A system for selecting delivery mechanisms sends a request to a plurality of servers associated with one or more autonomous delivery mechanism and one or more non-autonomous delivery mechanisms to provide delivery metadata. The request comprises a pickup location coordinate and a delivery location coordinate. The delivery metadata comprises a delivery time and a delivery quote. The system receives a first set of delivery metadata associated with one or more autonomous delivery mechanisms, and a second set of delivery metadata associated with one or more non-autonomous delivery mechanisms. The system identifies a particular autonomous delivery mechanism from within the category of autonomous delivery mechanisms based on the first set of delivery metadata. The system identifies a particular non-autonomous delivery mechanism from within the category of non-autonomous delivery mechanisms based on the second set of delivery metadata.

A system for selecting delivery mechanisms sends a request to a plurality of servers associated with one or more autonomous delivery mechanism and one or more non-autonomous delivery mechanisms to provide delivery metadata. The request comprises a pickup location coordinate and a delivery location coordinate. The delivery metadata comprises a delivery time and a delivery quote. The system receives a first set of delivery metadata associated with one or more autonomous delivery mechanisms, and a second set of delivery metadata associated with one or more non-autonomous delivery mechanisms. The system identifies a particular autonomous delivery mechanism from within the category of autonomous delivery mechanisms based on the first set of delivery metadata. The system identifies a particular non-autonomous delivery mechanism from within the category of non-autonomous delivery mechanisms based on the second set of delivery metadata.

A system receives content of a memory resource. The system compares the content of the memory resource with a first resource data associated with a first physical space, and with a second resource data associated with a second physical space. The system determines which of the first physical space and the second physical space can fulfill more than a threshold percentage of objects in the memory resource based on the comparison between the content of the memory resource with the first and second resource data. The system determines that the first physical space can fulfill more than the threshold percentage of objects in the memory resource. The system assigns the first physical space to the memory resource for concluding an operation associated with the memory resource.

In one implementation, a method comprises: receiving, on a computing device, a search term for a search engine; and mapping to a distributed ledger based at least on the search term, wherein the distributed ledger is stored on one or more servers coupled to the computing device over one or more computer networks, and wherein the distributed ledger corresponds to augmented data associated with the search term.

A method for prediction model determination can include: determining a set of models, training each model, determining package transit data, evaluating the set of models, selecting a model from the set of models, predicting package transit data using the selected model, and/or any other suitable element.

A system for initiating object transfer, the system comprising a computing device configured to receive a plurality of object transfer requests, generate, using the plurality of object transfer requests, a transfer apparatus interaction platform for a plurality of transfer apparatuses to proffer at least an object transfer request, determine transfer apparatus interaction data via the transfer apparatus interaction platform, wherein transfer apparatus interaction data comprises a proffer of an object transfer request, a proposed window of time for object transfer, and an object transfer compatibility metric, select the proffer from a plurality of proffers from the plurality of transfer apparatuses, wherein selecting comprises calculating a difference between the plurality of object transfer compatibility metrics associated with the plurality of proffers using a pairwise operation, and provide the transfer apparatus corresponding to the selected proffer the object transfer request.

A system for initiating object transfer, the system comprising a computing device configured to receive a plurality of object transfer requests, generate, using the plurality of object transfer requests, a transfer apparatus interaction platform for a plurality of transfer apparatuses to proffer at least an object transfer request, determine transfer apparatus interaction data via the transfer apparatus interaction platform, wherein transfer apparatus interaction data comprises a proffer of an object transfer request, a proposed window of time for object transfer, and an object transfer compatibility metric, select the proffer from a plurality of proffers from the plurality of transfer apparatuses, wherein selecting comprises calculating a difference between the plurality of object transfer compatibility metrics associated with the plurality of proffers using a pairwise operation, and provide the transfer apparatus corresponding to the selected proffer the object transfer request.

A method includes receiving a first set of identifiers selected based on commonality among descriptive data corresponding to the identifiers of the first set. Each identifier corresponds to a user who has been supplied a physical object. The method includes identifying event data for the first set within a specified epoch. The method includes training a machine learning model for the first set using the identified event data. The machine learning model is trained using parallel processing of records from a storage structure storing the event data. The parallel processing includes assigning analysis of the event data of each of a subset of the first set to respective processor threads for parallel execution on processing hardware. The trained machine learning model is configured to receive a selected identifier and generate an output representing an amount of resources expected to be used by the selected identifier for a subsequent epoch.

A method and a system dynamically adapt a passenger transport capacity of a transport line to the number of passengers determined for the transport line. The system contains a main evaluator configured for automatically determining, as a function of the time, the number of passengers for the transport line, and a processor configured for acquiring the number of passengers as a function of the time, a nominal timetable, and a nominal passenger transport capacity of each vehicle of the line. The processor applies a trained function to the number of passengers, and the trained function has been trained by a machine learning algorithm for predicting a future temporal evolution of the number of passengers. The processor is configured for determining a measure for adapting the transport capacity of the line to the future temporal evolution. The system is further configured for applying the measure to the transport line.

Systems and methods for determining an estimated time of arrival (ETA) and/or an on-time probability (OTP) metric are provided. For example, a request for an estimated time of arrival for a first load is requested. The request may include or reference scheduled delivery data. The scheduled delivery data may include information about the load and the driver and/or equipment scheduled to deliver the load. For example, driver hours of service information for the scheduled driver may be accessed. In addition, external data may be accessed, such as traffic and weather data. A trained machine-learning ETA model may be used to provide an ETA based on the load data, the external data, and information about the scheduled driver. In addition, a trained machine-learning OTP model may be provided to estimate a probability, based on the received information, of the load being delivered within a delivery window.