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Toyota Aims to Offer Uber-Like Services for Tokyo Taxis Using AI

A Toyota JPN Taxi vehicle. Photographer: Tomohiro Ohsumi/Bloomberg

Predicting demand for rides has been a key part of Uber Technologies Inc.’s strategy for disrupting taxi and transport providers. Now, Toyota Motor Corp. is betting that it can direct drivers just as efficiently.

Asia’s biggest car manufacturer began testing a new taxi-dispatch system last month by using data from smartphones, cab locations, weather patterns and other factors to determine the most efficient distribution of Tokyo’s taxi fleet. The project, which includes taxi-ordering startup Japan Taxi, telecommunications carrier KDDI Corp. and Accenture Plc, has a 94 percent accuracy rate, the companies said in a statement Friday.

While Toyota has backed San Francisco-based Uber as an investor and partner, the automaker has also made investments in Japan Taxi, an Uber rival run by the chairman of Tokyo’s biggest taxi operator. Akio Toyoda, Toyota’s president, is betting that data will be a key part of the company’s future, and has sought out partnerships with tech companies.

One of the key differences between traditional taxi-dominated markets and those with ride-hailing services is the availability of rides during peak times, especially when it’s raining. Taxis usually become harder to find. Using software and flexible pricing, Uber and rivals such as Lyft, Grab and Didi are able to direct drivers to areas with greater demand, boosting efficiency and profits. During Toyota and Japan Taxi’s pilot program, drivers using the system were able to boost their sales by about 20 percent, better than the average of 9 percent, the Toyota City-based company said.

A JPN Taxi during a road test.

Photographer: Tomohiro Ohsumi/Bloomberg

Toyota started installing its TransLog devices in 500 Tokyo-area taxis last year. Eventually, the AI-based dispatch service will be expanded to additional taxis with an eye to a full roll-out this year. Beyond the latest service, collecting driving data and video in real-time would enable the construction of dynamic maps that could speed up the adoption of automated driving, among other things.

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