The Past, Present, and Future of Transportation Data

The Past, Present, and Future of Transportation Data


October 14, 2021
Brendan Wesdock, MCP, GISP

The Fourth Industrial Revolution and the Impact of Technology on Transportation

We often hear about the Fourth Industrial Revolution and its transformative impact on the way we work. In the realm of data, this revolution is poised to create monumental changes that will permeate every facet of our lives, including how we store data.

For decades, we have focused on collecting and organizing data securely. Over time, this information was sectioned off into silos of valuable data storage. Today, we stand on the precipice of a new era where the emphasis shifts from data management systems to using data for prediction.

We’re beginning to see technologies like real-time data integration with past data sources to develop logical conclusions about what will happen. The high volume of data means artificial intelligence (AI) and machine learning will become essential in the data analysis and integration process.

Learning from the Past: Predictive Transportation and Infrastructure Management

The traditional approach of planning, designing, building, operating, maintaining, and replacing infrastructure may need to be revised. Other industries have successfully adopted predictive maintenance and replacement data models, saving considerable resources. The transportation industry can draw inspiration from these success stories, breaking down silos and fostering a connected digital delivery strategy that informs how we manage data for planning, design, construction, and operations.

The human mind alone can’t process the sheer volume of data generated. It forces us to ask ourselves how we extract the right insights to turn the data into usable information. Enter AI and machine learning, enabling us to harness data’s power like never before. AI and modern transport systems must be integrated into the planning phase to achieve a more holistic approach to these project phases.

John MacAdam, administrator and web developer for the Ohio Department of Transportation (DOT), believes transitioning to an integrated AI approach will require overcoming resistance.

“You’re going to have to convince the people who are used to the way of doing things that we have a predicted impact, or some data based on AI. How are they going to trust that more than the conservative, traditional way? I’m not saying we shouldn’t have that direction, but there will be resistance.”

John MacAdam, Administrator and Web Developer, Ohio DOT

Theo Agelopoulos, vice president of architecture, engineering, and construction (AEC) design and engineering strategy at Autodesk, explains the benefits of AI and machine learning:

“To experience the benefits of this technology, you need lots of strong data, but you also need a platform that provides you with access to run these different types of algorithms to gain insights. The cloud and these emerging technologies like AI and machine learning allow us to transform how we plan, design, build, and operate these assets.”

Theo Agelopoulos, Vice President of AEC Design and Engineering Strategy, Autodesk

Start Implementing Data Integration Processes Now

The AEC industry is at a pivot point: progress will not be made if we continue to focus on the traditional project construction methods. If we look at infrastructure management, data is what the future holds. With that in mind, we must continue progressing the industry in a positive direction.

MacAdam says thorough analysis is the key to initiating rapid progress.

“We must start circling back to assumptions we made in project development and ask ourselves, are those benefits realized or not? So that starting point is making sure we put these things into operations, running before-and-after analysis, seeing whether our assumptions were correct, and then fine-tuning those models. That’s a no-brainer and should be done on every major project.”

John MacAdam, Administrator and Web Developer, Ohio DOT

Despite the immense benefits of these new technologies, industry professionals must carefully weigh a handful of considerations.

“One of the things we’re going to have to solve is intellectual property rights. Who owns the data, and who can use the data? We still have a lot of legacy and limitations on that. You could argue that the owner thinks they own the data, even between the design company and a construction company. We must work through some internet protocol (IP) challenges as an industry as well.”

Theo Agelopoulos, Vice President of AEC Design and Engineering Strategy, Autodesk

Data-Driven Transportation Infrastructure Management

Historically, data collection was fragmented, exemplified by speed data collection through road loops. Today, we’re shifting towards more comprehensive and cost-effective data collection methods like sensors and radar stored in data warehouses. Real-time speed data was once a novelty but has become a commodity in data processing. This data now fuels predictions of bottlenecks, dangerous areas, and potential secondary crashes gradually integrating into our decision systems.

Creating an open-source data environment that standardizes data sets is essential. This standardization will facilitate data integration, enabling different sources to communicate effectively. Intellectual property rights, data security, and business models are significant challenges that require solutions. Establishing who owns data and how to use it is critical.

Recruiting Tomorrow's Infrastructure Professionals

This future also addresses the pressing issue of attracting and retaining talent in the transport industry. The future of work in transportation will involve data scientists and computer programmers. Automating repetitive tasks will free professionals to focus on data analysis and informed decision-making, making the industry more appealing to tech-savvy talent.

MacAdam explains that the emerging workforce is primed to enhance the future of transportation following this technologically involved vision.

“The younger generation is much more comfortable playing with data, much more comfortable with lightweight software development, writing a little bit of Python, doing a little bit of web development; it doesn’t scare them as much as it might have previous generations. It’s just taking advantage of the skill sets they’re coming in with. They’re more familiar with all these technologies.”

John MacAdam, Administrator and Web Developer, Ohio DOT

A valuable trait of the incoming workers is their pursuit of meaningful work with a significant societal impact. Understanding the broader effect of work is crucial for the younger workforce, as they hope to connect their daily tasks to larger societal goals.

Many young professionals prioritize the question, ”Why are we doing this?” It is important for them to understand how their work will impact industry, the environment, and society with a comprehensive thousand-year vision. Working according to our teams’ common goals and values will increase talent retention and exact a passionate driving force behind everything our team does, yielding a high quality of work.

Embracing the Future of Transportation Data

As we move toward this utopian vision of using predictive data and machine learning in the transportation industry, it’s crucial to consider its implications for infrastructure users, workforce attraction and retention, and future funding. Embracing clean, accessible data is the first step toward reshaping the transportation industry for a more innovative, efficient, and sustainable future.

Brendan is smiling in his headshot. He is wearing a white shirt, a blue and white striped tie, and a dark blue plaid suit jacket.

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