Method:

• Data Collection: Receiving a collection of trip data, which includes both location data and timestamp data for multiple trips on a specific route
• Determining Travel Times: Travel times, the time taken by the vehicle to traverse each segment of the route, are determined for each trip based on the collected data.
• Creating Travel Patterns: Creates travel patterns for each route based on the travel times collected over a predefined time interval that include information about temporal variation and spatial variation
• Prediction: Predicting the vehicle travel time during next instance when the vehicle travels in the same route using the travel pattern
• Temporal variation –Based on how travel times change based on the time of day or day of the week
• Spatial variation –Based on how travel times vary between different segments of the route, possibly due to traffic or other factors
• The travel pattern for each route is determined based on input features obtained using visualization method and statistical test
• The said visualization method comprises heat maps and the statistical test comprises at least one of K-means clustering and Davies-Bouldin (DB) score
• The invention also discloses a system with  components for data collection, determination of travel times, creation of travel patterns, and prediction having a unit for generating a travel time predicting model.
• The travel pattern for each route is determined based on similarity analysis and correlation analysis of the different travel times.