Machine Learning Technologies for Assessing and Classifying Shipping Facilities
A method includes receiving and processing raw shipment data using machine learning models trained using historical shipment data to identify and classify terminal shipment locations. A system includes a memory storing a set of computer-readable instructions and historical shipment data; and one or more processors interfaced with the memory and configured to execute the set of computer-readable instructions to cause the one or more processors to: receive and process raw shipment data using machine learning models trained to identify and classify terminal shipment locations. A non-transitory computer-readable storage medium configured to store instructions executable by a computer processor, the instructions comprising: instructions for receiving and processing raw shipment data using machine learning models trained using the historical shipment data to identify and classify terminal shipment locations.
1 . A computer-implemented method of using machine learning to improve automated shipping facility identification and classification by processing raw shipment data, the method comprising:
receiving, via one or more processors, the raw shipment data;
processing the raw shipment data using a first machine learning model trained using historical shipment data to identify one or more terminal shipment locations;
classifying each of the one or more terminal shipment locations using a second machine learning model trained using the historical shipment data to generate one or more facility classifications; and
storing the one or more facility classifications and the one or more terminal shipment locations in a memory.
2 . The computer-implemented method of claim 1 , wherein receiving the raw shipment data includes receiving real-time data from one or more vehicles.
3 . The computer-implemented method of claim 1 , wherein the historical shipment data includes at least one of:
an origin facility identifier, a destination facility identifier, a waypoint facility identifier, a facility materials identifier, a facility square footage size, an address, a latitude/longitude pair, a carrier identifier, a shipment count, a shipment size, a shipment shape, a shipment volume, a timestamp, geospatial data, facility building data, zoning data, landmark proximity data, electronic logging device data, GPS data, or vehicle identifier data.
4 . The computer-implemented method of claim 1 , wherein the second machine learning model is a logistic regression model; and wherein the historical shipment data is a time series data set including multiple historical shipments, each including a respective facility size, a respective facility landmark proximity, a respective shipment shape, and a respective shipment volume.
5 . The computer-implemented method of claim 1 , further comprising: processing a plurality of the one or more terminal shipment locations to infer one or more respective operational aspects of the one or more terminal shipment locations.
6 . The computer-implemented method of claim 1 , wherein the one or more facility classifications are selected from the group consisting of: (i) a warehouse facility, (ii) a retail facility, (iii) a weigh station, (iv) a harbor facility or (v) a rail yard facility.
7 . The computer-implemented method of claim 1 , further comprising:
displaying, by one or more processors, at least one of the one or more terminal shipment locations including the respective one or more facility classifications on a device of a user.
8 . A system for using machine learning for improved automated shipping facility identification and classification, comprising:
a memory storing a set of computer-readable instructions and historical shipment data; and
one or more processors interfaced with the memory, and configured to execute the set of computer-readable instructions to cause the one or more processors to:
receive, via one or more processors, raw shipment data;
process the raw shipment data using a first machine learning model trained using historical shipment data to identify one or more terminal shipment locations;
classify each of the one or more terminal shipment locations using a second machine learning model trained using the historical shipment data to generate one or more facility classifications; and
store the one or more facility classifications and the one or more terminal shipment locations in a memory.
9 . The system of claim 8 , wherein the one or more processors are configured to execute the set of computer-readable instructions to further cause the one or more processors to:
receive real-time data from one or more vehicles.
10 . The system of claim 8 , wherein the historical shipment data includes at least one of:
an origin facility identifier, a destination facility identifier, a waypoint facility identifier, a facility materials identifier, a facility square footage size, an address, a latitude/longitude pair, a carrier identifier, a shipment count, a shipment size, a shipment shape, a shipment volume, a timestamp, geospatial data, facility building data, zoning data, landmark proximity data, electronic logging device data, GPS data, or vehicle identifier data.
11 . The system of claim 8 , wherein the second machine learning model is a logistic regression model; and wherein the historical shipment data is a time series data set including multiple historical shipments, each including a respective facility size, a respective facility landmark proximity, a respective shipment shape, and a respective shipment volume.
12 . The system of claim 8 , wherein the one or more processors are configured to execute the set of computer-readable instructions to further cause the one or more processors to:
process a plurality of the one or more terminal shipment locations to infer one or more respective operational aspects of the one or more terminal shipment locations.
13 . The system of claim 8 , wherein the one or more facility classifications are selected from the group consisting of: (i) a warehouse facility, (ii) a retail facility, (iii) a weigh station, (iv) a harbor facility or (v) a rail yard facility.
14 . The system of claim 8 , wherein the one or more processors are configured to execute the set of computer-readable instructions to further cause the one or more processors to:
display, by one or more processors, at least one of the one or more terminal shipment locations including the respective one or more facility classifications on a device of a user.
15 . A non-transitory computer-readable storage medium configured to store instructions executable by a computer processor, the instructions comprising:
instructions for receiving, via one or more processors, raw shipment data; and
instructions for processing, using a first machine learning model trained using historical shipment data, the raw shipment data to identify one or more terminal shipment locations;
instructions for classifying, via one or more processors, each of the one or more terminal shipment locations using a second machine learning model trained using the historical shipment data to generate one or more facility classifications; and
instructions for storing, via one or more processors, the one or more facility classifications and the one or more terminal shipment locations in a memory.
16 . The non-transitory computer-readable medium of claim 15 , wherein the instructions for receiving the raw shipment data comprise:
instructions for receiving real-time data from one or more vehicles.
17 . The non-transitory computer-readable medium of claim 15 , wherein the historical shipment data includes at least one of:
an origin facility identifier, a destination facility identifier, a waypoint facility identifier, a facility materials identifier, a facility square footage size, an address, a latitude/longitude pair, a carrier identifier, a shipment count, a shipment size, a shipment shape, a shipment volume, a timestamp, geospatial data, facility building data, zoning data, landmark proximity data, electronic logging device data, GPS data, or vehicle identifier data.
18 . The non-transitory computer-readable medium of claim 15 , wherein the second machine learning model is a logistic regression model; and wherein the historical shipment data is a time series data set including multiple historical shipments, each including a respective facility size, a respective facility landmark proximity, a respective shipment shape, and a respective shipment volume.
19 . The non-transitory computer-readable medium of claim 15 , wherein the instructions further comprise:
instructions for processing a plurality of the one or more terminal shipment locations to infer one or more respective operational aspects of the one or more terminal shipment locations.
20 . The non-transitory computer-readable medium of claim 15 , wherein the instructions further comprise:
instructions for displaying, by one or more processors, at least one of the one or more terminal shipment locations including the respective one or more facility classifications on a device of a user.