Apparatus and methods for transport optimization

An apparatus for transport optimization, the apparatus comprising:

• at least a processor;
• and
• a memory communicatively connected to the at least a processor, the memory comprising instructions configuring the at least a processor to:
• receive transport data related to a transport, the transport data comprising vehicle data comprising fuel usage data and cargo data for the transport, the cargo data comprising information related to an unloading or loading of objects onto a vehicle, dimensions, a weight, and a quantity of the transport, wherein the cargo data further comprises data related to one or more of a fragility measurement, perishability status, or temperature requirement, wherein the transport data further comprises a gradient of a surface the vehicle travels along during the transport, wherein receiving the transport data further comprises calculating a carbon emission metric using an emission machine-learning model as function of the transport data related to the unloading or loading of objects onto the vehicle;
• compare the transport data to one or more transport plan parameters of a current transport plan of the transport, wherein the one or more transport plan parameters comprises a transport component size, and wherein the one or more transport plan parameters comprises a plan threshold providing a range of acceptable values of the transport data and the fuel usage data, wherein the plan threshold:
• assigns a weight to each of the one or more transport plan parameters, each weight reflecting a relative importance of each transport plan parameter;
• determines a combination of the transport data that maximizes a score of an objective function based on a constraint assigned to a pair of the transport data and the one or more transport plan parameters;
• and
• caps a carbon emission of the transport by a predetermined value;
• and
• determine, using an optimization machine-learning model, a pathway deviation as a function of at least the dimensions of the transport data and at least the transport component size of the one or more transport plan parameters, wherein the pathway deviation comprises a handoff of at least a portion of the transport from a first vehicle to a second vehicle, and wherein the at least a processor is configured to instruct an operator of the second vehicle to update the current transport plan based on the pathway deviation, wherein determining the pathway deviation comprises:
• receiving a training data set, wherein the training data set comprises outputs correlated with inputs, wherein the inputs comprise a plurality of transport data inputs and transport plan parameter inputs, and the outputs comprise a plurality of pathway deviations;
• filtering the training data set using a training data classifier, wherein the training data classifier identifies one or more subsets of the training data and labels the one or more subsets based on a distance metric;
• and
• generating the optimization machine-learning model as a function of the filtered training data set, wherein the optimization machine-learning model determines the pathway deviation as a function of the transport data and the one or more transport plan parameters.