Abstract: To explore the potential traffic characteristics of large-scale and complex networks, it is insufficient to capture the spatial characteristics of traffic flows only considering the total traffic volume. It is also necessary to consider the intensity distribution of traffic flows in different origin-destination (OD) directions. To this end, the paper proposes a network partitioning method with OD directions of traffic sub-regions, to better capture the intensity distribution of traffic flows in different OD directions. Firstly, with the objectives of minimizing the variance of vehicle speed and regional compactness in each region, the original network is partitioned into multiple initial regions. On this basis, with the objectives of minimizing the vehicle speed variance within regions, maximizing network compactness, and minimizing the difference of region scales, a mathematical optimization model is established to merge the initial regions. The Technique for Order Preference by Similarity to an Ideal Solution is used to assess these three objectives, and the optimal partition result is obtained using the genetic algorithm. Vehicle GPS data is processed to obtain vehicle trajectories. Based on these trajectory data, a method is proposed to calculate the speed variance in each region, which involves storing the speed of vehicle ODs and link IDs. Finally, the effectiveness of the proposed partitioning method is verified using bus GPS data in Shenzhen. Results show that, compared with traditional methods, the proposed method achieves more compact regions with even sizes, improving partitioning efficiency. Moreover, by constructing a macroscopic fundamental diagram (MFD) with OD directions of traffic sub-regions, the spatial evolution characteristics of traffic flows in the network are better captured.