You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: An Overview of Moving Object Trajectory Compression Algorithms 
Chapter Title:  Velocity Based Trajectory Compression. 
1. Abstract of Cited Paper (BIB001):  In this work we investigate the quality bounds for the data stored in Moving Objects Databases (MOD) in the settings in which mobile units can perform an on-board data reduction in real time. It has been demonstrated that line simplification techniques, when properly applied to the large volumes of data pertaining to the past trajectories of the moving objects. result in substantial storage savings while guaranteeing deterministic error bounds to the queries posed to the MOD. On the other hand. it has also been demonstrated that if moving objects establish an agreement with the MOD regarding the (im)precision tolerance significant savings can be achieved in transmission when updating the location-in-time information. In this paper we take a first step towards analyzing the quality of the history in making in MOD by correlating the (impact of the) agreement between the server and the moving objects for on-line updates in real time with the error bounds of the data that becomes a representation of the past trajectories as time evolves.

Literature Review:
Velocity is one of the most basic features of moving objects and it can reflect the motion features of moving objects as well as the internal features in trajectories. The researches on compressing trajectory data based on velocity are not perfect by now. A famous velocity based trajectory compression is top-down speed-based algorithm proposed by Meratnia and Rolf BIB001 improving the existing compression techniques by exploiting the spatiotemporal information hiding in the time series which can be made by analyzing the derived speeds subsequent to the trajectory. A large difference between the travel speeds of two subsequent segments is a criterion that can be applied to retain the data point in the middle. An online algorithm called Dead Reckoning algorithm proposed by Trajcevski et al. BIB002 compresses trajectory by estimating the successor point through the current point and its velocity. A polynomial-time algorithm for optimal directionpreserving trajectory simplification, which supports broader application range than position-preserving simplification, proposed by Long et al.  can be also regarded as a velocity based trajectory compression. This method uses the maximum angular difference between the direction of the movement during each time period in original trajectory and the direction of the movement during the same time period in a simplification of original trajectory. Velocity based trajectory compression not only is simple and efficient but also can keep the internal features in trajectories; however, it is not popular, for the existing velocity based trajectory compression methods only take speed into account which may lead to greater errors and break the holistic geometrical characteristics of trajectories. In the future study, we hope that researchers will pay their attention to compressing trajectory data by various features of velocity (such as velocity direction and accelerated velocity) except for the magnitude of velocity.