Traffic pattern recognition for accurate travel time prediction

A method for generating accurate pixel-specific travel time, the method comprising:

• obtaining historical travel-related data associated with a plurality of vehicles traveling within a geographic region, the historical travel-related data comprising location data and speed data associated with each pixel in a plurality of pixels at a month, day, and hour level, wherein each pixel in the plurality of pixels represents a unique sub-region division of the geographic region;
• generating a speed value at a weekday-hour-pixel (WHP) level for each pixel in the plurality of pixels, by a trained machine learning (ML) model using the historical travel-related data, wherein the ML model is trained to generate a predicted speed of travel through the plurality of pixels at the month, day, and hour level;
• creating a speed values table comprising a plurality of pixel-timeslot speed values for a plurality of future dates, a pixel-timeslot speed value in the plurality of pixel-timeslot speed values comprising a predicted speed of travel along a node through a selected pixel at a future date and within a selected hour of the future date;
• and
• storing the speed values table in a data storage device, wherein speed values in the speed values table are utilized by a mapping application to predict speeds to traverse a plurality of nodes between a point of departure and a destination at WHP level.

One or more computer storage devices having computer-executable instructions stored thereon, which, upon execution by a computer, cause the computer to perform operations comprising:

• receiving historical travel-related data associated with a plurality of vehicles traveling within a geographic region, the historical travel-related data comprising location data and speed data associated with each pixel in a plurality of pixels at a month, day, and hour level, each pixel representing a unique sub-region division of the geographic region;
• generating a speed value at a weekday-hour-pixel (WHP) level for each pixel in the plurality of pixels, by a trained machine learning (ML) model using the historical travel-related data, wherein the ML model is trained to generate a predicted speed of travel through the plurality of pixels at the month, day, and hour level;
• creating a speed values table comprising a plurality of pixel-timeslot speed values for a plurality of future dates, a pixel-timeslot speed value in the plurality of pixel-timeslot speed values comprising a predicted speed of travel along a node through a selected pixel at a future date and within a selected hour of the future date;
• and
• storing the speed values table in a data storage device, wherein speed values in the speed values table are utilized to predict speeds to traverse a plurality of nodes between a point of departure and a destination at WHP level.