A Survey On Machine Learning And Deep Learning Models For Predicting Traffic Transit Time

• Author(s):

  Vaishnavi Sambare | Prof. Pradeep Pal

• Keywords:

  Machine Learning, Intelligent Traffic Systems (ITS), Statistical Models, Regression, Forecasting Accuracy

• Abstract:

  The Transition Towards Intelligent Traffic Systems (ITS) Is Inevitable In Future. Accurate Travel Time Estimation Is Vital For The Effectiveness Of Modern Public Transportation Systems. It Plays A Central Role In Applications Such As Real-time Passenger Information, Transit Planning, And Traffic Management. With Increasing Urbanization And Demand For Efficient Mobility, Transit Agencies Are Turning To Data-driven Models To Improve Service Reliability. One Valuable Data Source Is The General Transit Feed Specification (GTFS), Which Standardizes Public Transportation Schedules And Associated Geographic Information. When Integrated With Statistical Modeling Techniques, GTFS Features Can Significantly Enhance The Precision Of Street-level Travel Time Estimations. This Paper Presents A Comprehensive Review Of Statistical Models For Forecasting Street Level Travel Time Employing GTFS Features, Along With Associated Challenges That The Sector Faces.

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Other Details

• Paper id:

  IJSARTV12I7105766

• Published in:

  Volume: 12 Issue: 7 July 2026

• Publication Date:

  2026-07-14

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