Abstract
In light of the development of intelligent vehicles and the persistent energy crisis, improving the predictive accuracy of macroscopic traffic flow and formulating energy-efficient road network design solutions are essential to promote sustainable development in road transportation. This study introduces a dual-objective impedance function that incorporates considerations of both travel time and energy consumption. The function is developed utilizing the principles of intelligent vehicle energy flow and traffic flow parameters. Then, an enhanced user equilibrium traffic assignment (E-UE) model is formulated. The effectiveness of the E-UE function was validated through road experimental data. The performance of E-UE was analyzed using a planned road network consisting of 28 sections. In comparison with the classical UE model, the E-UE model promotes uniform traffic distribution and alleviates congestion, leading to a 1.1 % reduction in average road energy consumption. This highlights the E-UE model’s effectiveness in accurately capturing the impacts on energy consumption with enhanced predictive precision. Strategies to mitigate potential energy consumption include implementing the E-UE model with realistic weights, adjusting weights for specific roads, employing a design speed of 50 km/h, and utilizing 6-lane roads. This study contributes to the advancement of energy-saving technologies in sustainable transportation.
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