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The connected and automated vehicle (CAV) technology in recent years has demonstrated its potential in improving efficiency in transportation systems. Prediction, as a key component of the technology, enables smart vehicles to anticipate future movements of traffic agents and potential future risks, so as to plan in advance by incorporating these predictions in their trajectory planning. In this dissertation I propose a prediction-based framework to identify risky scenarios at urban intersections, develop strategies to mitigate them, and conduct prediction-based vehicle trajectory planning in a connected environment. The framework consists of three main components: (1) real-time risky driving prediction; (2) traffic agent trajectory prediction; (3) prediction-based vehicle trajectory planning. For risky driving prediction, I propose an unsupervised learning framework to predict risky driving at urban intersections in a connected vehicle environment. The proposed framework uses time series k-means to categorize multi-dimensional time series trajectories into several context-aware driving patterns. I train an anomaly detection model on the trajectory dataset to identify anomalous trajectories, and apply this model to clusters of driving patterns to provide Risky Driving Prediction(RDP) scores for each driving pattern. I provide a real-time online assessment approach to predict the risk score of driving trajectories that travel toward a signalized intersection. For pedestrian trajectory prediction, I evaluate the model using a benchmark dataset and a proprietary dataset collected at urban intersections.I compare the performance of step attention with three existing …