 The paper presents a minimum volume-oriented bounding box, OBB, strategy to improve the computational efficiency of urban cellular automata, CA, models by reducing the number of unavailable cells in the axis-aligned bounding box, AABB, of study areas. The OBB strategy involves geometric transformation and a set of functions to describe spatial coordinate relationships between AABB and OBB layers. Experiments show that the OBB strategy can significantly reduce computational time, resulting in up to 25-fold speed-up when vectorization, parallel computing, and OBB strategy are combined. The paper argues that the OBB strategy makes the integration of vectorization and parallel computing more efficient and scalable, improving the applicability of urban CA models. This article was authored by Changsha, Binjong, Haijun Wang, and others.