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Sunshine-based trajectory analysis plays a significant impact on comprehensive scenarios, including driving safety and routing planning for mobile solar collection. To the best of our knowledge, however, a little work analyzes the impact of sunshine on mobile objects on different trajectories. This paper investigates the relationship between sunshine and the trajectories of mobile objects. We introduce a sunshine orientation model to integrate sunshine information with trajectories. A sunshine-based trajectory simplification model is proposed to remove redundant points in trajectories, which carry a little sunshine-related information. The sunshine-based trajectory simplification model is indeed versatile. We apply this model to help users efficiently query the sunshine information of their trips. Our design is evaluated with extensive simulations based on three-month general transit feed specification data from August to October in 2017 in five USA cities, including Washington, DC, USA, and Minneapolis. Furthermore, we develop a sunshine query system for mobile scenarios by embedding our sunshine-based trajectory simplification model and conduct a field study to show the validity and efficiency of this model. The evaluation results show that our design reduces 87.4% position points in trajectories while only sacrificing 9.6% of sunshine information in the field tests.

Big GPS trajectory datasets can have redundant spatio-temporal information for applications, which requires simplification as a key preprocessing for modeling. Many existing simplification methods focus on the geometric information from a trajectory per se. Conversely, methods considering geographic context often fail to provide spatially adaptive simplification, or require complex parameter settings to achieve this task. This study proposes a novel two-stage adaptive trajectory simplification method embedding spatial indexing, enrichment, and aggregation in an integrated process. The first stage employs a quadtree for the subdivision depending on the density of geographic context features (i.e. POIs), leading to a variable-resolution representation of the area. The second stage aggregates trajectory waypoints locating in the same quadtree leaf node into a representative point, making the aggregation adapting to the spatial layout of the geographic feature in the first stage. Evaluation with a real-world vehicle trajectory dataset shows that the proposed approach can automatically simplify trajectory segments at variable compression ratios with greater simplification in areas with sparse context features (e.g. rural) and less simplification in areas with dense context features (e.g. urban). More importantly, the method can still preserve inter-trajectory distances between original trajectories and simplified ones, while significantly reducing the computing time. [ABSTRACT FROM AUTHOR]

Facilitated by the establishment of terrestrial networks and satellite constellations of Automatic Identification System (AIS) receivers, large quantities of spatial and temporal information that trace ships' paths have been collected. The exponential increase in the amount of AIS data has caused expensive and time-consuming transmission, calculation and storage problems. Using appropriate trajectory simplification methods in a timely manner to compress redundant information while minimising the loss of importation information is important. To minimise the simplification error, this paper proposes an online multi-dimensional simplification algorithm for AIS trajectory streaming data. The simplification algorithm takes into account position, direction and speed preservation. Finally, a comparison experiment with other algorithms is made to examine the effectiveness of this algorithm. The results indicate that the proposed online multi-dimensional simplification algorithm can effectively preserve a ship's motion state, including its position, speed and course. [ABSTRACT FROM AUTHOR]

Various mobile devices have been used to collect, store and transmit tremendous trajectory data, and it is known that raw trajectory data seriously wastes the storage, network bandwidth and computing resource. To attack this issue, one-pass line simplification (LS ) algorithms have been developed, by compressing data points in a trajectory to a set of continuous line segments. However, these algorithms adopt the perpendicular Euclidean distance, and none of them uses the synchronous Euclidean distance (SED ), and cannot support spatiotemporal queries. To do this, we develop two one-pass error bounded trajectory simplification algorithms (CISED - S and CISED - W ) using SED , based on a novel spatiotemporal cone intersection technique. Using four real-life trajectory datasets, we experimentally show that our approaches are both efficient and effective. In terms of running time, algorithms CISED - S and CISED - W are on average 3 times faster than SQUISH - E (the fastest existing LS algorithm using SED ). In terms of compression ratios, CISED - S is close to and CISED - W is on average 19.6 % better than DPSED (the existing sub-optimal LS algorithm using SED and having the best compression ratios), and they are 21.1 % and 42.4 % better than SQUISH - E on average, respectively. [ABSTRACT FROM AUTHOR]

Sunshine has played a critical role in trajectory analysis. It is of great significance to design an algorithm that could not only estimate the sunshine information of the trajectory effectively, but also remove the redundant points and keep the necessary sunshine-based information. This paper proposes a sunshine based trajectory simplification algorithm, which considers the urban trajectory character, to solve the challenges of trajectory analyzing in various sunshine related scenarios. On the one hand, an orientation model is established to abstract the relationship between trajectory and sunlight. On the other hand, a sunshine related trajectory simplification model is proposed, and the genetic algorithm is applied to find the points that need to be reserved. Finally, the real-world data of Minneapolis, US, is used for experiment. The results demonstrate that the proposed algorithm can effectively reduce the number of turning points in the trajectories under the control of bound Tmax. [ABSTRACT FROM AUTHOR]

The high sampling rate of GPS makes the data set of the trajectory huge,which is difficult to handle in practical applications. It needs to rely on trajectory simplification algorithm to compress the original data. Aiming at this problem, this paper proposed a new simplification algorithm based on speed segmentation, namely SI'S algorithm, which preserved the spatiotempo-ral characteristics while preserving the velocity characteristics of a given trajectory. The SI'S algorithm divided the velocity values into several intervals and divided the trajectory into velocity-preserving segments. It calculated the SED threshold of each trajectory segment and derived a simplified trajectory by applying the TD-TR algorithm on each sub-trajectory segment. Extensive experiments with real datasets demonstrate that the proposed algorithm has better performance than ATS algorithms. [ABSTRACT FROM AUTHOR]

Lifelog is a record of one’s personal experiences in daily lives. User’s location is one of the most common information for logging a human’s life. By understanding one’s spatial mobility we can figure out other pieces of context such as businesses and activities. With GPS technology we can collect accurate spatial and temporal details of a movement. However, most GPS receivers generate a huge amount of data making it difficult to process and store such data. In this paper, we develop a generic add-on algorithm, feature-first trajectory simplification, to simplify trajectory data in lifelog applications. It is based on a simple sliding window mechanism counting occurrence of certain conditions. By automatically identifying feature points such as signal lost and found, stall, and turn, the proposed scheme provides rich context more than spatio-temporal information of a trajectory. In experiments with a case study of commuting in personal vehicles, we evaluate the effectiveness of the scheme. We find the proposed scheme significantly enhances existing simplification algorithms preserving much richer context of a trajectory.

We present an extensible, generic, spatio-temporal trajectory simplification framework that modularises trajectory simplification into the stages of normalising, ranking, and reduction. We combine a range of ranking strategies and scoring heuristics – some from the literature and some new – into our framework modules and create a variety of spatio-temporal trajectory simplification methods. These trajectory simplification methods are experimented upon using real world and synthetic datasets, measuring running time, geometric displacement, and region-of-interest visitation. The results indicate that our proposed framework creates a number of efficient and effective spatio-temporal trajectory simplification methods. [ABSTRACT FROM PUBLISHER]

Trajectory simplification has become a research hotspot since it plays a significant role in the data preprocessing, storage, and visualization of many offline and online applications, such as online maps, mobile health applications, and location-based services. Traditional heuristic-based algorithms utilize greedy strategy to reduce time cost, leading to high approximation error. An Optimal Trajectory Simplification Algorithm based on Graph Model (OPTTS) is proposed to obtain the optimal solution in this paper. Both min-# and min-ε problems are solved by the construction and regeneration of the breadth-first spanning tree and the shortest path search based on the directed acyclic graph (DAG). Although the proposed OPTTS algorithm can get optimal simplification results, it is difficult to apply in real-time services due to its high time cost. Thus, a new Online Trajectory Simplification Algorithm based on Directed Acyclic Graph (OLTS) is proposed to deal with trajectory stream. The algorithm dynamically constructs the breadth-first spanning tree, followed by real-time minimizing approximation error and real-time output. Experimental results show that OPTTS reduces the global approximation error by 82% compared to classical heuristic methods, while OLTS reduces the error by 77% and is 32% faster than the traditional online algorithm. Both OPTTS and OLTS have leading superiority and stable performance on different datasets. [ABSTRACT FROM AUTHOR]

In order to design a more concise and efficient algorithm for real-time trajectory simplification, by modifying the concept of zoning angle, which was more suitable for trajectory simplification of moving objects, this paper developed a new trajectory simplification algorithm NPLAZA based on PLAZA (piecewise linear approximation with zoning angle). With the instantaneous speed of moving objects, NPLAZA algorithm could control the errors in distance and direction to a smaller range. NPLAZA algorithm constructed a security zone to limit the error range of interpolation point by utilizing region filtering. Simulation experiments show that NPLAZA algorithm has smaller error range, high efficiency and stability in trajectory simplification. [ABSTRACT FROM AUTHOR]

Lifelog is a record of one's personal experiences in daily lives. User's location is one of the most common information for logging a human's life. By understanding one's spatial mobility we can figure out other pieces of context such as businesses and activities. With GPS technology we can collect accurate spatial and temporal details of a movement. However, most GPS receivers generate a huge amount of data making it difficult to process and store such data. In this paper, we develop a generic add-on algorithm, feature-first trajectory simplification, to simplify trajectory data in lifelog applications. It is based on a simple sliding window mechanism counting occurrence of certain conditions. By automatically identifying feature points such as signal lost and found, stall, and turn, the proposed scheme provides rich context more than spatio-temporal information of a trajectory. In experiments with a case study of commuting in personal vehicles, we evaluate the effectiveness of the scheme. We find the proposed scheme significantly enhances existing simplification algorithms preserving much richer context of a trajectory. [ABSTRACT FROM AUTHOR]

We propose to aid the interactive visualization of time-varying spatial data sets by simplifying node position data over the entire simulation as opposed to over individual states. Our approach is based on two observations. The first observation is that the trajectory of some nodes can be approximated well without recording the position of the node for every state. The second observation is that there are groups of nodes whose motion from one state to the next can be approximated well with a single transformation. We present data set simplification techniques that take advantage of this node data redundancy. Our techniques are general, supporting many types of simulations, they achieve good compression factors, and they allow rigorous control of the maximum node position approximation error. We demonstrate our approach in the context of finite element analysis data, of liquid flow simulation data, and of fusion simulation data. [ABSTRACT FROM AUTHOR]