Online vehicle trajectory compression algorithm based on motion pattern recognition

Zhao, Dongbao

doi:10.1049/itr2.12187

Cited by 7 publications

(6 citation statements)

References 34 publications

(44 reference statements)

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“…Therefore, in the map matching, only from the geometric point of view was not enough to ensure high precision results. This is similar to the research results of K. Researchers K. Zhang et al [ 47 ] designed a spatio-temporal trajectory data compression algorithm that identifies the turning and speed change behavior of vehicles by analyzing their motion patterns, and extracts feature points from multiple perspectives.…”

Section: Resultssupporting

confidence: 79%

Traffic trajectory data analysis technology based on HMM model map matching algorithm

Sun,

2024

PLoS ONE

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The rapid growth of traffic trajectory data and the development of positioning technology have driven the demand for its analysis. However, in the current application scenarios, there are some problems such as the deviation between positioning data and real roads and low accuracy of existing trajectory data traffic prediction models. Therefore, a map matching algorithm based on hidden Markov models is proposed in this study. The algorithm starts from the global route, selects K nearest candidate paths, and identifies candidate points through the candidate paths. It uses changes in speed, angle, and other information to generate a state transition matrix that match trajectory points to the actual route. When processing trajectory data in the experiment, K = 5 is selected as the optimal value, the algorithm takes 51 ms and the accuracy is 95.3%. The algorithm performed well in a variety of road conditions, especially in parallel and mixed road sections, with an accuracy rate of more than 96%. Although the time loss of this algorithm is slightly increased compared with the traditional algorithm, its accuracy is stable. Under different road conditions, the accuracy of the algorithm is 98.3%, 97.5%, 94.8% and 96%, respectively. The accuracy of the algorithm based on traditional hidden Markov models is 95.9%, 95.7%, 95.4% and 94.6%, respectively. It can be seen that the accuracy of the algorithm designed has higher precision. The experiment proves that the map matching algorithms based on hidden Markov models is superior to other algorithms in terms of matching accuracy. This study makes the processing of traffic trajectory data more accurate.

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Section: Resultssupporting

confidence: 79%

Traffic trajectory data analysis technology based on HMM model map matching algorithm

Sun,

2024

PLoS ONE

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“…Traditional robot control methods, such as PID control, classical trajectory planning, etc., although to a certain extent can meet industrial needs, but with the complexity of the application scenarios, these methods gradually reveal their limitations [8][9]. For example, they may be difficult to adapt to rapidly changing environments or inefficient in dealing with nonlinear systems [10].…”

Section: Introductionmentioning

confidence: 99%

Robot kinematics analysis and trajectory planning based on artificial potential field method

Li,

Zhang

2024

Applied Mathematics and Nonlinear Sciences

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In order for a robot to complete a given task, it must first be made to autonomously reach a specified target location, so optimizing the robot path trajectory planning is a prerequisite for the use of robots. In this paper, for the two problems of the traditional artificial potential field method of target unreachable and local optimization, we first improve the repulsive potential field function so that the robot’s gravitational force and repulsive force are zero at the target position and then construct the robot kinematic analytical model by setting the virtual target point away from the local minimum value point. On this basis, the improved algorithm is used to compare simulation experiments in three environments with the real trajectory planning test in the showroom. In the climate “near the obstacle of the target point” and the pure U-shaped area environment, the robot of the traditional APF algorithm cannot reach the target point. However, the algorithm in this paper reaches the target point in all three environments, with a time taken of only 21, 33, and 42 seconds, respectively. In the real trajectory planning of the showroom, the improved algorithm in this paper reaches the target point quickly and accurately, with a total path trajectory length of 77.7835m, a time of 43 seconds, and a total turning angle of 793°. This paper provides an effective method for planning robot path trajectories in a complex and variable environment.

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“…Recent advances in navigation and positioning have made smart devices (such as smartphones, tablets, and wearable gadgets) essential to our daily lives (Yang, Stewart, Tang, Xie, and Li, 2018). GPS positional data collected by these devices support a wide range of applications such as route planning, anomaly detection, and decision-making (Zhang, Zhao, and Liu, 2022;Zhao and Shi, 2019). However, data collection processes are typically performed in real-time with usually very short time intervals, resulting in very large data volumes.…”

Section: Introductionmentioning

confidence: 99%

“…In online methods, trajectory data selection is processed on the fly then reducing data storage but increasing computational * Corresponding author time (Sun, Xia, Yuan, and Li, 2016). Although offline methods often result in higher accuracy, online methods are usually preferred to avoid the generation of large data volumes and to facilitate further data processing and mining (Zhang et al, 2022). This paper introduces a Flexible Douglas-Peucker (FDP) for trajectory compression and whose peculiarity is to consider the semantic and spatial dimensions.…”

Section: Introductionmentioning

confidence: 99%

A Flexible Trajectory Compression Algorithm for Multi-Modal Transportation

Mirvahabi

Abbaspour²,

Claramunt³

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Continuous progress in navigation, sensor-based, and GPS technologies have made smart devices essential to our daily lives and many location-based applications. However, the trajectory datasets generated by these applications require the management of large data volumes while preserving their main properties and semantics. One of the most popular methods for compressing trajectory data offline is the Douglas–Peucker (DP) algorithm, but its principles should be applied to a diverse range of contexts when considering real-time trajectory data. This paper introduces a Flexible Douglas-Peucker algorithm (FDP) that takes into account the data’s diversity, underlying properties, and semantics. The proposed framework is applied to the Geolife benchmark dataset with a series of different thresholds that reflects different contexts and constraints when performing a trajectory compression process. The results show that the proposed algorithm achieves a significant compression rate while preserving trajectory data points that have a semantic role concerning different modes of transportation.

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Online vehicle trajectory compression algorithm based on motion pattern recognition

Cited by 7 publications

References 34 publications

Traffic trajectory data analysis technology based on HMM model map matching algorithm

Traffic trajectory data analysis technology based on HMM model map matching algorithm

Robot kinematics analysis and trajectory planning based on artificial potential field method

A Flexible Trajectory Compression Algorithm for Multi-Modal Transportation

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