Applying Dynamic Modeling Methods to IH 35 Through Austin: Exploring Options for Addressing Future Congestion

Traffic on highways has increased significantly in the past few years. Consequently, this has caused delays for the drivers in reaching their final destination and increased the highway’s congestion level. Many options have been proposed to ease these issues. In this paper, a model of the highway drivers’ population was built based on several factors, including the behavioral patterns of the drivers, like drivers’ time flexibility to reach the destination, their carpool eligibility, and their tolerance to pay the toll price, in addition to the traffic information from the system. A fuzzy logic decision-making model is presented to emulate how drivers would choose the lane to use based on the aforementioned factors and the current congestion levels of all the lanes on the highway. The presented model, along with the simulation results from applying the model to different simulation scenarios, show the usefulness of such a model in predicting an optimal toll value. Such optimal value would reduce congestion on the highway at one end while maximizing the revenue for the toll company.

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Applying Dynamic Modeling Methods to IH 35 Through Austin: Exploring Options for Addressing Future Congestion

Cited by 2 publications

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Modeling route choice criteria from home to major streets: A discrete choice approach

Modeling route choice criteria from home to major streets: A discrete choice approach

Probabilistic modeling and simulation of drivers’ lane selection and its impact on highway traffic using fuzzy-based decision-making

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