Machine Learning in transportation has become the driving force for such mammoths as Amazon, DHL, and FedEx. Now, here are examples of data analytics in transportation. A use case of predictive analytics in transportation is the sales forecast. It consists in turning historical data collected from transportation management systems into a data-driven tool to forecast sales and shipping volumes.
Maching Learning application to transports
In most cases, such regression models consist of both historical data and external elements, including seasonality, climate, and even public holidays. Another use case for predictive models in logistics is predictive maintenance. The goal is to train neural networks on big data to anticipate potential failures and schedule repairs. Apart from predictive maintenance, sensor data is also helpful for achieving an entire fleet's real-time tracking. Thanks to IoT sensors, it is possible to monitor vehicles' speed, location, and direction in real-time.
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