One major research effort at TREC has to do with active transportation data fusion.

What is Data Fusion?

Data Fusion refers to the combining of multiple data sources into one model, in order to leverage the advantages of each source and ensure the model is as accurate as possible.

Learn more about how this works in a recorded seminar from March 2025.

The Basic Idea

Traditional permanent and short-term counting methods can directly provide counts, but are limited to certain locations or short periods of time. Meanwhile, crowdsourced data (such as Strava or StreetLight) can cover a wider area but with less accuracy, as they only capture a subset of users. Fusing the two methods together–potentially with the use of deep learning algorithms–is a promising way to get the best of both. 

The Research

These research efforts got underway in 2018 with funding from the National Institute for Transportation and Communities (NITC). NITC launched a pooled fund project with support from the DOTs of Oregon, Virginia, Colorado, Utah, and the District of Columbia, as well as Central Lane MPO and the Cities of Portland and Bend, Oregon. With matching funds from NITC, those agencies came together to fund the initial project Exploring Data Fusion Techniques to Estimate Network-Wide Bicycle Volumes, with a research team led by Sirisha Kothuri.

Subsequent projects by Kothuri and her team include:

• Evaluating the Potential of Crowdsourced Data to Estimate Network-Wide Bicycle Volumes
• Exploring the Use of Crowdsourced Data Sources for Pedestrian Count Estimations
• Improving the Accuracy and Precision of Bicycle Volume Estimates Using Advanced Machine Learning Approaches

This emerging method allows transportation agencies and state departments of transportation (DOTs) to track changes in walking and bicycling mode share over time, prioritize projects, plan and design new infrastructure, conduct safety analyses, and estimate public health impacts.