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How Transportation Agencies Can Leverage the AI Revolution

Artificial intelligence (AI) is a capability that combines computing power, large amounts of data and human guidance to enable problem solving. Recent advancements in technology allow users to leverage AI in ever-expanding ways. When deployed effectively, AI has the potential to increase efficiency in data analysis, provide new insights and make predictions about future outcomes. Because of these recent advancements, transportation professionals are interested in the future potentials of AI to bring efficiencies and automation to their systems.

Although many applications of AI are still being tested, some transportation agencies have already turned to these capabilities for impactful solutions that are making a real difference in their work today. These agencies are using tools such as intelligence signaling for traffic control, image recognition for tolling purposes and various monitoring devices for tracking asset health and traffic flow. Along the way, AI is absorbing large amounts of data generated by infrastructure, vehicles and other sources to better understand, report, monitor or identify potential issues.

There are many other examples where AI is tackling some enduring challenges in the U.S. transportation sector:

  • Michigan Department of Transportation is capturing the locations of structural assets and detailed information about the conditions of each asset using autonomous-flight drones and AI data processing.
  • Wisconsin Department of Transportation is turning to AI to predict pedestrian and bicycle traffic volume based on changes in origin and destination features as well as route quality. This capability enables WisDOT to quantify the public benefit of improved bike and pedestrian mobility on all infrastructure improvement projects statewide.
  • Virginia Department of Transportation, in partnership with other agencies, is leveraging AI to analyze travel data to monitor emerging conditions and recommend plans for coordination to reduce congestion, incidents and traffic events. Their AI solution predicts the impact of disruptions to the transportation network and provides automation to the coordinated response options.
  • Florida Department of Transportation is using AI to inventory all traffic control devices throughout a 26-mile roadway corridor. Its trained AI model collects corridor 360-degree imagery and generates a complete inventory of traffic control devices, including traffic signals and signs detailing their locations, types and condition status.

An Intrinsic Opportunity

AI is particularly significant right now, as transportation agencies benefit from one of the biggest federal infrastructure investments in decades – the Bipartisan Infrastructure Law (BIL). As funding is allocated and new projects take shape, agencies can look for ways to enhance their operations through improved workflows and automation, with AI emerging as a potential tool in this transformative process.

In navigating the challenges of workforce development and propelling projects forward more efficiently, AI is set to play a pivotal role. Its application are wide-ranging, from predicting freight movements by aggregating traffic data and weighing station observations to enhancing asset management strategies and optimizing traffic load in urban areas. By providing agencies with predictability in identifying patterns of extremely heavy freight movements, AI empowers the private sector to streamline traffic and consider alternative routes, which can contribute to improved overall travel patterns.

Furthermore, AI opens up a realm of possibilities through simulation capabilities. Agencies can harness AI to run simulations across an array of scenarios, offering an improved understanding of potential future needs. For instance, in anticipating the demand for electric vehicle charging stations, agencies can simulate scenarios based on variables such as the projected increase in battery life over the next five years or the expected rate of public adoption of electric vehicles. This foresight enables agencies to proactively plan for infrastructure requirements and accommodate evolving transportation landscapes.

Data Drives Outcomes

AI has the potential to increase how efficiently and accurately transportation agencies analyze information. But without robust and curated data, AI’s value diminishes. The larger and cleaner the dataset used, the better AI can become at problem-solving and prediction, leading to improved outcomes. Data can include information from real-time traffic conditions and incident reports to weather patterns and the environment to devices monitoring themselves.

As such, transportation agencies are structuring their data more effectively, creating data warehouses, data lakes and developing governance strategies for the future implementation of AI use cases. These strategies enable the right roles, tools and workflows for managing data securely throughout an organization while enabling future AI applications in the effort to make transportation systems more efficient, safe, and sustainable.

A Future with AI

AI is rapidly advancing and will continue to play a growing role in the transportation space. This revolutionary suite of capabilities can be leveraged in many ways to improve mobility for people and goods. Transportation agencies can assess their readiness for AI, develop a data strategy and identify areas where the technology can be most helpful to their processes. And as it always does, the transportation industry will adapt, and benefit, from this technology.


Jeff Siegel
Digital Transformation Solutions Director
HNTB Corporation

Jeff Siegel leads HNTB’s discipline of professionals that focus on empowering digital platforms and data-driven initiatives to build equitable, resilient and sustainable infrastructure, enterprises and communities. Siegel is committed to advancing the geospatial and information technology industry as it relates to transportation and engineering. Jeff has more than 29 years of experience specializing in the implementation of digital and data management solutions for transportation agencies in all markets that HNTB serves. Read more from Jeff here.

Contact Jeff at JSiegel@HNTB.com.


Craig Bettmann
Data Sciences and Analytics/AI Team Lead
HNTB Corporation

Craig Bettmann is the data sciences and analytics/AI leader in HNTB’s Digital Transformation Solutions group. Bettmann brings more than two decades of experience using big data to better understand customer behavior and make process improvements. He provides data-driven solutions by utilizing AI, machine learning, business intelligence, intensive data mining, geospatial data, demographic segmentation and profiling, predictive modeling and benefit-cost analysis to advance client objectives.

Contact Craig at CBettmann@HNTB.com.