Abstract: A machine learning-based proxy model may generate measurements of quality of user experience in a vehicle-based communication network in the absence of direct feedback from the users regarding the user experience. Once generated, the proxy model may be applied to observed operational parameters of the on-board network to quantify the user experience for any user in any given instance. User experience measurements (e.g., trends identified therein) may be utilized to identify and implement adjustments to hardware, firmware, software, and/or service procedures associated with implementation of the on-board network. These adjustments may be implemented between transits of the vehicle, or in some cases, during transit of the vehicle to improve the user experience over the duration of use of the vehicle-based communication network.

Abstract: A machine learning-based proxy model may be generated to determine quality of user experience in a vehicle-based communication network in the absence of direct feedback from the users regarding the user experience. The machine learning-based proxy model may include supervised and/or unsupervised machine learning models, including for example a k-means clustering algorithm and/or an artificial neural network. Once generated, the proxy model may be applied to observed operational parameters of the vehicle-based communication network to quantify the user experience for any user for any duration of the vehicle-based communication network.