<ul><li>A modular neural network (NN)-based framework has been proposed to estimate operating mode distributions of light-duty vehicles without relying on predefined driving cycles.</li><li>The method utilizes macroscopic variables such as speed, flow, and link infrastructure attributes to estimate operating modes like braking, idling, and cruising.</li><li>The proposed framework outperforms the Motor Vehicle Emission Simulator (MOVES) in calculating the operating mode distribution, achieving a closer match to actual operating mode distribution derived from trajectory data.</li><li>The average error in emission estimation across pollutants is 8.57% for the proposed method, lower than the 32.86% error for MOVES. CO2 estimation has an error of just 4% compared to 35% for MOVES.</li></ul>

Estimating City-wide operating mode Distribution of Light-Duty Vehicles: A Neural Network-based Approach

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