Concerning the insufficient consideration of the cumulative error and offset which online Global Positioning System (GPS) trajectory data compression based on motion state change and the insufficient key point evaluation of online GPS trajectory data compression based on the offset calculation, an online compression of GPS trajectory data based on motion state change, named Synchronous Euclidean Distance (SED) Limited Thresholds Algorithm (SLTA), was proposed. This algorithm used steering angle and speed change to evaluate information of trajectory point. At the same time, SLTA introduced the SED to limit offset of trajectory point. So SLTA could reach better information retention. The experimental results show that the trajectory compression ratio can reach about 50%. Compared with Thresholds Algorithm (TA), the average SED error (less than 5 m) of SLTA can be negligible. For other trajectory data compression algorithms, SLTA's average angel error is the lowest (1.5°-2.3°) and run time is the most stable. SLTA can stably and effectively do online GPS trajectory data compression.

Concerning the "data sparsity" problem of moving object's trajectory prediction, i.e., the available historical trajectories are far from enough to cover all possible query trajectories that can obtain predicted destinations, a Trajectory Prediction Algorithm suffer from Data Sparsity based on Iterate Grid Partition and Entropy Estimation (TPDS-IGP&EE) was proposed. Firstly, the moving region of trajectories was iteratively divided into a two-dimensional plane grid graph, and then the original trajectories were mapped to the grid graph so that each trajectory could be represented as a grid sequence. Secondly, an L-Z entropy estimator was used to calculate the entropy value of trajectory sequence, and a new trajectory space was generated by doing trajectory synthesis based on trajectory entropy. At last combining with the Sub-Trajectory Synthesis (SubSyn) algorithm, sparse trajectory prediction was implemented. The experimental results show when trajectory completed percentage increases towards 90%, the coverage of the Baseline algorithm decreases to almost 25%. TPDS-IGP&EE algorithm successfully coped with it as expected with only an unnoticeable drop in coverage, and could constantly answer almost 100% of query trajectories. And TPDS-IGP&EE algorithm's prediction accuracy was generally 4% higher than Baseline algorithm. At the same time, the prediction time of Baseline algorithm to 100 ms was too long, while the prediction time of TPDS-IGP&EE algorithm could be negligible (10 μs). TPDS-IGP&EE algorithm can make an effective prediction for the sparse trajectory, providing much wider predicting range, faster predicting speed and better predicting accuracy.