For more than 20 years, data scientists have recommended federal and state transportation agencies adopt data governance plans to answer important questions about our national transportation system. These include questions like why is there so much traffic congestion and where should we build new roads? Unfortunately, most state governments have failed to make significant progress in creating and adopting data governance plans.
Data governance plans establish criteria and requirements for data collecting and reporting, quality standards and other management policies. A 2018 survey revealed that just 19 percent, eight out of 43, responding state transportation departments (DOTs) had data governance councils.
Given that official federal data governance guidelines were only released in 2015, the number of DOTs interested in adopting better data standards could increase. Fortunately, the federal guidelines lay out a simple and easy way for transportation departments to establish better data governance.
According to the Federal Highway Administration (FHWA), the nationwide data governance model would be a three-tiered structure. Under the structure, a National Data Governance Advisory Council (DGAC) would set the overarching goals for state DOTs, such as viewing data and software as an enterprise asset, which means the data are an asset to the entire enterprise of government and not the property of each government entity to record and store data at their behest. It also sets standards such as ensuring data interoperability, open access, and machine-readability.
The other two tiers are data regimes and data stewards. Regimes consist of a set of managers who ensure technical standards are met. Data stewards are the subject matter experts responsible for on-the-ground execution of DGAC principles. A regime manager could serve double duty as the actual steward, but it is important that there be a managerial and accountability tier between the DGAC and stewards.
The DGAC would be a national body composed of state and federal officials, whereas most regimes would consist of state and local officials. Localized regimes could have slightly different procedures but would be required to always comply with DGAC standards.
This bottom-up but well organized and interconnected approach gives flexibility to local actors so that they can meet their own technology needs while also maximizing the amount of standardized and shareable software and data.
There are many benefits that state transportation departments around the country could reap from adopting this model.
State DOTs are currently required to report certain data to the federal government every year. Under the DGAC structure, if one regime discovers a superior and cheaper method to meet reporting standards, other regimes can adopt the better method. If two variables that measure the same category are being labeled differently between regimes, DGAC can help unify those definitions and labels.
The Federal Highway Administration is responsible for publishing much of the data that states report to the federal government.
Each year, Reason Foundation produces the Annual Highway Report, an evaluation of the conditions and cost-effectiveness of state-owned transportation systems, which is based primarily on data reported by states to FHWA. If states adopted better data governance, there would be more detailed, timely, and uniform information available in the FHWA repository of highway statistics. For example, the FHWA is currently not responsible for congestion data precisely because there is no unified plan behind measuring and collecting the data. Better data governance may enable more traffic and congestion analysis, as well as policy changes, because the data would be publicly available.
Better data would also spur more private innovation. Emerging technologies like driverless cars can currently enter into data and technology partnerships with state agencies, assuming privacy and intellectual property are protected. With better data governance, these partnerships could happen much more frequently and could generate more complex and high-performing technologies.
Academic research and universities with technology transfer programs also rely heavily on public data. Open and machine-readable data makes it easier for researchers to answer important transportation questions such as:
- How can we reduce congestion?
- Where should the new highways be constructed?
- How well are current roads being maintained?
- What is causing road damage?
- Why are certain states performing better than others?
- How can we make roads safer?
- What exactly is the state spending transportation money on?
Unfortunately, roughly 70 percent of the 2018 state DOT data governance survey respondents indicated that the major obstacle to better data governance was a lack of resources and staff. While some states have made good progress in this area by dedicating new resources to data projects, most fail to see the value of investing in data governance.
Policymakers need to understand that over the last 10 years the volume of transportation data has increased 30 times over and that computing capacity, if employed properly, allows for the transmission and deep analysis of this data.
While data governance plans are not a panacea for all modern transportation system problems, they would provide value to stakeholders and are being used to great effect by many private and public bodies around the world. To unlock many of the benefits of modern data and technology capabilities, more state transportation departments need to engage in better data governance.