A trajectory data compression algorithm based on spatio-temporal characteristics

Zhong, Yanling; Kong, Jinling; Zhang, Juqing; Jiang, Yizhu; Fan, Xiao; Wang, Zhuoyue

doi:10.7717/peerj-cs.1112

Cited by 4 publications

(5 citation statements)

References 40 publications

(46 reference statements)

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“…However, the time information of the GNSS measurements is not preserved with the DP algorithm. Therefore, there exist TS algorithms, or sometimes also called trajectory compression algorithms in literature, that try to preserve the timing information, for example, the TD-TR algorithm (Meratnia & de By, 2004), which is adopted from the DP algorithm, or the more recent CASC algorithm (Zhong et al, 2022) that offers further improvements over the TD-TR algorithm. In Li et al (2014Li et al ( , 2015 several more advanced (compared to the pure DP algorithm) geometric TS algorithms are benchmarked with map matching.…”

Section: State-of-the-artmentioning

confidence: 99%

Open source map matching with Markov decision processes: A new method and a detailed benchmark with existing approaches

Wöltche

2023

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Map matching is a widely used technology for mapping tracks to road networks. Typically, tracks are recorded using publicly available Global Navigation Satellite Systems, and road networks are derived from the publicly available OpenStreetMap project. The challenge lies in resolving the discrepancies between the spatial location of the tracks and the underlying road network of the map. Map matching is a combination of defined models, algorithms, and metrics for resolving these differences that result from measurement and map errors. The goal is to find routes within the road network that best represent the given tracks. These matches allow further analysis since they are freed from the noise of the original track, they accurately overlap with the road network, and they are corrected for impossible detours and gaps that were present in the original track. Given the ongoing need for map matching in mobility research, in this work, we present a novel map matching method based on Markov decision processes with Reinforcement Learning algorithms. We introduce the new Candidate Adoption feature, which allows our model to dynamically resolve outliers and noise clusters. We also incorporate an improved Trajectory Simplification preprocessing algorithm for further improving our performance. In addition, we introduce a new map matching metric that evaluates direction changes in the routes, which effectively reduces detours and round trips in the results. We provide our map matching implementation as Open Source Software (OSS) and compare our new approach with multiple existing OSS solutions on several public data sets. Our novel method is more robust to noise and outliers than existing methods and it outperforms them in terms of accuracy and computational speed.

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Section: State-of-the-artmentioning

confidence: 99%

Open source map matching with Markov decision processes: A new method and a detailed benchmark with existing approaches

Wöltche

2023

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“…The advantages of matrix operations and the point reduction method were used to improve the algorithm's computational efficiency. Zhong et al [43] VOLUME 11,2023 proposed a data compression algorithm based on the spatiotemporal characteristics of the AIS data for velocity change or stopping points. This algorithm compresses the trajectory data with orientation and velocity differences and time intervals as its parameters.…”

Section: Introductionmentioning

confidence: 99%

Compressing AIS Trajectory Data Based on the Multi-Objective Peak Douglas–Peucker Algorithm

et al. 2023

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The automatic identification system (AIS) provides a massive database for ocean science. The original AIS data are redundant. Direct use will cause a waste of data storage space and computation costs; hence, data compression must be performed. The Douglas-Peucker algorithm (DP) is an effective trajectory compression algorithm that can well preserve the spatial characteristics of a trajectory but has the following shortcomings: first, it has poor track recovery when compressing multi-turn routes; second, it does not consider the ship speed and heading; and third, it may have the wrong result of the compressed trajectory crossing the obstacle. To address these situations, this study proposes a multi-objective peak DP algorithm (MPDP) that adopts a peak sampling strategy, considers three optimization objectives (spatial characteristics, heading and speed) of trajectory and adds an obstacle detection mechanism to realize a compression algorithm more suitable for curved trajectories. The classical DP algorithm is compared with the MPDP algorithm by simulating trajectory and real trajectory experiments. The results show that the MPDP algorithm optimizes the length loss rate, simultaneous Euclidean distance, and average deviations of the speed and the heading while maintaining a high compression rate similar to that of the DP algorithm. Moreover, it can also successfully avoid obstacles. The optimization effect is most obvious for the multi-turn or hovering trajectory. The optimization rate of length loss, synchronous Euclidean distance, and average deviation of the heading can reach 40%.INDEX TERMS AIS data, Douglas-Peucker algorithm (DP), multi-objective peak Douglas-Peucker algorithm (MPDP), trajectory compression.

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“…In addition to the fact that the compression results are greatly afected by parameters, the sliding window algorithm only considers the operating mechanism of the trajectory points in the window, which may result in a greater degree of trajectory deformation before and after compression. Many researchers have improved this problem [28][29][30][31]. Sánchez-Heres and Sánchez [28] proposed a trajectory simplifcation algorithm based on behavior recognition for an equivalent passage plan (EPP).…”

Section: Introductionmentioning

confidence: 99%

“…Te EPP algorithm can ultimately preserve the extraordinary trajectory changes of the vessel's navigation, but the algorithm has a high time complexity and a low compression rate. Zhong et al [30] proposed a data compression algorithm based on the spatiotemporal characteristics of trajectory data (CASC). Te algorithm takes azimuth diference, velocity diference, and time interval as input thresholds and calculates the azimuth diference and velocity diference of trajectory points within the input time interval.…”

Section: Introductionmentioning

confidence: 99%

Vessel Trajectory Data Compression Algorithm considering Critical Region Identification

Zhang,

Zhou

2023

Journal of Advanced Transportation

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Vessel trajectory data are currently the most important data source for vessel trajectory data mining research. However, vessel AIS data have a short sampling time interval and a large amount of data redundancy, which hampers the efficient utilization of AIS data. In order to effectively remove redundant information from AIS data and improve its usage efficiency, a compression algorithm for vessel trajectory data compression algorithm considering critical region identification (VATDC_CCRI) is proposed. The VATDC_CCRI algorithm identifies the critical regions of a vessel’s trajectory by analyzing the distribution of node variation rates. It employs the Douglas–Peucker (DP) algorithm to compress the data in these critical regions, reducing the distortion of the trajectory after compression. Additionally, the algorithm utilizes a sliding window approach to process the initial trajectory to improve the quality of the compressed vessel trajectories and retain as many spatiotemporal characteristics of the original trajectories as possible. It combines the feature nodes from the crucial regions in the vessel’s trajectory with the results obtained from the sliding window algorithm, effectively compressing the vessel’s trajectory. Experiments conducted on individual and multiple trajectories demonstrate that the VATDC_CCRI algorithm achieves higher compression rates and exhibits faster processing speeds compared to other classical vessel trajectory compression algorithms while preserving the shape of the vessel’s trajectory significantly.

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A trajectory data compression algorithm based on spatio-temporal characteristics

Cited by 4 publications

References 40 publications

Open source map matching with Markov decision processes: A new method and a detailed benchmark with existing approaches

Open source map matching with Markov decision processes: A new method and a detailed benchmark with existing approaches

Compressing AIS Trajectory Data Based on the Multi-Objective Peak Douglas–Peucker Algorithm

Vessel Trajectory Data Compression Algorithm considering Critical Region Identification

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