The ETA results were improved by 57%. OTP results noted a 37% reduction in the number of false delays and a 22% reduction in the number of false on-times.
Meet our client
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Client’s Challenge
An award-winning transportation management company sought
to optimize their platform’s functionalities, focusing on Estimated Time of Arrival (ETA) and On-time Probability (OTP). These metrics were critical for improving the optimization of deadhead miles—distances traveled without loads.
Our Solution
We developed an ML engine to calculate delay probabilities and estimated arrival times. The system analyzed factors such as weather conditions, truck/trailer information, driver hours of service, resting periods, and driving styles. We employed artificial neural networks and random forest methods to achieve highly accurate results.
Client’s Benefits
The solution delivered a 57% improvement in ETA accuracy. OTP results saw a 37% reduction in false delays and a 22% reduction in false on-time estimates, enhancing the platform’s efficiency.
