Predictive Framework for Smart Toll Pricing Using Demand Analytics and Intelligent Transportation Data

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@article{1717767,
      author = {Nitin Rajgor, Dr. M N Nachappa},
      title = {Predictive Framework for Smart Toll Pricing Using Demand Analytics and Intelligent Transportation Data},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {11},
      pages = {1834-1846},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717767.pdf},
      abstract = {Traffic jam has become the order of the day in most urban centres and highways. The classic toll pricing mechanisms have fixed price usually that does not change depending on the traffic situation. Due to this, they can hardly be used in handling congestion during the high traffic hours and end up increasing travel time and limited use of the roads. There are works that have attempted this issue with the help of optimization models and control-based pricing in order to adapt toll prices. Other studies have predicted the level of congestion using machine learning and data of traffic. To a certain extent, these approaches have been improved, although they are largely investigated independently. Real time demand prediction is not regularly used in Toll pricing models whereas prediction models are not applied to make decisions on pricing. There is still a gap. There is no single system that integrates the issue of demand in traffic with real-time dynamic toll pricing. This contributes to the fact that congestion is hard to control in the new transportation systems. This paper suggests a framework that integrates the demand analytics and the dynamic toll pricing. It relies on the traffic information like the number of vehicles, their speed and the number of vehicles on the highway to forecast demand and increase or decrease the price of the tolls. This is possible to improve the traffic flow and minimize congestion. Through this practice, the transportation systems will be able to become more effective and competitive. It can assist road operators as well as the people in charge since it will make traffic control smarter and effective.},
      keywords = {Smart Toll Pricing, Dynamic Tolling, Demand Analytics},
      month = {May},
      doi = {https://doi.org/10.64388/IREV9I11-1717767}
  }