shorter borrow period.

The app view for postponing your return upon arriving at a full station.

The city bikes in Oslo are meant to be seen as shared infrastructure, not traditional rental bikes. And while shared city bikes were once thought of as novelty street furniture, we see them as a vital and dynamic element of urban mobility. Using a city bike is often the quickest and most direct method of getting from point A to B, or when connecting to public transit. The system is most effective when used for these short trips. UIP’s goal with Oslo City Bike and our other platforms is to combine the individual experience with the collective effect; this means building better bike sharing for each user, but also for the city as a whole. This example shows how implementing new solutions based on data analysis can produce real and tangible effects, both in the optimization of bike sharing, and on the overall effectiveness of urban mobility.

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