LESS EMPTY TRUCKS.
Empty Trucks =
Less Money and More CO2
It’s common sense that trucks that drive one way full and return empty is a waste of opportunity and emissions. The better route is to be fully loaded coming and going.
RunBuggy’s data science team is tackling the challenge of empty trucks by developing algorithms and machine learning models that match and group transportation orders so that we can help shippers reduce cost, transporters maximize earnings and minimize CO2 emissions for the planet.
How We Keep Trucks Full
Machine Learning, advanced analytics, and data science are the tools we use to keep trucks full and minimize emissions.
GPS data of all trucks and paths can be used to understand driver behavior and influence matching algorithms.
GIS -based nationwide historical and real-time traffic traffic data allows us to determine trucking specific routes. We can compare the optimal routes to actual data to inform our grouping results
Location optimization algorithms allow us to direct inventory to markets by combining cost to deliver, speed to deliver, and regional specific sales prices.
Order-transporter matching algorithms allow us to efficiently source drivers by combining internal and external datasets of transporter behaviors and truck details.
Order grouping machine learning models combine multiple orders with similar origin and destinations together to increase truck load, decrease delivery time and carbon footprint.
TOTAL CO2 SAVED