The physics of shockwaves is a fundamental traffic characteristic that is useful for microscopic traffic flow modeling. Thanks to connected vehicle technologies, it is possible to capture shockwaves by collecting the information of multiple downstream vehicles. Numerous classical physics-based models have utilized the physics of shockwaves to predict vehicle trajectory dynamics, yet their predictability is often limited due to the volatile and complex nature of highway traffic composed of human-driven vehicles. Recent learning-based trajectory prediction models utilize historical trajectories of surrounding vehicles to improve predictability. However, those learning-based models are purely data-driven, thus lacking interpretability and physical insights, or even missing opportunities for further improving model predictability. To leverage the advantages of both learning-based and physics-based models, this study proposes a physics-aware learning-based model for a trajectory prediction of congested traffic in a connected vehicle environment. A newly collected highway trajectory dataset is adopted for training and validation. Experiment results show that the proposed hybrid model yields better predictability, compared with the learning-based models (e.g., long short-term memory neural networks and convolutional neural networks), with an 8.7% predictability reduction of position errors and a 6.5% reduction of speed errors, which further verify the positive impacts of adopting physics of shockwaves in hybrid learning models. Moreover, result analysis shows that predictability improves as the market penetration rate increases, and the proposed hybrid model is better than the learning-based models with 3-10% improvements in predictability.