In this second installment of my column series covering the major issues facing transportation authorities around the world, I’d like to turn my attention to data collection.
Last issue I wrote about how authorities can look to forming partnerships with private transportation data companies. However, authorities will always be uniquely responsible for collecting certain information: a prime example being lane closures due to maintenance and construction. DOTs are the only source for this information, and yet few agencies have a reliable way to report lane closures and openings in real time. State DOTs could improve their credibility with the public if lane closures were reported in a timely and accurate manner.
While it’s possible that DOTs could be tempted to see their data as a potential revenue source, the big trend in government is to make public data sets readily available for general benefit. Apart from lane closure information, DOTs will also be the primary source for a number of other unique types of transportation data: traffic camera feeds; weather and surface reporting from maintenance trucks; accident reports; and transit vehicle location. The more transparent and usable data sources are, the better public and private partners will be able to make use of it. Some DOTs, such as Massachusetts, have a strong commitment to making data public, and invite partners to develop innovative applications with it.
Of course, in the digital age, some data virtually collects itself. This takes us into the realm of big data: extremely large data sets that are generated by internet-connected devices. As we are aware, every time we use our smartphones, huge amounts of data are being generated and analyzed regarding our behaviors. Companies look through these data sets to find large patterns to predict consumer preferences, or smaller patterns to target individual advertising. In transportation, big data has been initially represented by the location reports of millions of connected cars, trucks and smartphones moving along in the traffic stream.
Companies use this location data to provide real-time speed maps of the US roadway system. In addition, crowdsourcing apps such as Waze use driver reports to identify accidents or other roadway incidents. As data analytics improve, these data sets will provide new ways for DOTs to look at project planning and provide access to real-time analytics to better manage the highway system and respond to incidents. Big data patterns may be able to locate incidents before they are reported, or even evaluate traffic operations data and predict where incidents could imminently occur. Purdue University and the Indiana DOT are currently using Inrix probe data to identify the end of freeway queues in real time, and dispatch law enforcement to warn approaching drivers. Big data is changing the way we analyze transportation use; it is important that DOTs stay ahead of the curve.
As DOTs begin to manage data sets comprised of public and private files, they will have a responsibility to certify the private data they report. Multiple private data providers will lead to multiple conditions reported in the same corridor. For accountability, DOTs will have to select the data provider that can most accurately provide information and certify that data is accurate.