Abstract: A computer-implemented method includes constructing a plurality of nodes; adding the nodes to a graph; receiving origin, destination and shipment parameters; and processing the parameters to determine target outcomes corresponding to a cargo shipment. A computing system includes a processor; and a memory having stored thereon computer-executable instructions that, when executed by the processor, cause the computing system to: construct a plurality of nodes; add the nodes to a graph; receive origin, destination and shipment parameters; and process the parameters to determine target outcomes corresponding to a cargo shipment. A computer-readable medium includes instructions that, when executed by a processor, cause a computer to: constructing a plurality of nodes construct a plurality of nodes; add the nodes to a graph; receive origin, destination and shipment parameters; and process the parameters to determine target outcomes corresponding to a cargo shipment.

Abstract: The present disclosure provides systems and methods that impute missing shipment milestones using vehicle tracking data (e.g., global positioning system (GPS data, automatic identification system (AIS) data, and/or the like). In particular, the present disclosure provides improved techniques to impute when a shipping vehicle (and the cargo loaded thereon) has arrived at a transportation location (e.g., port). In some implementations, the milestone imputation process first includes collecting a historical sample of the vehicle tracking data. Next, density-based clustering methods can be applied to identify vessel stops. This historical set of vessel stops can then be further clustered in order to identify individual docking or loading/unloading locations for all the transportation locations around the world.

Abstract: The present disclosure provides systems and methods that impute missing shipment milestones using vehicle tracking data (e.g., global positioning system (GPS data, automatic identification system (AIS) data, and/or the like). In particular, the present disclosure provides improved techniques to impute when a shipping vehicle (and the cargo loaded thereon) has arrived at a transportation location (e.g., port). In some implementations, the milestone imputation process first includes collecting a historical sample of the vehicle tracking data. Next, density-based clustering methods can be applied to identify vessel stops. This historical set of vessel stops can then be further clustered in order to identify individual docking or loading/unloading locations for all the transportation locations around the world.

Abstract: Systems and methods for analyzing statistics among geographic regions to assess shipping information are disclosed herein. An example method includes receiving an origin and a destination from an electronic device associated with a user, and determining an origin tile representative of a first geographic area that includes the origin and a destination tile representative of a second geographic area that includes the destination. The example method further includes automatically determining a first region surrounding the origin tile and a second region surrounding the destination tile. The example method further includes aggregating shipping information corresponding to shipments delivered between the first region and the second region, and calculating one or more shipping metrics based on the shipping information. The example method further includes rendering, on a user interface, the one or more shipping metrics for access by the electronic device.

Abstract: The present disclosure provides systems and methods that impute planned transshipment locations for an itinerary associated with an item of cargo. In particular, according to one aspect of the present disclosure, a supply chain management computing system can obtain itinerary data that describes a planned shipment of an item of cargo from an origin location to a destination location. For example, the itinerary data can identify at least a shipping vehicle planned to transport the item of cargo. The supply chain management computing system can access vehicle location data associated with at least the shipping vehicle and can predict, based at least in part on the itinerary data and the vehicle location data, a transshipment location at which the item of cargo is transferred from the shipping vehicle to a different shipping vehicle.

Abstract: The present disclosure provides systems and methods that impute planned transshipment locations for an itinerary associated with an item of cargo. In particular, according to one aspect of the present disclosure, a supply chain management computing system can obtain itinerary data that describes a planned shipment of an item of cargo from an origin location to a destination location. For example, the itinerary data can identify at least a shipping vehicle planned to transport the item of cargo. The supply chain management computing system can access vehicle location data associated with at least the shipping vehicle and can predict, based at least in part on the itinerary data and the vehicle location data, a transshipment location at which the item of cargo is transferred from the shipping vehicle to a different shipping vehicle.

Abstract: The present disclosure provides systems and methods that impute missing shipment milestones using vehicle tracking data (e.g., global positioning system (GPS data, automatic identification system (AIS) data, and/or the like). In particular, the present disclosure provides improved techniques to impute when a shipping vehicle (and the cargo loaded thereon) has arrived at a transportation location (e.g., port). In some implementations, the milestone imputation process first includes collecting a historical sample of the vehicle tracking data. Next, density-based clustering methods can be applied to identify vessel stops. This historical set of vessel stops can then be further clustered in order to identify individual docking or loading/unloading locations for all the transportation locations around the world.

Abstract: The present disclosure provides systems and methods that impute missing shipment milestones using vehicle tracking data (e.g., global positioning system (GPS data, automatic identification system (AIS) data, and/or the like). In particular, the present disclosure provides improved techniques to impute when a shipping vehicle (and the cargo loaded thereon) has arrived at a transportation location (e.g., port). In some implementations, the milestone imputation process first includes collecting a historical sample of the vehicle tracking data. Next, density-based clustering methods can be applied to identify vessel stops. This historical set of vessel stops can then be further clustered in order to identify individual docking or loading/unloading locations for all the transportation locations around the world.

Abstract: The present disclosure provides systems and methods that impute missing shipment milestones using vehicle tracking data (e.g., global positioning system (GPS data, automatic identification system (AIS) data, and/or the like). In particular, the present disclosure provides improved techniques to impute when a shipping vehicle (and the cargo loaded thereon) has arrived at a transportation location (e.g., port). In some implementations, the milestone imputation process first includes collecting a historical sample of the vehicle tracking data. Next, density-based clustering methods can be applied to identify vessel stops. This historical set of vessel stops can then be further clustered in order to identify individual docking or loading/unloading locations for all the transportation locations around the world.

Abstract: The present disclosure provides systems and methods that impute planned transshipment locations for an itinerary associated with an item of cargo. In particular, according to one aspect of the present disclosure, a supply chain management computing system can obtain itinerary data that describes a planned shipment of an item of cargo from an origin location to a destination location. For example, the itinerary data can identify at least a shipping vehicle planned to transport the item of cargo. The supply chain management computing system can access vehicle location data associated with at least the shipping vehicle and can predict, based at least in part on the itinerary data and the vehicle location data, a transshipment location at which the item of cargo is transferred from the shipping vehicle to a different shipping vehicle.