Source (Cover Image): Seoul Solution, 2014
At first glance, the colourful routes of Seoul’s Owl Bus service resemble any ordinary transportation network. However, upon diving beneath its surface, I noticed the hidden invisible technological networks that contributed to its creation, which I will explain below.
Brief background of Seoul’s Owl Bus services
Problem: They are night buses running 9 routes, which came into effect after feedback from Seoulites about the heavy financial burdens and inconvenience of taxi services as the only means of transportation after midnight.
How was the solution created: As such, the Seoul Metropolitan Government (SMG) conducted big data analysis of 3 billion late-night mobile phone call and text collected by Korea Telecommunication (KT) to determine the heavily trafficked routes (Seoul Solution, 2014).
Figure 1. Example of N26 Seoul Owl Bus service route
Source: Korea Tourism Organisation
Invisible communication network – feedback from citizens
The identification of the transportation problem was first derived from the invisible communication network that connected Seoulites with the SMG. Since 2012, the SMG has managed a multitude of channels such as an official blog, social media platforms, to better listen to citizens and develop policies (Cities Alliance, 2014). It was a tweet to the mayor that resulted in the Seoul Owl Bus service launched 8 months later. In this case, the SMG tapped on the commonly-used social media network, providing convenience and incentive (less hassle as compared to traditionally tedious processes of filling up forms, etc. to submit feedbacks) for citizens to participate in identifying problems and solutions for the city.
“By the time I get off work, there are no bus lines running. I do not have a car. I wish there would be a bus service operating in the late hours as well.” (Translated from Korean)
– @gu**** on Twitter
Source: Lee, 2017 and Seoul Urban Solutions Agency
Invisible data network – network of phone calls and text messages of Seoulites
The SMG analysed data of phone calls and text messages from KT to ‘construct a radical-shape network linking outer districts of the city with the hub areas such as Jongno and Gwanghwamun’ (Cities Alliance, 2014). Then, by colour-coding regions based on call and text volumes, the mapping of the floating population after midnight was used to determine the most heavily trafficked routes in Seoul (Guay, 2017). This unintuitive and indirect way of establishing effective night bus routes is fundamental to the success of this initiative. This is because the straight-forward method of retrieving passenger travel data directly from late-night taxi drivers may be met with reluctance or resistance due to the fear of reduced earnings.
Figure 2. Simplified process of how the data of phone calls and text was utilised to design Seoul’s Owl Bus routes
Source: Seoul Solution, 2014
In this post, I explicitly detailed the invisible networks that contributed to the (visible) transportation network of the Seoul Owl buses. Seoul’s Owl Bus acts as a poster child for how urban flow and ecology is remade to bring about social benefit and minimise the unjust landscape in Seoul (Heynen, 2013). However, my main reason for examining these invisible networks is to shine a light on the unobvious processes that could go unnoticed despite having the potential to provide immense help in devising urban solutions. I thought that this is especially important, considering the constant and rapid technological advancement that we see today, where the ability to craft solutions may very much depend on the ability of cities (be it individuals, the private or public sector) in spotting and effectively utilising these invisible networks to improve the way of life.
- Seoul Solution (2014) Night Bus (called Owl Bus): Route Design Using Big Data. Seoul Solution, [online]. Available at: https://www.seoulsolution.kr/en/content/night-bus-called-owl-bus-route-design-using-big-data [Accessed 1 Dec. 2017].
- Cities Alliance (2014) Solutions from Seoul: “Owl Bus” Based on Big-Data Technology. Cities Alliance, [online]. Available at: http://www.citiesalliance.org/node/5063 [Accessed 1 Dec. 2017].
- Moon, S.K. Utilizing Big Data to Solve Urban Issues: The Case of Seoul. Seoul Urban Solutions Agency, [online]. Available at: https://www.thegpsc.org/sites/gpsc/files/partnerdocs/seoul_utilizing_big_data_to_solve_urban_issues_-_the_case_of_seoul.pdf [Accessed 1 Dec. 2017]
- Guay, J. (2017) Seoul uses citizens’ late night calls to plan new night bus routes. Apolitical, [online]. Available at: https://apolitical.co/solution_article/seoul-uses-citizens-late-night-calls-plan-new-night-bus-routes/ [Accessed 1 Dec. 2017]
- Heynen, N. (2013) Urban political ecology I. Progress in Human Geography, 38, 4, 598–604.