MACHINE LEARNING TECHNIQUES FOR INFERRING TRANSIT TIMES AND MODES FOR SHIPMENTS

1 . A computer-implemented method of using machine learning to construct and train a graph to predict target outcomes corresponding to a cargo shipment, the method comprising:

constructing, via one or more processors, a plurality of location nodes, wherein each of the location nodes corresponds to a respective location type,

wherein each of the location nodes corresponds to a respective real-world location, and

wherein each of the location nodes includes a mode input, a dray input, a mode output, and a dray output;

adding, via one or more processors, the plurality of location nodes to the graph;

receiving, via one or more processors, an origin input parameter, a destination input parameter, and one or more shipment events corresponding to the cargo shipment; and

processing, via one or more processors, the shipment events using the graph to determine one or more target outcomes corresponding to the cargo shipment.

2 . The computer-implemented method of claim 1 ,

wherein each respective location type is selected from the group consisting of (i) seaport, (ii) railyard and (iii) airport.

3 . The computer-implemented method of claim 1 ,

wherein the one or more shipment events include at least one of (i) a transit event, (ii) a dwell event or (iii) a dray event.

4 . The computer-implemented method of claim 1 ,

wherein the target outcomes include at least one of (i) a net transit time of the cargo shipment, (ii) a net transit cost of the cargo shipment, (iii) a net emissions measure of the cargo shipment.

5 . The computer-implemented method of claim 1 ,

wherein processing the events using the graph to determine one or more target outcomes corresponding to the cargo shipment includes:

training, via one or more processors, a machine learning model using historical data to predict information related to at least one segment of the cargo shipment.

6 . The computer-implement method of claim 1 ,

wherein the cargo shipment includes at least one trucking segment, and

wherein processing, via one or more processors, the events using the graph to determine one or more target outcomes corresponding to the cargo shipment includes:

dynamically generating a node representing an origin of the trucking segment;

adding the dynamically-generated node to the graph;

selecting one or more candidate nodes; and

dynamically connecting the dynamically-generated node to each of the one or more candidate nodes.

7 . The computer-implemented method of claim 1 ,

wherein processing, via one or more processors, the events using the graph to determine one or more target outcomes corresponding to the cargo shipment includes:

determining the one or more target outcomes based on one or more optimization metrics and/or one or more optimization constraints.

8 . A computing system for using machine learning to construct and train a graph to predict target outcomes corresponding to a cargo shipment, comprising:

one or more processors; and

one or more memories having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing system to:

construct a plurality of location nodes, wherein each of the location nodes corresponds to a respective location type,

wherein each of the location nodes corresponds to a respective real-world location, and

wherein each of the location nodes includes a mode input, a dray input, a mode output, and a dray output;

add the plurality of location nodes to the graph;

receive an origin input parameter, a destination input parameter, and one or more shipment events corresponding to the cargo shipment; and

process, via one or more processors, the shipment events using the graph to determine one or more target outcomes corresponding to the cargo shipment.

9 . The computing system of claim 8 , the one or more memories having stored thereon further instructions that, when executed by the one or more processors, cause the computing system to:

select each respective location type from the group consisting of (i) seaport, (ii) railyard and (iii) airport.

10 . The computing system of claim 8 ,

wherein the one or more shipment events include at least one of (i) a transit event, (ii) a dwell event or (iii) a dray event.

11 . The computing system of claim 8 ,

wherein the target outcomes include at least one of (i) a net transit time of the cargo shipment, (ii) a net transit cost of the cargo shipment, (iii) a net emissions measure of the cargo shipment.

12 . The computing system of claim 8 , the one or more memories having stored thereon further instructions that, when executed by the one or more processors, cause the computing system to:

train a machine learning model using historical data to predict information related to at least one segment of the cargo shipment.

13 . The computing system of claim 8 , the one or more memories having stored thereon further instructions that, when executed by the one or more processors, cause the computing system to:

dynamically generate a node representing an origin of a trucking segment;

add the dynamically-generated node to the graph;

select one or more candidate nodes; and

dynamically connect the dynamically-generated node to each of the one or more candidate nodes.

14 . The computing system of claim 8 , the one or more memories having stored thereon further instructions that, when executed by the one or more processors, cause the computing system to:

determine the one or more target outcomes based on one or more optimization metrics and/or one or more optimization constraints.

15 . A computer-readable medium having stored thereon a set of instructions that, when executed by one or more processors, cause a computer to:

construct a plurality of location nodes, wherein each of the location nodes corresponds to a respective location type,

wherein each of the location nodes corresponds to a respective real-world location, and

wherein each of the location nodes includes a mode input, a dray input, a mode output, and a dray output;

add the plurality of location nodes to the graph;

receive an origin input parameter, a destination input parameter, and one or more shipment events corresponding to the cargo shipment; and

process, via one or more processors, the shipment events using the graph to determine one or more target outcomes corresponding to the cargo shipment.

16 . The computer-readable medium of claim 15 , the one or more memories having stored thereon instructions that, when executed by the one or more processors, cause a computer to:

select each respective location type from the group consisting of (i) seaport, (ii) railyard and (iii) airport.

17 . The computer-readable medium of claim 15 , the one or more memories having stored thereon instructions that, when executed by the one or more processors, cause a computer to:

wherein the one or more shipment events include at least one of (i) a transit event, (ii) a dwell event or (iii) a dray event.

18 . The computer-readable medium of claim 15 , the one or more memories having stored thereon instructions that, when executed by the one or more processors, cause a computer to:

train a machine learning model using historical data to predict information related to at least one segment of the cargo shipment.

19 . The computer-readable medium of claim 15 , the one or more memories having stored thereon instructions that, when executed by the one or more processors, cause a computer to:

dynamically generate a node representing an origin of a trucking segment;

add the dynamically-generated node to the graph;

select one or more candidate nodes; and

dynamically connect the dynamically-generated node to each of the one or more candidate nodes.

20 . The computer-readable medium of claim 15 , the one or more memories having stored thereon instructions that, when executed by the one or more processors, cause a computer to:

determine the one or more target outcomes based on one or more optimization metrics and/or one or more optimization constraints.