Geoff BoeingMy article "Travel Time Prediction from Sparse Open Data" has just been published in the International Journal of Geographical Information Science. We tackle a longstanding problem of how to predict realistic driving travel times without access to expensive proprietary data: https://buff.ly/wHhiCJy
Planners often still rely on "naïve" methods (e.g., minimizing Euclidean distance, network distance, or speed limit based traversal time) that systematically under-predict real driving times. This is a problem if it makes driving seem unrealistically fast relative to transit, biking, or walking.
At the other extreme, state-of-the-art models in computer science and transportation engineering can achieve really good accuracy, but often require billions of observations, deep learning models, and massive computational resources and capacity.
We argue that planners and applied researchers need a cheaper, easier middle ground to predict minimally-congested but accurate travel times:
- a method that uses free, open data
- runs on ordinary hardware
- and substantially improves accuracy over old naïve approaches
Using LA as a case study, we combine open data on street networks, speed limits, traffic controls, and turns with a small training sample of empirical travel times from the Google Routes API.
Whereas a naïve model under-predicts travel time by >3 minutes on average, our model mis-predicts by <1 second on average and achieves an out-of-sample MAPE of ~8%, similar to far more data-intensive approaches.
Our goal is not to replace state-of-the-art congested travel models, but to equip less-resourced planners, scholars, and community advocates with a free, open, and accurate tool for accessibility analysis, scenario planning, and evidence-based interventions when resources are limited.
For more, check out our article at IJGIS: https://doi.org/10.1080/13658816.2026.2628193
Or the open-access preprint here: https://osf.io/qepc6_v1