Accelerate >> Los Angeles: Guest Post by Ruben Hovanesian

Accelerate >> Los AngelesThe Accelerate >> Los Angeles event hosted by the LA chapter of Young Professionals in Transportation (YPT) was a great opportunity to hear about big data and engaging the public. There was an overall agreement between all of the panelists that the transportation planning and engineering fields are venturing through some new territory with the immense amount of data now available as well as now having access to data that was previously too difficult to gather.

Panelists for big data described some of the general trends in industry. Rob Hranac, VP at Iteris, spoke about how data is being used from an operational perspective. Fascinating to see how we now have the capability to “see” individual vehicles as they travel along a road. You can determine not only the traditional speed and get a count but can also determine the size of vehicle, and it is all live! Ron Milam, Principal at Fehr & Peers, spoke on big data from the perspective of transportation planning. It’s amazing just how quickly a massive set of data can be attained and used to develop an accurate travel model. The models are now even more reliable; using mobile devices as data points, now we can know all of the actual origin-destination points without heavily relying on estimations or regressions.

The panelists discussed how there is a shift in the industry from expanding the transportation network to focusing more on managing the networks, and as Rob said, hopefully leading to efficiency and optimization. Another shift is going from measuring infrastructure to measuring travel experience. Instead of focusing solely on the average speed of drivers on a roadway, we now want to measure what that travel experience is like holistically. However, the data for active transportation, such as cycling and walking, still has some catching up to do. Especially since this is where privacy concerns tend to be, as your data is identifying a specific person, and no longer just a vehicle. The discussion ended with an agreement that bigger data doesn’t necessarily mean better data.

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