Big Data Transforms How We Travel

On Monday, NYU Shanghai Center for Data Science hosted Yin Dafei, leading data scientist of the shared-bike provider Mobike, for an introduction of how big data optimizes business operation as well as city planning.

Introduced by Professor Keith Ross, Dean of Engineering and Computer Science, Yin kickstarted the Fall 2017 Seminar Series on Data Science and AI with an overview of the booming bike-sharing industry in China while explaining Mobike’s data team structure, its data pipeline and the team’s operation.

“Shared bikes have already transformed people’s mode of transportation. Mobike data shows the total biking distance of its users in China has surpassed 2.5 billion kilometers since April 2017,” he said. “It is said to have put the last piece on the public transit map.”

Speaking of how technology can help solve problems such as supply-demand prediction and bike relocation, Yin raised an example of Mobike using big data to detect hot areas in different time periods of a day, in order to optimize the distribution of bikes.

According to him, Mobike’s data science team has also employed machine learning and trained computers to identify images of illegal parking or lock violations, uploaded by users.

“Once the back-end confirms the violation shown in the image, the user who uploaded the picture is then awarded accordingly,” he added.

At the end of the lecture, Yin suggested the application of bike-sharing data to better city construction and planning, for example in reducing housing prices for properties around metro stations.

“As the bike-sharing economy continues to bloom, distance to metro stations will witness a decrease in importance and value, which will be reflected in housing prices,” he said, adding that major cities, where bike-sharing has a larger presence, have observed a significant drop in congestion rates of the central business areas.

by Chen Mengzhu '18