Dynamic toll pricing has become a very important tool for managing traffic conditions on the road, improving the lane usage by using lanes to the maximum capacity and solving traffic congestion problems. Investigating the effectiveness of machine learning models in forecasting dynamic toll costs on the I-95 Interstate Highway will help to solve the ongoing congestion and travel time uncertainty problems. By using historical traffic data and sophisticated predictive modeling approaches, the project aims to create realistic models to forecast toll prices and travel time variations, so enabling congestion control and toll optimization. By reviewing the advancement of the methodologies used till now based on the recent publications like reinforcement learning hybrid models and optimization frameworks, this research tries to identify the main strengths, limitations, and future opportunities in this research field. The findings shown in this report show the importance of using real-time data on the road and simplifying the computational requirements for the practical deployment of dynamic tolling systems in order to solve the real-life traffic problems.


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    Titel :

    Dynamic Toll Prediction Using Historical Data on Toll Roads: Case Study of I-95 Interstate Highway


    Beteiligte:

    Kongress:

    International Conference on Transportation and Development 2025 ; 2025 ; Glendale, Arizona



    Erscheinungsdatum :

    05.06.2025




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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