Didi Launches Gaia Data Open Plan and Smart Traffic Signal Challenge to Advance Big Data and AI Research

While extensively leveraging big data and artificial intelligence to help cities innovate traffic governance, Didi is also actively collaborating with academia to explore scientific frontiers.

At the 2017 China Computer Conference (CNCC 2017), Didi announced two major initiatives: the Gaia Data Open Plan and the "Smart Signal" Challenge, aiming to promote broader academic research in the transportation field and invite global algorithm talent to accelerate innovation in smart traffic.

The Gaia Data Open Plan provides the academic community with free access to a portion of real, desensitized data. The first release includes raw trajectory data and order OD data (origin‑destination) for Didi’s premium and ride‑hailing services in selected areas of Chengdu over a continuous month. The data are anonymized, encrypted, and colored to ensure that no personal information can be traced back to individuals.

Beyond data sharing, Didi will also provide computing resources and funding to outstanding traffic research projects. In its first phase, Didi partners with the China Computer Federation (CCF) to launch a Youth Scholar Research Fund, encouraging young scholars to conduct cutting‑edge research and accelerate the translation of high‑quality academic results.

The "Smart Signal" Challenge offers algorithm enthusiasts a real‑world big‑data playground to tackle the global problem of congestion. The preliminary round focuses on "arterial intersection timing optimization," while the semi‑final round upgrades to "network‑wide intersection timing optimization." Participants use desensitized Didi vehicle trajectory data, calibrated traffic network simulations, and multiple real traffic flow inputs to evaluate their timing proposals, with scores calculated to the second for precise, scientific signal control.

Didi’s senior vice president and head of smart transportation, Zhang Wensong, noted that the platform processes 25 million orders daily, generating valuable traffic metrics such as average speed in a region, average stop count at an intersection approach, and queue length on a road segment. Leveraging this data, Didi has collaborated with over 20 cities—including Guiyang, Shenyang, Nanjing, Shenzhen, Jinan, and Wuhan—to deploy smart guidance screens, smart signals, and other AI‑driven solutions that support forward‑looking municipal planning and infrastructure layout.

Practical implementations of signal optimization in cities like Jinan and Wuhan demonstrate how big data and the internet can provide new approaches to traffic management, evolving from point‑and‑line optimizations on major roads to area‑wide improvements.

Overall, Didi is committed to building an efficient, open, and sustainable future mobility ecosystem, actively opening desensitized data and computing infrastructure to universities and research institutes, encouraging technological innovation, and jointly addressing world‑class traffic and environmental challenges.

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