The Future Of Transportation Data And Technology – Part 2: What Can We Do?

In this two-part blog series, Iteris national data practice leader Anita Vandervalk will explore the future of transportation data and technology, and explain why transportation agencies need a comprehensive data strategy.

Data is the new currency - the perfect time for a data strategy to organize, connect and maximize the value of transportation data is now. But there is work to be done within transportation agencies to ensure maximum benefit to leverage of the proliferation of data and technology to meet growing customer expectations for mobility in the future.

The power is in agencies’ hands to harness and leverage public sector data and infrastructure, and to partner with the range of vendors in communications, architecture, cloud storage and computing to integrate transportation data to be prescriptive and continue to drive transportation’s strong role in the future of mobility.

What kind of data are we talking about? Incident data, speed, volume, location, origin, destination, and purpose of trips made by people, vehicles and freight. Sources such as road sensors, cameras, controllers, connected vehicles, phones, probes, and other IoT devices all play a role. When turned into performance measures such as travel time reliability, delay and cost of delay, all of this travel data can help agencies understand the impact and benefits of projects such as signal timing, work zone management, traveler information and other ITS solutions. This in turn provides support for planners and operators at transportation agencies to provide optimum safety and mobility solutions to their customers.

Agencies need a data strategy, data framework and a data hub to ensure data can be:

• Descriptive (what happened?)
  
  Mobility data and performance measures allow transportation agencies to monitor the state of the system
  
• Diagnostic (why did it happen?)
  
  Analyzing mobility data and performance measures together with weather and incident data can help to understand why delay and crashes are occurring.
  
• Predictive (what will happen?)
  
  Applying models with data analytics can help us to understand what will happen given weather, demand and probability of incidents. Predictive capability allows us to estimate impacts of improvement strategies on mobility and safety.
  
• Prescriptive (what should we do to make it happen?)
  
  This capability allows transportation agencies to operation and plan the system to ensure actions are taken based on fully integrated data to achieve a desired outcome.
  

An ideal solution takes the form of a Data Portal as shown below. The portal concept allows a variety of data sources to be combined seamlessly – taking advantage of storage and analytics in the cloud to sort through millions of records to draw inferences and scenarios with data. The portal allows transportation agencies to perform critical function such as performance management and operations.

Road to Success: How Do We Get There?

The key to overcoming challenges associated with leveraging, integrating and providing data to customers is to organize it with a Technology Data Framework. Are you leveraging data to generate insights and unlock value?

The role of the transportation agency in the world of big data, transportation disruptive forces and technology advancements is unique. The following framework for a transportation data strategy offers an approach that combines technology with management approaches on an equal footing.

• Data discovery and identification:
  
  Take full advantage of all sources – all sensors, traffic signals, cameras and roadway conditions (weather, incidents, events, etc.) and private sector sources (probes, social media, IoT). These include both structured and unstructured data.
  
• Data management and governance:
  
  Establish standards, roles and responsibilities to avoid overlap and gaps in data. In this step, one needs to partner with cross-disciplinary teams, representing both business owners of data and the IT elements.
  
• Data integration:
  
  Often the most challenging step requiring a variety of aspects including common identifiers (such as location), data storage (i.e. cloud), and system infrastructure/integration. Another challenging aspect in this step is simply getting access to all the data.
  
• Data analytics:
  
  Allows one to come up with predictive insights, communicate relevant information to travelers, understand travel behavior, and ultimately be able to make decisions and inform action on transportation choices. Analytics should not be carried out to find possible relationships between unrelated events but rather to inform transportation planning, policy and operations. Data analytics need to be embedded into tools that allow for automation but also for human intervention.
  

Applying these four steps in developing a data strategy will result in: 1. A data architecture describing how data are collected, stored, transformed, distributed and acted upon, and 2. An information architecture to govern the processes and rules to convert the data into information and action. The data strategy will support the development of a data portal to allow for integration, analysis, dissemination, and visualization of the data and information.

Keys for success for a data strategy and framework include:

• Strong and committed leadership
• Inclusion of public, private and academic partners
• Comprehensive planning process including shared vision and goals
• Clear value proposition of a Data Framework for all partners
  

Transportation agencies must develop a data strategy now that includes a data portal, to ensure a prominent role in guiding the future of connected mobility.

Read part one of this blog series here.

This article was originally published in FLITE, the official publication of the Florida Section Institute of Transportation Engineers.

About the Author:

Anita Vandervalk is associate vice president, Transportation Systems at Iteris.

Connect with Anita on LinkedIn.