An Overview of Moving Object Trajectory Compression Algorithms <s> Speed Error. <s> Current efforts directed at enforcing traffic laws are beset by a dilemma. On the one hand enforcement programmes are limited in duration and intensity, thereby producing weak halo effects on drivers' behaviour. On the other hand, public attitudes towards compliance are largely conditioned by the visibility of enforcement programmes. This paper reviews the impact of the semi-automatic monitoring systems in improving detection in traffic law programmes. The effects of the technology on drivers' behaviour are assessed and implications for the development of a fully automatic traffic policing system are teased out. Secondly, the paper attempts to put into perspective any consequences that an automatic monitoring system may have for driver behaviour. The fact that such systems do not yet exist poses problems for an ergonomics evaluation of their projected impact. However, taking into account the existence of core technology, tentative conclusions can be drawn as to the likely benefits and disadvantages of such systems. <s> BIB001 </s> An Overview of Moving Object Trajectory Compression Algorithms <s> Speed Error. <s> This paper proposes the use of a wireless sensor network of "cat's eye" augmented with embedded processing, communication, and sensing capabilities to monitor vehicle behaviour on augmented roads. The primary goal of the system is to provide drivers with early warning of potentially dangerous situations that may arise. The focus of the paper is on the software architecture needed and the technical challenges to be overcome in order to support this and related applications. <s> BIB002
Speed error (SpdE) is an important metric for various kinds of transit applications. For instance, velocity measurement system gets overspeed hotspots by velocity information BIB001 , as well as acceleration and deceleration data help to identify all kinds of irregular driving behaviors, which will help police to find vehicle's illegal activities BIB002 . The computing method of speed error is similar to heading error. It calculates the difference value between actual velocity and estimated velocity instead of calculating angular deflection. Information Loss Degree based on SED (ILDSED) is the mean value of maximum SED distance error (SEDEmax(OT, RT)), average SED distance error (SEDEavg(OT, RT)), and minimum SED distance error (SEDEmin(OT, RT)) between original trajectory OT and compressed trajectory RT, which can be calculated as Information Loss Degree based on DTW (ILDdtw) is measured by the time warping distance between original trajectory OT and compressed trajectory RT, which can be calculated as others. Here, SEDE(op 1 , rp 1 ) is the SED error between point op 1 and rp 1 , which, respectively, are the first point of OT and RT. Rest(OT) and Rest(RT) are the remaining trajectory after removing the first sampling point. ILDdtw calculates the Information Loss Degree by DTW error. Information Loss Degree based on Speed Corner (ILDcorner) is measured by the original and compressed Speed Corner of moving objects which can be calculated as