commuter trips that mix segments of bus, light rail, and trolley systems. Urban Insights fuses data from five different sources, uses analytics to model trips, and identifies ways the MTS can improve its services.
Customers wanted to combine the data that Cubic’s management systems generate with many other data bases they collect to derive insights to improve their operations, Cole said. “This is something that was interesting to them,” he said. “They’ll have a better understanding now of how commuters and travelers are using the transit system.”
In the United Kingdom, “the road network is such a critical thing that closing one lane for maintenance can cause chaos,” Cole said. By using Urban Insights, transportation planners in London can run computerized simulations to model what might happen if different detours and lane closures were used to minimize the impact on traffic.
“The first area of innovation for us was in finding a way to manage very large volumes of detailed data, and to find a way to integrate them without losing any detail in the process,” said Wade Rosado, Urban Analytics’ director of analytics. To address that challenge, Rosado said Urban Insights developed a distributed data management and processing system using Apache Hadoop, an open-source software system for large-scale processing of unstructured data from a variety of sources.
“We’re willing to take disparate data sets that were never really meant to be spliced together in any meaningful way, and making it possible to use analytic software,” Rosado said.
Urban Insights also could integrate social media and other types of data to evaluate consumer sentiment, Rosado said. The data itself would remain in systems maintained and controlled by customers, but Urban Insights would “de-identify” information that might be used to identify individual users and their personal habits and preferences, Rosado said.
“I look at it like birds flocking,” Rosado said. “We’re not interested in what any one particular bird is doing.”
