Abstract: Examples provide a traffic pattern recognition for accurate pixel-level travel time prediction. Historical travel-related data is obtained from vehicles traveling within various geographic regions that includes location data and speed data associated with each pixel in a plurality of pixels at a month, day, and hour level. A trained machine learning (ML) model predicts the speed of travel through each pixel at a weekday-hour-pixel (WHP) level. The ML model is trained using the historical travel-related data enabling the ML model to make accurate predictions of future travel times at the WHP level. The predicted speeds of travel are used to create a table of speed values for multiple pixel-timeslots associated with future dates. Each pixel-timeslot includes a predicted speed value for vehicles traveling through segments of each node on a given future data during a specific hour of the day enabling more accurate estimations of arrival time.