Method, apparatus, and system for end-to-end traffic estimation from minimally processed input data

An apparatus comprising:

• at least one processor;
• and
• at least one memory including computer program code for one or more programs,
• the at least one memory and the computer program code configured to, within the at least one processor, cause the apparatus to perform at least the following,
• retrieve probe data collected from one or more sensors of one or more devices traveling in a geographic area;
• aggregate the probe data into an input sequence of one or more frames, wherein each frame of the sequence comprises a plurality of spatial cells representing a geographic area at a respective time interval, and wherein the probe data is spatially and temporally binned into the plurality of spatial cells;
• process the input sequence using a machine learning model to directly determine a predicted traffic state;
• and
• provide the predicted traffic state as an output.

A non-transitory computer-readable storage medium carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to perform:

• retrieving sensor data collected from one or more sensors of one or more devices traveling in a geographic area;
• initiating a pre-processing pipeline to associate the sensor data with one or more map features of a geographic database;
• determining a ground-truth traffic state based on the associated sensor data;
• training a machine learning model using the ground-truth traffic state to determine a predicted traffic state directly from input sensor data without using the pre-processing pipeline to process the input sensor data;
• and
• providing the trained machine learning model as an output.