The challenge? Integrating various datasets, said Alex Pazuchanics, assistant director of planning, policy and permitting at the city’s Department of Mobility and Infrastructure…

Usually such data is siloed and used for a specific purpose, such as when a city comes up with bus routes, explained Zhen Qian, director of CMU’s Mobility Data Analytics Center in Oakland.

And even though startups like Gridwise and LaneSpotter could enable better infrastructure decisions, the “old-fashioned way” is still popular, he said.

To predict traffic on the Parkway East, for example, city planners rely on inductive loop detectors, buried in the pavement, and radar detectors set up on the shoulder to count cars.

“It’s very useful, but it misses a lot things,” Mr. Qian explained. “It’s a fixed location number. You don’t know where people are going, where they’re coming from, what route they take, why they take Uber over the bus. This is what I’m missing.”
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