The partition of a long urban arterial is necessary for efficient and reliable traffic signal coordination. Previous studies have utilized fixed detector data or aggregated vehicle trajectory data to measure the correlation between adjacent intersections for an arterial partition. However, the appropriate partition of a long urban arterial remains a challenge because of the difficulty in quantifying the correlation between any pair of intersections instead of two adjacent intersections. The availability of spatiotemporal continuous individual vehicle trajectory data provides opportunities for a more accurate dynamic urban arterial partition by constructing a correlation matrix between intersections. In this article, a novel three-step framework is proposed for a dynamic urban arterial partition using the data collected by automatic license plate recognition devices, which are widely installed in China. First, the arterial traffic origin-destination (OD) flow between any pair of intersections along an arterial is extracted, and a similarity model is proposed to identify the spatiotemporal pattern of the arterial traffic OD flow matrixes. Second, a community aggregation-based algorithm is developed to produce candidate arterial partition solutions using the distributed characteristics of the arterial traffic OD flow matrixes. Third, two new metrics are designed to determine the optimal arterial partitions considering the splits of vehicle trajectories and their coordination utilization in partitioned intersection groups. The proposed framework is implemented on an actual arterial in Kunshan, China. The comparison to conventional adjacent correlation methods indicates that the OD flow-based partition method can generate better signal coordination control performance, in terms of average delay, average speed, and so on.