Moving object trajectory tracking plays an important role in traffic scheduling, route planning, advertising recommendations, and other associated social services. The success of moving object trajectory tracking can be attributed to the extensive use of Internet of Things (IoT) devices, which collect a growing volume of spatio-temporal data. To mine the spatio-temporal correlation, traditionally, recurrent neural networks (RNNs) and their variants, such as long-short-term memory (LSTM) and bidirectional long-short-term memory (BiLSTM), have shown their effectiveness in forecasting moving object positions. However, these methods have faced challenges in dealing with complex temporal dependencies due to the limited memory of storing past information using basic hidden layers. To address this issue, in this study, we propose a spatiotemporal attention-based transformer model to mine the spatiotemporal correlation of moving object trajectories in smart transportation systems, which offers improved performance in long-term trajectory prediction tasks. Moreover, most existing works overlook the importance of sampling issues in the trajectory prediction and tracking process. To this end, we develop an adaptive approach by leveraging spatio-temporal sampling to optimize trajectory tracking with reduced data transmission rates and computational costs. The experimental results on real-world datasets demonstrate the superiority of our transformer-based approach over existing RNN-based methods in trajectory predictions and confirm the feasibility of our optimal sampling solution in enhancing trajectory tracking performance.