A computer-implemented method for machine-learning-based traffic prediction, comprising: receiving , with at least one processor, historic transaction data comprising a plurality of transactions by at least one consumer; generating , with at least one processor and using a machine-learning classification model, based at least partially on the historic transaction data, a transportation categorization for the at least one consumer representative of a mode of transportation to be taken by the at least one consumer when the at least one consumer travels to complete a transaction at a point-of-sale terminal; receiving , with at least one processor, at least one message associated with at least one transaction between the at least one consumer and at least one point-of-sale terminal in a region; identifying , with at least one processor, at least one geographic node of activity in the region comprising the at least one point-of-sale terminal; generating , with at least one processor and based at least partially on the at least one message and the transportation categorization of the at least one consumer, an estimate of traffic intensity for the at least one geographic node of activity, the estimate of traffic intensity representative of a predicted volume of traffic for a mode of transportation; comparing , with at least one processor, the estimate of traffic intensity for the at least one geographic node of activity to a threshold of traffic intensity for the at least one geographic node of activity; and, in response to determining that the estimate of traffic intensity for the at least one geographic node of activity satisfies the threshold of traffic intensity, generating , with at least one processor, a communication to at least one navigation device configured to cause the at least one navigation device to modify a navigation route; and communicating , with at least one processor, the communication to the at least one navigation device. receiving , with at least one processor, historic transaction data comprising a plurality of transactions by at least one consumer; generating , with at least one processor and using a machine-learning classification model, based at least partially on the historic transaction data, a transportation categorization for the at least one consumer representative of a mode of transportation to be taken by the at least one consumer when the at least one consumer travels to complete a transaction at a point-of-sale terminal; receiving , with at least one processor, at least one message associated with at least one transaction between the at least one consumer and at least one point-of-sale terminal in a region; identifying , with at least one processor, at least one geographic node of activity in the region comprising the at least one point-of-sale terminal; generating , with at least one processor and based at least partially on the at least one message and the transportation categorization of the at least one consumer, an estimate of traffic intensity for the at least one geographic node of activity, the estimate of traffic intensity representative of a predicted volume of traffic for a mode of transportation; comparing , with at least one processor, the estimate of traffic intensity for the at least one geographic node of activity to a threshold of traffic intensity for the at least one geographic node of activity; and, in response to determining that the estimate of traffic intensity for the at least one geographic node of activity satisfies the threshold of traffic intensity, generating , with at least one processor, a communication to at least one navigation device configured to cause the at least one navigation device to modify a navigation route; and communicating , with at least one processor, the communication to the at least one navigation device. <EOS>