Automatic Vehicle Trajectory Behavior Classification Based on Unmanned Aerial Vehicle-Derived Trajectories Using Machine Learning Techniques

IF 2.8 3区 地球科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Tee-Ann Teo, Min-Jhen Chang, Tsung-Han Wen
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引用次数: 0

Abstract

This study introduces an innovative scheme for classifying uncrewed aerial vehicle (UAV)-derived vehicle trajectory behaviors by employing machine learning (ML) techniques to transform original trajectories into various sequences: space–time, speed–time, and azimuth–time. These transformed sequences were subjected to normalization for uniform data analysis, facilitating the classification of trajectories into six distinct categories through the application of three ML classifiers: random forest, time series forest (TSF), and canonical time series characteristics. Testing was performed across three different intersections to reveal an accuracy exceeding 90%, underlining the superior performance of integrating azimuth–time and speed–time sequences over conventional space–time sequences for analyzing trajectory behaviors. This research highlights the TSF classifier’s robustness when incorporating speed data, demonstrating its efficiency in feature extraction and reliability in intricate trajectory pattern handling. This study’s results indicate that integrating direction and speed information significantly enhances predictive accuracy and model robustness. This comprehensive approach, which leverages UAV-derived trajectories and advanced ML techniques, represents a significant step forward in understanding vehicle trajectory behaviors, aligning with the goals of enhancing traffic control and management strategies for better urban mobility.
利用机器学习技术,基于无人机得出的轨迹对车辆轨迹行为进行自动分类
本研究采用机器学习(ML)技术,将原始轨迹转换为各种序列:时空、速度-时间和方位-时间,从而提出了一种创新方案,用于对无人驾驶航空飞行器(UAV)产生的飞行轨迹行为进行分类。对这些转换后的序列进行归一化处理,以便进行统一的数据分析,通过应用三种 ML 分类器(随机森林、时间序列森林 (TSF) 和典型时间序列特征)将轨迹分为六个不同的类别。在三个不同的交叉路口进行了测试,结果显示准确率超过 90%,这突出表明在分析轨迹行为时,方位角-时间序列和速度-时间序列的整合性能优于传统的时空序列。这项研究凸显了 TSF 分类器在整合速度数据时的鲁棒性,证明了其在特征提取方面的效率以及在处理复杂轨迹模式方面的可靠性。研究结果表明,整合方向和速度信息可显著提高预测准确性和模型稳健性。这种综合方法利用了无人机衍生轨迹和先进的 ML 技术,在理解车辆轨迹行为方面迈出了重要一步,符合加强交通控制和管理策略以改善城市交通的目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ISPRS International Journal of Geo-Information
ISPRS International Journal of Geo-Information GEOGRAPHY, PHYSICALREMOTE SENSING&nb-REMOTE SENSING
CiteScore
6.90
自引率
11.80%
发文量
520
审稿时长
19.87 days
期刊介绍: ISPRS International Journal of Geo-Information (ISSN 2220-9964) provides an advanced forum for the science and technology of geographic information. ISPRS International Journal of Geo-Information publishes regular research papers, reviews and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. The 2018 IJGI Outstanding Reviewer Award has been launched! This award acknowledge those who have generously dedicated their time to review manuscripts submitted to IJGI. See full details at http://www.mdpi.com/journal/ijgi/awards.
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