In smart phones, vehicles and wearable devices, GPS sensors are ubiquitous, which can collect a large amount of valuable trajectory data by tracking moving objects. Analysis of this valuable trajectory data can benefit many practical applications, such as route planning and transportation optimization. However, unprecedented large-scale GPS data poses a challenge to the effective storage of trajectories. Therefore, the necessity of trajectory compression (also called trajectory sampling) is reflected. However, the latest compression methods usually perform unsatisfactorily in terms of space-time complexity or compression rate, which leads to rapid exhaustion of memory, computing, storage, and energy. In response to this problem, this paper proposes an online trajectory compression algorithm (ROPW algorithm) with error bounded that traverses the sliding window backwards. This algorithm has significantly improved the trajectory compression rate, and its average time complexity and space complexity is O(NlogN) and O(1) respectively. Finally, we conducted experiments on three real data sets to verify that the ROPW algorithm performed very well in terms of compression rate and time efficiency.