Compelling data insights from transportation networks equals opportunity for urban planners

By Wade Rosado

In February 2014, the USDOT Transportation Secretary Anthony Foxx launched a government program that was essentially a call for ideas from the general public for web-based tools, data visualizations, mobile apps or other innovative technologies that can access publicly available datasets. He said, “At the DOT, we think it’s time to take transportation data to the next level. It’s all part of a balanced and responsible approach to improving our transportation system.” The categories of the program’s focus were safety, transportation access and managing traffic and congestion. All things that are critical to a city and the health of its economy. The very existence of this program underscores how critical data – especially access to data – has become in transportation efficiency and more broadly, urban planning.

Some brief examples of how data insights will allow urban planners to shape the future of transportation:

• Define new public transit routes requiring fewer connections
• Align routes and timetables to reduce wait time for travelers
• Predict and analyze revenue generated vs. the cost of delivering services
• Predict impacts on roads, highways and public transit of planned road-works or transit maintenance projects
• Predict impacts to transportation and local economy of major unplanned events such as a labor strike
• Examine the impact of major commercial development on transportation and commerce so public leaders can maximize the public benefit of such a project

Presently, the transportation industry is a leader in generating  data and enabling the Internet of Things. That means that each day, huge volumes of operational and transportation data are being generated from enterprise sources, in addition to on-board sensors found in busses, rail cars and metro and ferry systems, to name just a few. These massive datasets are often locked in siloes, from passenger counting systems, vehicle location systems, ticketing and fare collection systems, as well as scheduling and asset management systems. What often challenges cities has been that the agencies that generate and manage these valuable data sources haven’t had the means to harvest them to gain insight from them in a way that supports the broader urban planning effort.

Recently, Microsoft sponsored a report from analyst firm IDC, which suggests that over the next four years, worldwide government organizations can potentially gain more than $200 billion in value from data simply by connecting data streams, using new analytics tools, delivering insights to more people and ensuring that the information is expedited. It is clear from those numbers that big data is a big opportunity.

If city and county managers can find ways to incorporate all of this rich transportation data into their urban planning processes, the realized benefits will be substantial to citizens and the overall economy of a city.

Secretary Foxx’s challenge suggests that the answers can be delivered through the application of data science, a discipline that can finally unlock the potential of these rich information stores and allow agencies and city planners to see a fuller, more accurate picture of the factors and dynamics causing inefficiencies in the delivery and consumption of multi-modal transportation services. The good news is that the technology necessary to accomplish these goals is available today. However, the true differentiator is found in firms that have the knowledge and transportation sector DNA to deliver on the promise of data science to address the specific needs of the transportation agencies.

San Diego is a great example, where the Metropolitan Transit System (MTS) is using big data and predictive analytic tools to truly understand how travelers use multi-modal services, like light rail and bus. Through this approach they are able to contrast those patterns of usage with assumptions that underpin current planning of services. The project involves fusing five independent data sources produced by four different technology vendor systems, as well as data collected manually. None of these data resources were designed to be integrated in a meaningful way with each other, but a transportation-specific usage model is capable of doing exactly that. San Diego MTS will use the resulting information to identify opportunities for improved services by pinpointing any incongruence between the scheduling and resourcing of services with the way travelers actually use the services.

Both process improvement and the application of predictive analytics will help evolve existing transportation networks and the way they operate. Anticipating traveler response to service changes and identifying stress points in transportation networks in order to suggest remedies will define how to move more people faster and more conveniently.  Improving planning tools and facilitating the restructuring of transportation networks to achieve operational efficiencies will lower costs. In the future, these same tools will help influence behavioral change among travelers and directly influence travel choices made in real time.

The positive economic impacts on cities that efficiently operate their transportation networks are plain to see. Connecting more people to their jobs, schools and housing and community resources will spur urban development and improve the quality of life for those living in cities. At stake is nothing less than the global competitiveness of the country’s vast transportation system, long regarded as a major economic advantage.

Wade Rosado is director of analytics for Urban Insights, responsible for advanced analytics project and program management integrated into the firm’s consulting services and technology offerings.

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