Tech companies are using their data to solve China’s traffic problems
As data analytics develop and mature, transportation will be one of the most important use-cases for the technology
Air pollution, driving restrictions, and devastatingly long traffic jams are an everyday nuisance for many Chinese commuters. Some traffic jams in China are so bad, they’ve even earned their own Wikipedia entries. But companies such as Didi, ofo, and other local innovators are using big data to bring us into the era of smart commuting.
One of the companies on the forefront of urban travel innovation is bike-rental company ofo. With 6 million bicycles in 120 cities across five countries and plans to expand to 200 cities worldwide, ofo is collecting tons of information about user behavior.
“When it comes to city construction and planning, especially when it comes to what role bicycles will play in the process, there has been a lack of data”, said ofo co-founder Austin Zhang during last week’s TechCrunch event in Shenzhen.
“Through the development of bike sharing, our own data from ofo, the growth of everyday use of our product by tens of millions of people on city streets, through our own back-end, we can see what are the peak hours at the most demanding places, what places are less demanding, which areas are congested and which are not. This can serve as a reference for future urban planning.”
But ofo is not the only Chinese company changing urban transportation in China. It is currently collaborating with ride-hailing giant Didi to transform public transportation. The two companies are designing more efficient bike-bus transfer options for short-distance travelers. ofo’s bike-routing analytics will help develop AI-powered algorithms for DiDi’s real-time bus tracker.
Didi is also developing its own public transportation solutions with support from city governments. Its Smart Transportation Feature Team, which was established last year, is using the enormous amount of data generated by its 400 million users across 400 cities to upgrade transportation in nearly 20 Chinese cities.
In Shenzhen, Didi has gained access to the city’s urban bus, subway, taxi, bicycle and road infrastructure data and plans to build a data-driven smart transportation system for the city using the company’s AI technology. In other cities, the company is already working on reducing congestion by adjusting traffic lights and adding smart traffic screens with an ETA forecast.
Didi’s vision of future transportation is shared, electric, and automated. With more people sharing their vehicles there will be fewer cars on the streets and those cars will be more efficient than ever. And the key to all of these advances is AI and machine learning.
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“Looking forward to the next 10, 20 or more years, intelligent machines will become more and more important; machines will in many aspects do things better than humans, ” said Vice President of the US-based DiDi Research Institute and Head of DiDi Labs Dr. Fengmin Gong during last week’s TechCrunch Shenzhen.
Other companies in China are also looking to make their transportation solutions intelligent. Electric car makers such as Singulato (奇点汽车), smart e-scooter manufacturer Niu (小牛) and transportation robot designer Ninebot hope to bring AI to China’s streets.
These companies are developing different transportation solutions for all kinds of travelers: smart cars for long distance, e-scooters suitable for traveling up to 5 kilometers, and short-distance unicycles.
But the biggest changes to our commute will be brought by self-driving. In the future, autonomous vehicles could also completely change our urban landscape. Assistant to the President at Singulato, James Gao told the audience that he can imagine a world where streets will disappear underground, making our cities look and feel completely different.
“If we imagine a world with self-driving, everything is going to be changed, the infrastructure will be totally unlike today’s,” said Gao.
However, when it comes to automated vehicles, big data will not be enough. Putting self-driving cars on the street will require legal frameworks, a transition period and a degree of caution, Dr.Gong noted.
“This is precisely the biggest challenge,” said Dr.Gong. “On Didi’s side we have done a lot of research, and we also took advantage of our big data platform. We believe that this is the most central thing.”
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