Static Stochastic Model-Based Prediction of City Bus Velocity
International Conference on Smart Systems and Technologies 2022 (SST 2022), Osijek, Croatia2022This paper proposes a static, stochastic, deep feed forward neural network-based model for prediction of city bus velocity along a regular route. The emphasis is on a proper formation of model outputs to consistently learn the conditional probability distribution of vehicle velocity based on the vehicle position as only input feature. First, a rich set of recorded driving cycles of a representative fleet of ten city buses is statistically analyzed. Next, the recorded dataset is properly downsampled and used for computationally-efficient training and validation of the neural network. Finally, the prediction accuracy is demonstrated on a test dataset by considering different prediction quality indices. driving cycle, velocity, prediction, stochastic model, neural network, road vehicles
Cited by 2
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Deep-Learning-Based Prediction Algorithm for Fuel-Cell Electric Vehicle Energy With Shift Mixup🔗
IEEE Sensors Journal, 2024
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International Conference on Smart Systems and Technologies 2022 (SST 2022), Osijek, Croatia
2022
Cited by 2
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[1] Deep-Learning-Based Prediction Algorithm for Fuel-Cell Electric Vehicle Energy With Shift Mixup🔗IEEE Sensors Journal, 2024