Transportation Machine Learning Archives

Transportation Machine Learning

Businesses have long struggled with how to move assets around the transportation network in the most efficient way. There are many variables to contend with: shifting demand, human error, traffic, fuel costs, weather, etc. Mosaic Data Science carries over a decade of experience designing and developing predictive analysis and decision support tools for NASA, the FAA, Boeing, Lockheed Martin, UPS, and FedEx. We provide world-class analytics consulting to transportation and logistics clients, applying cutting-edge machine learning and algorithm development techniques coupled with unparalleled domain knowledge.

Case Studies

Predictive Trucking Analytics

We revamped this trucking operator’s demand forecasting models to optimize fleet placement across North America.

By Drew Clancy, 8 yearsAugust 16, 2017 ago

White Papers

Dispatch Routing Optimization

In this whitepaper, we examine how different companies can attack the dispatch routing optimization problem using ML & AI.

By Drew Clancy, 8 yearsJanuary 25, 2017 ago

Blogs

Human Decision Making in Machine Learning Deployment for Air Traffic Flow Management

Being able to accept machine learning outputs in the decision making process is critically important, especially in Air Traffic decisions.

By Drew Clancy, 8 yearsDecember 7, 2016 ago

Case Studies

Dynamic Airspace Configuration

This case study examines how Mosaic helped NASA and the FAA dynamically balance airspace capacity & demand.

By Drew Clancy, 11 yearsFebruary 3, 2014 ago

Case Studies

Predicting Ground-Traffic Delays Resulting from Weather and Traffic Uncertainties

We built a predictive machine learning model that incorporates weather forecasts and air traffic movements to provide decision support to air traffic controllers.

By Drew Clancy, 11 yearsJanuary 30, 2014 ago