YouBike+: Reimagine The Alternatives Of Taipei’s City Bike
YouBike is Taipei’s well-established city bike system. Many cities in the world had introduced similar city bike systems but few had achieved subsidy-free and financial sustainability. However, the emerging station-less model such as Singapore’s OBike, China’s MoBike had revolutionized the imagination of public bike. It makes us wonder if YouBike model is out of date, and how might it face the new challenge?
What if we can take advantage of the space of the fixed bike stations? What if we can offer more value at those stations? And how can the stations better respond to the city’s needs?
“YouBike+” is the project based on YouBike open data. By analyzing around 300 station data accompanied with field research, we proposed some value-adding and alternatives for current station space. The goal was to showcase the potential of using data for urban innovation and provoke a constructive public conversation.
Visualizing the Overall Activeness
By end of this research (Dec 2016), there were 292 YouBike stations in Taipei, and the number was still growing. From YouBike open data, we parsed the number of borrowed and returned bike of 3 weekdays every 5 minutes. The visualization for hourly concentration of parked bikes was shown as the below:
The brighter area represents stations with more bikes. We can see from 8am, there’s an obvious reduce of bike amount, meaning most of the bikes started to release to the city. About 2pm is the second usage peak hour, and after 7pm, the usage of bikes starts to decline. City Hall Station is a long time hotspot, this is because the station itself was extra larger than other stations (180 bike racks).
Improving the User Experience for Each Station?
Limited to open data source, we could only get numbers of parked and in-use bikes without more information such as origin and destination, payment, user profile, etc. General data only leads to general facts. For better understand the bike usage, we needed to look into other factors and field study from user’s perspective.
Above is the overview of each YouBike station profile during weekday. For each diagram, the x axis represents 0:00 to 24:00; while y axis indicates the number of bikes parked at each station. Even though each station has it’s own unique pattern, they could be categorized in two types: multiple peaks and single peak.
We made a simple assumption: single-peak type is the result of singular user type, meaning the same user who ride to elsewhere in the morning and return in the evening; while the multiple-peak type results from more than one user type. This assumption is very likely over-simplified, but base on such assumption we could at least start to dig more. We chose two identical stations for further observation, which are S.Y.S Memorial Hall Station(multiple peaks) and Jiangquo Rd. Nongan St. Intersection Station(single peak).
Multiple-peak type
As the land-use plan shown above, S.Y.S Memorial Hall Station is next to MRT station and within a commercial residential mix-use area.
Around peak hour 8–9am, the main users are office workers who cycled from MRT to workplace. And when there’s no bike left, people would spontaneously wait aside for returning bikes, which made us wonder if there could be a designated waiting area where people could stand under the arcade, away from sunlight or rain, or even be seated while waiting?
Around 4–5pm, another main users appeared, the students. Differ from office workers, students usually come in groups. Therefore, you often see some took the bike (or sitting on their own ones) and wait for their friends thus temporary occupied the walkway space. What if we could mark a zone for temporary waiting area where it doesn’t block the way?
Single-peak type
As for the other single-peak type, Jiangquo Rd. Nongan St. Intersection Station, it is located in a residential area under an overpass, and far away from the MRT station. According to our observation, the users are mostly nearby residents who use the bike to commute between their house and MRT station.
During peak hour 8–9am, office workers were taking breakfast and rushing to pick the bike. Around 4–6pm it was almost empty and quiet. And around 9pm after dinner time, people began to return the bikes and went home. For such a huge space in residential district, could there be a breakfast vendor during morning peak hour so the workers could simply grab and go? Or could we turn the vacant space into playground for local kids, so that during 4–6pm when they just got out of school they could play until heading back for dinner?
Alternatives for the Empty Dock Space?
During our field observation, we saw something inspiring: a street vender occupied several bike docks for business. Perhaps they were aware about of the number of vacancy during that particular hours and took advantage of it for business. Quite brilliant, wasn’t it? This inspired us to rethink the station from another aspect. What if we could reuse the spare space of each station? How much temporary vacant space like this are there in the city could potentially be used for other function?
Firstly, we highlighted those period with vacancy that’s over two-third of total docks. And then we filtered the ones that last over 4 hours. These large portion and long period of vacancies, are highly reusable urban space that currently were of no use at all.
How much value could it bring to the city? We did a very simple calculation. If we could somehow make the dock space integrated and shareable with street parking, then, at a typical car park rate, it could bring the value equivalent to NTD 42K per day [1]. If those space are leasable to street vendors, it could generate value equivalent to NTD 420K per day as rental profit [2]. Furthermore, it could potentially be opened up for community use such as pop-up garden, temporary seating, etc. Now these vacancies, from social or civic point of view, are worthless.
Conclusion
The pattern analysis of bike usage for all stations was one major challenge, we had difficulty finding a proper model to sort the diverse pattern, therefore couldn’t inform practical design solution. However, these might be solved if involve data scientist and domain expert. For the later part of the study, the proposal for station vacancy was rather rough and simplified, yet the point being made that there are possibilities to remake the stations, whether that was to integrate the station with other public amenities or to share the vacant space with other functionality. Hopefully this study arouse more attention to our city space, and encourage public sectors to release more data for public use.
[1] Estimation based on NTD 60/hour per parking space, 14 sq. meters per space.
[2] Estimation based on rental rate NTD 1000 per sq. meter per month.
