The smart city vision raises the prospect that cities will become more intelligent in various fields, such as more sustainable environment and a better quality of life for residents. As a key component of smart cities, intelligent transportation system highlights the importance of vehicle re-identification (Re-ID). However, as compared to the rapid progress on person Re-ID, vehicle Re-ID advances at a relatively slow pace. Some previous state-of-the-art approaches strongly rely on extra annotation, like attributes (e.g., vehicle color and type) and key-points (e.g., wheels and lamps). Recent work on person Re-ID shows that extracting more local features can achieve a better performance without considering extra annotation. In this paper, we propose an end-to-end trainable two-branch Partition and Reunion Network (PRN) for the challenging vehicle Re-ID task. Utilizing only identity labels, our proposed method outperforms existing state-of-the-art methods on four vehicle Re-ID benchmark datasets, including VeRi-776, Vehi-cleID, VRIC and CityFlow-ReID by a large margin.