<p>In an urban traffic environment without traffic lights, pedestrians’ proactivity significantly influences their crossing decision-making. Comprehensively understanding this proactive behavior and accurately predicting their crossing trajectories are crucial for intelligent vehicle motion planning. Therefore, this paper proposes a pedestrian crossing trajectory prediction method based on a personalized proactive perceived risk model. The personalized proactive perceived risk model, by introducing risk field theory, integrates multiple perceived risk influencing factors, including pedestrian crossing style, surrounding vehicles, and the degree of deviation from the crossing destinations. It effectively quantifies a pedestrian’s personalized proactive risk perception ability. On this basis, a risk-resistance network is established to predict pedestrian crossing trajectories. It maps the personalized proactive perceived risk of pedestrians in physical space to the branch resistance of the network in virtual space and solves for the proactive optimal crossing path and speed for pedestrians with the minimum risk. By fully leveraging the unique advantages of the risk-resistance network, this process successfully achieves a fine-grained simulation of pedestrians making crossing decisions based on personalized proactive perceived risk. The proposed method is validated on the publicly available dataset using Average Displacement Error and Final Displacement Error. The results demonstrate that compared to some baselines, this method improves the accuracy of predicting pedestrian crossing trajectories and enhances the interpretability of the model.</p>