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

Abstract: Dynamic Toll Prediction Using Historical Data on Toll Roads -Case Study of I-95 Interstate Highway:
The proposed research aims to explore the efficacy of machine learning models in predicting dynamic toll prices on the I-95 Interstate Highway, addressing the persistent issue of congestion and travel time uncertainties. Leveraging historical traffic data and advanced predictive modeling techniques, the study seeks to develop accurate models to predict toll prices and travel time differences, ultimately facilitating congestion management and toll optimization. By thoroughly analyzing extensive historical traffic data and employing predictive modeling techniques, the research endeavors to develop highly accurate models capable of forecasting toll prices and travel time differences with precision. Through a systematic approach, incorporating comprehensive data preprocessing, feature engineering, and model evaluation methodologies, this research aims to elucidate the complex relationships underlying toll pricing dynamics. The findings of this study are expected to yield valuable insights into the optimization of transportation systems, with implications for enhancing traffic flow, reducing congestion, and improving the overall efficiency and reliability of travel experiences. Ultimately, this research seeks to contribute to the advancement of traffic management strategies, providing tangible benefits for commuters, transportation authorities, and the broader community alike.
Keywords: Dynamic toll prediction, Machine learning, Traffic management, I-95 Interstate Highway, Congestion, Travel time uncertainties