• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

一种基于历史轨迹的集装箱定位与跟踪地图匹配算法。

A Historical-Trajectories-Based Map Matching Algorithm for Container Positioning and Tracking.

作者信息

Li Wenfeng, Zhang Wenwen, Gao Cong

机构信息

School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China.

出版信息

Sensors (Basel). 2022 Apr 15;22(8):3057. doi: 10.3390/s22083057.

DOI:10.3390/s22083057
PMID:35459042
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9027993/
Abstract

Positioning and tracking of containers is becoming an urgent demand of container transportation. Map matching algorithms have been widely applied to correct positioning errors. Because container trajectories have the characteristics of low sampling rate and missing GPS points, existing map matching algorithms based on the shortest path principle are not applicable for container positioning and tracking. To solve this problem, a historical-trajectories-based map matching algorithm (HTMM) is proposed. HTMM mines the travel time and the frequency in historical trajectories to help find the local path between two adjacent candidate points. HTMM first presents a path reconstruction method to calculate the travel time of historical trajectories on each road segment. A historical path index library based on a path tree is then developed to efficiently index historical paths. In addition, a location query and tracking method is introduced to determine the location of containers at given time. The performance of HTMM is validated on a real freight trajectory dataset. The experimental results show that HTMM has more than 3% and 5% improvement over the ST-Matching algorithm and HMM-based algorithm, respectively, at 60-300 s sampling intervals. The positioning error is reduced by half at a 60 s sampling interval.

摘要

集装箱的定位与跟踪正成为集装箱运输的迫切需求。地图匹配算法已被广泛应用于校正定位误差。由于集装箱轨迹具有采样率低和GPS点缺失的特点，现有的基于最短路径原则的地图匹配算法不适用于集装箱的定位与跟踪。为解决这一问题，提出了一种基于历史轨迹的地图匹配算法（HTMM）。HTMM挖掘历史轨迹中的行程时间和频率，以帮助找到两个相邻候选点之间的局部路径。HTMM首先提出一种路径重建方法，以计算历史轨迹在每个路段上的行程时间。然后开发一个基于路径树的历史路径索引库，以高效地索引历史路径。此外，引入一种位置查询和跟踪方法，以确定给定时间集装箱的位置。在真实货运轨迹数据集上验证了HTMM的性能。实验结果表明，在60 - 300秒的采样间隔下，HTMM分别比ST-Matching算法和基于HMM的算法有超过3%和5%的改进。在60秒的采样间隔下，定位误差减少了一半。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/c683f9dbf745/sensors-22-03057-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/697dab7f32f9/sensors-22-03057-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/2830211afcba/sensors-22-03057-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/e3b0e961fe79/sensors-22-03057-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/65f800ac831a/sensors-22-03057-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/05235fdb6ebf/sensors-22-03057-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/08892d38796a/sensors-22-03057-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/2a9a783eff71/sensors-22-03057-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/32166338522c/sensors-22-03057-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/8d3c6c69a720/sensors-22-03057-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/a8f8bc055cc0/sensors-22-03057-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/008ea4ccf980/sensors-22-03057-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/184f9d02a60d/sensors-22-03057-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/6373e243d411/sensors-22-03057-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/dbc0d10171fc/sensors-22-03057-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/c683f9dbf745/sensors-22-03057-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/697dab7f32f9/sensors-22-03057-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/2830211afcba/sensors-22-03057-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/e3b0e961fe79/sensors-22-03057-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/65f800ac831a/sensors-22-03057-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/05235fdb6ebf/sensors-22-03057-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/08892d38796a/sensors-22-03057-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/2a9a783eff71/sensors-22-03057-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/32166338522c/sensors-22-03057-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/8d3c6c69a720/sensors-22-03057-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/a8f8bc055cc0/sensors-22-03057-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/008ea4ccf980/sensors-22-03057-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/184f9d02a60d/sensors-22-03057-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/6373e243d411/sensors-22-03057-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/dbc0d10171fc/sensors-22-03057-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97a8/9027993/c683f9dbf745/sensors-22-03057-g015.jpg

相似文献

1
A Historical-Trajectories-Based Map Matching Algorithm for Container Positioning and Tracking.一种基于历史轨迹的集装箱定位与跟踪地图匹配算法。
Sensors (Basel). 2022 Apr 15;22(8):3057. doi: 10.3390/s22083057.
2
ConGPS: A Smart Container Positioning System Using Inertial Sensor and Electronic Map with Infrequent GPS.ConGPS：一种使用惯性传感器和带有不频繁GPS的电子地图的智能集装箱定位系统。
Sensors (Basel). 2023 Nov 15;23(22):9198. doi: 10.3390/s23229198.
3
A Trajectory Collaboration Based Map Matching Approach for Low-Sampling-Rate GPS Trajectories.基于轨迹协同的低采样率 GPS 轨迹地图匹配方法。
Sensors (Basel). 2020 Apr 6;20(7):2057. doi: 10.3390/s20072057.
4
Traffic trajectory data analysis technology based on HMM model map matching algorithm.基于 HMM 模型地图匹配算法的交通轨迹数据解析技术。
PLoS One. 2024 May 8;19(5):e0302656. doi: 10.1371/journal.pone.0302656. eCollection 2024.
5
Estimating Urban Road GPS Environment Friendliness with Bus Trajectories: A City-Scale Approach .利用公交轨迹评估城市道路的GPS环境友好性：一种城市尺度方法
Sensors (Basel). 2020 Mar 12;20(6):1580. doi: 10.3390/s20061580.
6
An adaptive map-matching algorithm based on hierarchical fuzzy system from vehicular GPS data.基于车辆 GPS 数据的层次模糊系统的自适应地图匹配算法。
PLoS One. 2017 Dec 5;12(12):e0188796. doi: 10.1371/journal.pone.0188796. eCollection 2017.
7
A map matching algorithm based on modified hidden Markov model considering time series dependency over larger time span.一种基于改进隐马尔可夫模型的地图匹配算法，该模型考虑了更大时间跨度上的时间序列依赖性。
Heliyon. 2023 Oct 23;9(11):e21368. doi: 10.1016/j.heliyon.2023.e21368. eCollection 2023 Nov.
8
An Improved Map-Matching Technique Based on the Fréchet Distance Approach for Pedestrian Navigation Services.一种基于弗雷歇距离法的改进型地图匹配技术，用于行人导航服务。
Sensors (Basel). 2016 Oct 22;16(10):1768. doi: 10.3390/s16101768.
9
Map-matching algorithm based on the junction decision domain and the hidden Markov model.基于交叉口决策域和隐马尔可夫模型的地图匹配算法。
PLoS One. 2019 May 13;14(5):e0216476. doi: 10.1371/journal.pone.0216476. eCollection 2019.
10
An Enhanced Map-Matching Algorithm for Real-Time Position Accuracy Improvement with a Low-Cost GPS Receiver.一种增强的地图匹配算法，可提高低成本 GPS 接收器的实时定位精度。
Sensors (Basel). 2018 Nov 8;18(11):3836. doi: 10.3390/s18113836.

本文引用的文献

1
GPS Trajectory Completion Using End-to-End Bidirectional Convolutional Recurrent Encoder-Decoder Architecture with Attention Mechanism.基于端到端双向卷积循环编解码器架构与注意力机制的 GPS 轨迹补全。
Sensors (Basel). 2020 Sep 9;20(18):5143. doi: 10.3390/s20185143.
2
A Trajectory Collaboration Based Map Matching Approach for Low-Sampling-Rate GPS Trajectories.基于轨迹协同的低采样率 GPS 轨迹地图匹配方法。
Sensors (Basel). 2020 Apr 6;20(7):2057. doi: 10.3390/s20072057.