Chinese ride-hailing giant Didi Chuxing is opening up its significant stores of transit data with the release of two major datasets in order to improve understanding of transport patterns and optimize infrastructure investments.

Why it matters: The move is likely to win the company goodwill from city officials after attracting heightened scrutiny from authorities, especially over the past year. Machine-learning applications, largely driven by data sharing, play a critical role in resource utilization and planning safer, smarter transport networks.

  • Didi in late 2017 first launched its GAIA Initiative, a global research platform under which scientists can apply for access to anonymized data to explore traffic solutions.

Detail: Didi will make available two of its anonymized historical TTI (Travel Time Index) datasets which index urban congestion, gathered from vehicles on its platform, Didi CTO Zhang Bo announced Friday at the China National Computer Congress summit in Suzhou.

  • The release contains traffic congestion indices, calculated using passenger trip information, as well as the average speed of motor vehicles on Didi’s platform over the past year in six Chinese major cities: Shenzhen, Chengdu, Xi’an, Jinan, Suzhou, and Haikou.
  • The other dataset includes detailed historical trip-level data, namely anonymized start and end points and route information from Didi’s Express and Premier service tiers for a two-month period in Chengdu and Xi’an in late 2018.
  • The ride-hailing giant said it has partnered with governments from more than 20 Chinese cities to provide innovative solutions for transport and traffic management, such as smart traffic signaling technologies. The company said adjusting the timing of more than 2,000 traffic lights across the country reduced congestion by 10% to 20% on average.
  • Didi was not immediately available for comment when contacted by TechNode on Monday.

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Context: Didi is the not the only company seeking to play an important role in a smart transportation system built around connected autonomous vehicles.

  • Uber in early 2017 launched an online website called Movement using data and tools which allowed users to measure travel times between points in cities including Washington D.C., Sydney, and Manila. It was updated two years later with a feature allowing users to track vehicle speeds down to the street level in a total of 38 cities.

Jill Shen is Shanghai-based technology reporter. She covers Chinese mobility, autonomous vehicles, and electric cars. Connect with her via e-mail: or Twitter: @jill_shen_sh

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