Didi plans to solve traffic jams with big data
Fri 27 May 2016
As the largest ride-hailing app in China, Didi Chuxing (formerly Didi Kuaidi) has access to enormous amounts of data. Today, Didi announced that they intend to use the big data gathered by their app to solve the problem of traffic jams in the cities where they operate. With over 1.43 billion rides completed in 2015, and a billion-dollar investment from Apple, Didi now has the data and resources to take on this complex problem to the benefit of their customers and themselves.
The Didi approach to traffic is fairly simple. ‘The Great Tidal Strategy,’ or ‘Tides’ for short, is the idea that traffic problems can be solved if all of the vehicles under Didi’s service umbrella are dispatched and routed logically. To accomplish this, Didi will attempt to use the information that they have to react to traffic problems in real time by promoting ride-sharing and routing travelers around existing traffic situations. They also plan to create predictive models based on historical data, to anticipate where traffic jams are likely to occur and route their drivers around potential problems as well. Because the Didi system can see both the passenger location and destination, they can route drivers in a way that avoids encountering existing traffic problems as well as causing new ones. Didi can also add drivers and customers to a carpool mode for efficiency, and notify drivers of where the high demand hotspots are, and tell them how to get there easily.
A spokesperson for Didi Chuxing told Tech in Asia that Tides will “help mitigate traffic jams before they develop and allow us to prevent surge pricing at peak hours.” Surge pricing is a method used by ride-hailing apps including Didi and Uber, whereby rides cost more during peak hours. This aims to both increase the supply of drivers, by encouraging them with higher rewards, and reduce the demand for drivers, by discouraging riders with higher costs. It is a practical, albeit inelegant solution to a lack of available resources during peak traffic times. By using predictive algorithms to anticipate traffic jams, and real-time data to route drivers around existing traffic, Didi hopes to eliminate surge pricing as well as mitigate traffic problems altogether. This also adds another level of differentiation between Didi and Uber. If Uber wishes to remain competitive with Didi, they will have to change their surge-pricing model as well, and customers may be tempted to stick with the company that can help them to avoid traffic while reaching their destination.
