Traffic condition forecasting using matrix compression and deep neural networks

A computer program product, comprising a non-transitory machine-readable storage medium having encoded therein executable code of one or more software programs, wherein the one or more software programs when executed by at least one processing device perform the following steps:

• obtaining a compressed origination-destination matrix indicating a cost to travel between pairs of a plurality of nodes, wherein said compressed origination-destination matrix is compressed using a locality-aware compression technique that maintains only non-empty data, wherein the compressed origination-destination matrix is created by:
• selecting a point-of-interest;
• creating a distance vector only for said non-empty data;
• and
• applying a space-filling curve technique from the selected point-of-interest to said distance vector;
• obtaining a trained deep neural network trained using said compressed origination-destination matrix and past observations of traffic conditions at various times;
• and
• applying traffic conditions between two nodes in said compressed origination-destination matrix at a time, t, to said trained deep neural network to obtain predicted traffic conditions between said two nodes at a time, t+Δ.

An apparatus, comprising:

• a memory;
• and
• at least one processing device, coupled to the memory, operative to implement the following steps:
• obtaining a compressed origination-destination matrix indicating a cost to travel between pairs of a plurality of nodes, wherein said compressed origination-destination matrix is compressed using a locality-aware compression technique that maintains only non-empty data, wherein the compressed origination-destination matrix is created by:
• selecting a point-of-interest;
• creating a distance vector only for said non-empty data;
• and
• applying a space-filling curve technique from the selected point-of-interest to said distance vector;
• obtaining a trained deep neural network trained using said compressed origination-destination matrix and past observations of traffic conditions at various times;
• and
• applying traffic conditions between two nodes in said compressed origination-destination matrix at a time, t, to said trained deep neural network to obtain predicted traffic conditions between said two nodes at a time, t+Δ.