Understanding pedestrian flow patterns in urban areas could support the decision‐making for infrastructure planning. By incorporating computer vision techniques into surveillance video processing, human walking trajectories in a wide area could be identified by pedestrian re‐identification (ReID) across multiple cameras. Recent ReID methods mostly use convolutional neural networks equipped with deep learning techniques to extract discriminative human features from images for identity matching. However, they still suffer from realistic challenges such as occlusion and appearance variation. This paper develops a ReID‐based framework for pedestrian trajectory recognition across multiple cameras. Specifically, a generic approach of explainable model design is presented, which intuitively analyzes existing baseline models based on feature visualization. Hence, a new model named OSNet + BDB is developed that extracts discriminative‐and‐distributed features. Additionally, an incremental feature aggregation strategy is designed for more robust identity matching. Our ReID method notably outperforms its baselines by 4% identification F1 accuracy in public benchmarks. Practically, pedestrian flow statistics in a real building are extracted for behavioral modeling. Simulations of several what‐if layouts are then conducted for facility performance evaluation.