Turning Freight Data into Better Decisions

Public agencies today have more freight data at their fingertips than ever before. A variety of GPS-based datasets exist that can reveal how trucks move, where congestion occurs and how supply chains function across regions. 

But for many transportation agencies, the opportunity does not lie in access to data — it’s turning data into insights that can inform actionable planning decisions.

This is the challenge that approaches like DeepView Freight are intended to address.

This platform brings together datasets, analytical methods and expert interpretation to support agencies working with complex freight data. Developed by WSP, the platform is used to help inform planning decisions across an array of freight-related challenges.

“Clients don’t just want more data,” said Sebastian Guerrero, freight and logistics practice lead, WSP in the U.S. “They want clarity. They want answers they can trust.”

Rather than relying upon a standalone software product or a one-size-fits-all dashboard, platforms like DeepView Freight reflect an approach that combines data analysis with hands-on freight planning expertise.

“At the core of freight planning today are these very large, complex datasets about truck movements,” Guerrero said. “But those datasets are hard to work with and even harder to interpret without specialized knowledge. Our focus has been on developing the tools and expertise to turn that data into insights agencies can actually use.”

We began working with freight GPS data before it became more widely used in transportation planning. Since then, these methods have been applied across a wide range of projects nationwide, helping to develop practical ways to use locational data to support planning decisions.

“A key part of this approach is that we’re not doing data processing for its own sake,” Guerrero said. “We’re using these methods to answer very specific planning questions for agencies.”

Today, many public-sector agencies invest heavily in commercial freight datasets — often spending six figures to acquire them — but struggle to extract full value.

“These datasets can cost $100,000, $200,000, even $300,000, and agencies are realizing only a fraction of the value from their investment,” Guerrero said. “They’re large, complicated and require specialized expertise to interpret.”

One way agencies are processing and interpreting large locational datasets is through approaches like DeepView Freight, translating results into planning insights that can inform decision-making. In practice, this can support more efficient project delivery and help agencies make fuller use of data they already have.

“It’s helping clients save resources and speed up project timelines,” Guerrero added. “And it strengthens the freight plans and studies they need to produce.”

This approach has been used across a wide range of freight planning efforts, particularly those driven by federal requirements and funding programs.

One common application is truck congestion analysis, a required component of state freight plans that must be updated every four years. Using truck GPS data, these analyses can help identify bottlenecks and congestion hotspots across highway networks.

“We’ve developed tools that generate those insights in a relatively short timeframe,” Guerrero said. “We’ve applied this approach in more than a dozen other states and cities across the country.”

In one such effort, freight locational data was used to pinpoint recurring bottlenecks along key corridors, giving agencies a clearer picture of where congestion was affecting freight movement most consistently.

Another major application is truck parking analysis, an issue that has gained national attention as demand has outpaced supply. With new federal funding available for truck parking investments, agencies need data-driven justification for where and how to make improvements.

“To pursue that funding, agencies need to understand where trucks are parking, where shortages exist and how parking behavior relates to freight routes and industrial areas,” Guerrero explained. “Those insights come directly from analyzing GPS-based freight data.”

Beyond these examples, similar approaches have been applied to a range of use cases — from supply chain analysis to emerging policy and infrastructure questions — each shaped by the specific decisions agencies need to make.

While freight data platforms have become more common, approaches to using that data continue to evolve. Rather than relying on a single dataset or fixed analytical framework, many projects require combining multiple sources to answer specific planning questions.

“Every dataset has strengths and limitations,” Guerrero said. “Some platforms are built around one data source, but we think a more flexible approach is better. Different questions require different data, and in some cases you need to combine multiple sources.”

DeepView Freight reflects this type of approach, drawing from a range of datasets and applying different analytical methods depending on the question being addressed.

This work spans both freight research and implementation, from contributing to broader guidance to supporting state and local planning efforts and projects.

“We’re involved at the research level, helping shape national guidance, and then implementing those same approaches on the ground for states and local agencies around the country,” Guerrero said.

At its core, this work is about making freight data more usable — helping translate multiple datasets into information that can inform planning and investment decisions.

As freight systems grow more complex and data-driven planning becomes more common, approaches like these provide a way for agencies to more clearly understand and respond to evolving freight needs.

Learn more about the DeepView Freight platform.