<div align="center">
<img src="docs/_static/images/gotrackit.png" />
</div>
<br>
<div align=center>
[](https://gotrackit.readthedocs.io/en/latest/?badge=latest)
~ 一个包搞定：路网获取、路网优化、宏微观地图匹配、匹配可视化、问题路段快速定位 ~
唐铠, 794568794@qq.com, tangkai@zhechengdata.com
</div>
<br>
**版本状态：04.27即将更新更新: v0.2.2**
更新命令：pip install --upgrade -i https://pypi.org/simple/ gotrackit
- 向量化改造, 且引入FMM(Fast Map Matching)路径预存储机制, 大规模路网匹配效率大幅度提升
- 完善报错机制, 遇到GPS脏数据不再报错停止, 而是跳过, 并且在所有的agents计算完毕后输出有问题的agent编号
- BUG修复
<br>
<div align=center>
~ v0.2.2效率将大幅度提升 ~
</div>
<br>
与上一版本对比:
| 样例数据 | 有效的GPS点数 | top_k(k邻近候选参数) | gps_buffer(临域半径) | 候选路段条数 | 状态转移次数 | v0.2.1版解算时间 | v0.2.2版解算时间 |
|----------------|----------|----------------|------------------|---------|------------|-------------|-----------|
| 1辆车,深圳稀疏轨迹点样例1 | 190 | 60 | 500m | 10615 | 629788次 | 28秒 | **3.3秒** |
| 1辆车,深圳稀疏轨迹点样例2 | 400 | 20 | 120m | 5137 | 82006次 | 7.8秒 | **1.7秒** |
v0.2.2多核效率对比:
基于上表深圳稀疏轨迹点样例2，我们将他复制150份，进行多核测试，可以看到到6核时, 效率已经不再提升，最快96s解算完150条轨迹，平均每条轨迹0.64s，相较于1.7s再次提升了60%，在车辆数较多时，多核的效率提升很明显。
| 样例数据 | 有效的GPS点数 | top_k | gps_buffer | 候选路段条数 | 状态转移次数 | v0.2.2解算时间 |
|-------------------------------------------|----------|----------------|------------------|-----|--------|------------|
| 150辆车的GPS轨迹(单核串行,子图搜索,有构建子图的额外开销,提前预计算路径) | 6w | 20 | 120m | 75W | 1200W次 | 300.0秒 |
| 150辆车的GPS轨迹(3核并行,子图搜索,有构建子图的额外开销,提前预计算路径) | 6w | 20 | 120m | 75W | 1200W次 | 139.6秒 |
| 150辆车的GPS轨迹(3核并行,全图搜索,无构建子图的额外开销,提前预计算路径) | 6w | 20 | 120m | 75W | 1200W次 | 120.3秒 |
| 150辆车的GPS轨迹(4核并行,全图搜索,无构建子图的额外开销,提前预计算路径) | 6w | 20 | 120m | 75W | 1200W次 | 104.9秒 |
| 150辆车的GPS轨迹(5核并行,全图搜索,无构建子图的额外开销,提前预计算路径) | 6w | 20 | 120m | 75W | 1200W次 | 96.4秒 |
| 150辆车的GPS轨迹(6核并行,全图搜索,无构建子图的额外开销,提前预计算路径) | 6w | 20 | 120m | 75W | 1200W次 | 97.5秒 |
<br>
<div align=center>
~ 稀疏轨迹匹配与路径补全 ~
</div>
<br>
深圳稀疏轨迹点样例1：
<div align="center">
<img src="docs/_static/images/极稀疏轨迹匹配.gif" />
</div>
<div align="center">
<img src="docs/_static/images/匹配动画样例3.gif" />
</div>
<br>
<div align=center>
~ 常规匹配 ~
</div>
<br>
<div align="center">
<img src="docs/_static/images/匹配动画样例1.gif" />
</div>
<div align="center">
<img src="docs/_static/images/匹配动画样例2.gif" />
</div>
<div align="center">
<img src="docs/_static/images/匹配动画样例4.gif" />
</div>
<div align="center">
<img src="docs/_static/images/geojson_res.jpg" />
</div>
<br>
<div align=center>
~ 用户交流群， 遇到BUG无法解决请进群交流，别忘了给项目一颗star哦， 您的支持是我迭代的动力 ~
</div>
<br>
<div align="center">
<img src="docs/_static/images/wxq.jpg" />
</div>
## 1. 简介
本地图匹配包基于隐马尔可夫模型(HMM)实现了连续GPS点位的概率建模，利用这个包可以轻松对GPS数据进行地图匹配，本开源包的特点如下:
**数据无忧**
- 提供路网生产模块以及路网优化接口，您不需要准备任何路网和GPS数据即可玩转地图匹配；
- 提供GPS样例数据生产模块，解决没有GPS数据的难题；
- 提供GPS数据清洗接口，包括滑动窗口降噪、数据降频。
**文档齐全**
- 中文文档，有详细的操作指引；
- 算法原理讲解部分不涉及复杂的公式推导，使用动画形式剖析算法原理,简洁明了。
**匹配结果自动优化**
- 对基于HMM匹配的初步路径进行了优化，对于不连通的位置会自动补路，对于实际路网不连通的位置会输出警告，方便用户检查路网。
### 1.1. 如何安装gotrackit
#### __所需前置依赖__
- geopy(2.4.1)
- gdal(3.4.3)
- shapely(2.0.3)
- fiona(1.9.5)
- pyproj(3.6.1)
- geopandas(0.14.3)
- networkx(3.2.1)
- pandas(2.0.3)
- numpy(1.26.2)
- keplergl(0.3.2)
括号中为作者使用版本(基于python3.11), 仅供参考
#### __使用pip安装__
安装：
``` shell
pip install -i https://pypi.org/simple/ gotrackit
```
更新：
``` shell
pip install --upgrade -i https://pypi.org/simple/ gotrackit
```
### 1.2 用户手册与视频教程
[用户手册](https://gotrackit.readthedocs.io/en/latest/)
[基于隐马尔可夫模型(HMM)的地图匹配算法动画版！学不会你来打我！](https://www.bilibili.com/video/BV1gQ4y1w7dC)
[一个python包搞定路网获取+地图匹配！](https://www.bilibili.com/video/BV1nC411z7Vg)
[gotrackit地图匹配包参数详解与问题排查](https://www.bilibili.com/video/BV1qK421Y7hV)
[QGIS路网拓扑显示、底图加载、样式复用、map保存](https://www.bilibili.com/video/BV1Sq421F7QX)
## 2. 地图匹配问题
__如何依据GPS数据推算车辆的实际路径？__
Raw data
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"author_email": "Kai Tang <794568794@qq.com>",
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"description": "\r\n\r\n<div align=\"center\">\r\n <img src=\"docs/_static/images/gotrackit.png\" />\r\n</div>\r\n\r\n<br>\r\n\r\n<div align=center>\r\n\r\n[](https://gotrackit.readthedocs.io/en/latest/?badge=latest)\r\n\r\n\r\n\r\n\r\n\r\n~ \u4e00\u4e2a\u5305\u641e\u5b9a\uff1a\u8def\u7f51\u83b7\u53d6\u3001\u8def\u7f51\u4f18\u5316\u3001\u5b8f\u5fae\u89c2\u5730\u56fe\u5339\u914d\u3001\u5339\u914d\u53ef\u89c6\u5316\u3001\u95ee\u9898\u8def\u6bb5\u5feb\u901f\u5b9a\u4f4d ~\r\n\r\n\u5510\u94e0, 794568794@qq.com, tangkai@zhechengdata.com\r\n</div>\r\n<br>\r\n\r\n\r\n\r\n**\u7248\u672c\u72b6\u6001\uff1a04.27\u5373\u5c06\u66f4\u65b0\u66f4\u65b0: v0.2.2**\r\n\r\n\u66f4\u65b0\u547d\u4ee4\uff1apip install --upgrade -i https://pypi.org/simple/ gotrackit\r\n\r\n- \u5411\u91cf\u5316\u6539\u9020, \u4e14\u5f15\u5165FMM(Fast Map Matching)\u8def\u5f84\u9884\u5b58\u50a8\u673a\u5236, \u5927\u89c4\u6a21\u8def\u7f51\u5339\u914d\u6548\u7387\u5927\u5e45\u5ea6\u63d0\u5347\r\n\r\n- \u5b8c\u5584\u62a5\u9519\u673a\u5236, \u9047\u5230GPS\u810f\u6570\u636e\u4e0d\u518d\u62a5\u9519\u505c\u6b62, \u800c\u662f\u8df3\u8fc7, \u5e76\u4e14\u5728\u6240\u6709\u7684agents\u8ba1\u7b97\u5b8c\u6bd5\u540e\u8f93\u51fa\u6709\u95ee\u9898\u7684agent\u7f16\u53f7\r\n\r\n- BUG\u4fee\u590d\r\n\r\n<br>\r\n\r\n<div align=center>\r\n~ v0.2.2\u6548\u7387\u5c06\u5927\u5e45\u5ea6\u63d0\u5347 ~\r\n</div>\r\n\r\n<br>\r\n\r\n\u4e0e\u4e0a\u4e00\u7248\u672c\u5bf9\u6bd4:\r\n\r\n| \u6837\u4f8b\u6570\u636e | \u6709\u6548\u7684GPS\u70b9\u6570 | top_k(k\u90bb\u8fd1\u5019\u9009\u53c2\u6570) | gps_buffer(\u4e34\u57df\u534a\u5f84) | \u5019\u9009\u8def\u6bb5\u6761\u6570 | \u72b6\u6001\u8f6c\u79fb\u6b21\u6570 | v0.2.1\u7248\u89e3\u7b97\u65f6\u95f4 | v0.2.2\u7248\u89e3\u7b97\u65f6\u95f4 |\r\n|----------------|----------|----------------|------------------|---------|------------|-------------|-----------|\r\n| 1\u8f86\u8f66,\u6df1\u5733\u7a00\u758f\u8f68\u8ff9\u70b9\u6837\u4f8b1 | 190 | 60 | 500m | 10615 | 629788\u6b21 | 28\u79d2 | **3.3\u79d2** |\r\n| 1\u8f86\u8f66,\u6df1\u5733\u7a00\u758f\u8f68\u8ff9\u70b9\u6837\u4f8b2 | 400 | 20 | 120m | 5137 | 82006\u6b21 | 7.8\u79d2 | **1.7\u79d2** |\r\n\r\n\r\nv0.2.2\u591a\u6838\u6548\u7387\u5bf9\u6bd4:\r\n\r\n\u57fa\u4e8e\u4e0a\u8868\u6df1\u5733\u7a00\u758f\u8f68\u8ff9\u70b9\u6837\u4f8b2\uff0c\u6211\u4eec\u5c06\u4ed6\u590d\u5236150\u4efd\uff0c\u8fdb\u884c\u591a\u6838\u6d4b\u8bd5\uff0c\u53ef\u4ee5\u770b\u5230\u52306\u6838\u65f6, \u6548\u7387\u5df2\u7ecf\u4e0d\u518d\u63d0\u5347\uff0c\u6700\u5feb96s\u89e3\u7b97\u5b8c150\u6761\u8f68\u8ff9\uff0c\u5e73\u5747\u6bcf\u6761\u8f68\u8ff90.64s\uff0c\u76f8\u8f83\u4e8e1.7s\u518d\u6b21\u63d0\u5347\u4e8660%\uff0c\u5728\u8f66\u8f86\u6570\u8f83\u591a\u65f6\uff0c\u591a\u6838\u7684\u6548\u7387\u63d0\u5347\u5f88\u660e\u663e\u3002\r\n\r\n| \u6837\u4f8b\u6570\u636e | \u6709\u6548\u7684GPS\u70b9\u6570 | top_k | gps_buffer | \u5019\u9009\u8def\u6bb5\u6761\u6570 | \u72b6\u6001\u8f6c\u79fb\u6b21\u6570 | v0.2.2\u89e3\u7b97\u65f6\u95f4 |\r\n|-------------------------------------------|----------|----------------|------------------|-----|--------|------------|\r\n| 150\u8f86\u8f66\u7684GPS\u8f68\u8ff9(\u5355\u6838\u4e32\u884c,\u5b50\u56fe\u641c\u7d22,\u6709\u6784\u5efa\u5b50\u56fe\u7684\u989d\u5916\u5f00\u9500,\u63d0\u524d\u9884\u8ba1\u7b97\u8def\u5f84) | 6w | 20 | 120m | 75W | 1200W\u6b21 | 300.0\u79d2 | \r\n| 150\u8f86\u8f66\u7684GPS\u8f68\u8ff9(3\u6838\u5e76\u884c,\u5b50\u56fe\u641c\u7d22,\u6709\u6784\u5efa\u5b50\u56fe\u7684\u989d\u5916\u5f00\u9500,\u63d0\u524d\u9884\u8ba1\u7b97\u8def\u5f84) | 6w | 20 | 120m | 75W | 1200W\u6b21 | 139.6\u79d2 |\r\n| 150\u8f86\u8f66\u7684GPS\u8f68\u8ff9(3\u6838\u5e76\u884c,\u5168\u56fe\u641c\u7d22,\u65e0\u6784\u5efa\u5b50\u56fe\u7684\u989d\u5916\u5f00\u9500,\u63d0\u524d\u9884\u8ba1\u7b97\u8def\u5f84) | 6w | 20 | 120m | 75W | 1200W\u6b21 | 120.3\u79d2 |\r\n| 150\u8f86\u8f66\u7684GPS\u8f68\u8ff9(4\u6838\u5e76\u884c,\u5168\u56fe\u641c\u7d22,\u65e0\u6784\u5efa\u5b50\u56fe\u7684\u989d\u5916\u5f00\u9500,\u63d0\u524d\u9884\u8ba1\u7b97\u8def\u5f84) | 6w | 20 | 120m | 75W | 1200W\u6b21 | 104.9\u79d2 | \r\n| 150\u8f86\u8f66\u7684GPS\u8f68\u8ff9(5\u6838\u5e76\u884c,\u5168\u56fe\u641c\u7d22,\u65e0\u6784\u5efa\u5b50\u56fe\u7684\u989d\u5916\u5f00\u9500,\u63d0\u524d\u9884\u8ba1\u7b97\u8def\u5f84) | 6w | 20 | 120m | 75W | 1200W\u6b21 | 96.4\u79d2 | \r\n| 150\u8f86\u8f66\u7684GPS\u8f68\u8ff9(6\u6838\u5e76\u884c,\u5168\u56fe\u641c\u7d22,\u65e0\u6784\u5efa\u5b50\u56fe\u7684\u989d\u5916\u5f00\u9500,\u63d0\u524d\u9884\u8ba1\u7b97\u8def\u5f84) | 6w | 20 | 120m | 75W | 1200W\u6b21 | 97.5\u79d2 | \r\n\r\n\r\n<br>\r\n\r\n<div align=center>\r\n~ \u7a00\u758f\u8f68\u8ff9\u5339\u914d\u4e0e\u8def\u5f84\u8865\u5168 ~\r\n</div>\r\n\r\n<br>\r\n\r\n\u6df1\u5733\u7a00\u758f\u8f68\u8ff9\u70b9\u6837\u4f8b1\uff1a\r\n<div align=\"center\">\r\n <img src=\"docs/_static/images/\u6781\u7a00\u758f\u8f68\u8ff9\u5339\u914d.gif\" />\r\n</div>\r\n\r\n\r\n<div align=\"center\">\r\n <img src=\"docs/_static/images/\u5339\u914d\u52a8\u753b\u6837\u4f8b3.gif\" />\r\n</div>\r\n\r\n\r\n<br>\r\n\r\n<div align=center>\r\n~ \u5e38\u89c4\u5339\u914d ~\r\n</div>\r\n\r\n<br>\r\n\r\n<div align=\"center\">\r\n <img src=\"docs/_static/images/\u5339\u914d\u52a8\u753b\u6837\u4f8b1.gif\" />\r\n</div>\r\n\r\n<div align=\"center\">\r\n <img src=\"docs/_static/images/\u5339\u914d\u52a8\u753b\u6837\u4f8b2.gif\" />\r\n</div>\r\n\r\n\r\n<div align=\"center\">\r\n <img src=\"docs/_static/images/\u5339\u914d\u52a8\u753b\u6837\u4f8b4.gif\" />\r\n</div>\r\n\r\n<div align=\"center\">\r\n <img src=\"docs/_static/images/geojson_res.jpg\" />\r\n</div>\r\n\r\n<br>\r\n\r\n<div align=center>\r\n~ \u7528\u6237\u4ea4\u6d41\u7fa4\uff0c \u9047\u5230BUG\u65e0\u6cd5\u89e3\u51b3\u8bf7\u8fdb\u7fa4\u4ea4\u6d41\uff0c\u522b\u5fd8\u4e86\u7ed9\u9879\u76ee\u4e00\u9897star\u54e6\uff0c \u60a8\u7684\u652f\u6301\u662f\u6211\u8fed\u4ee3\u7684\u52a8\u529b ~\r\n</div>\r\n\r\n<br>\r\n\r\n<div align=\"center\">\r\n <img src=\"docs/_static/images/wxq.jpg\" />\r\n</div>\r\n\r\n\r\n## 1. \u7b80\u4ecb\r\n\u672c\u5730\u56fe\u5339\u914d\u5305\u57fa\u4e8e\u9690\u9a6c\u5c14\u53ef\u592b\u6a21\u578b(HMM)\u5b9e\u73b0\u4e86\u8fde\u7eedGPS\u70b9\u4f4d\u7684\u6982\u7387\u5efa\u6a21\uff0c\u5229\u7528\u8fd9\u4e2a\u5305\u53ef\u4ee5\u8f7b\u677e\u5bf9GPS\u6570\u636e\u8fdb\u884c\u5730\u56fe\u5339\u914d\uff0c\u672c\u5f00\u6e90\u5305\u7684\u7279\u70b9\u5982\u4e0b:\r\n\r\n**\u6570\u636e\u65e0\u5fe7**\r\n- \u63d0\u4f9b\u8def\u7f51\u751f\u4ea7\u6a21\u5757\u4ee5\u53ca\u8def\u7f51\u4f18\u5316\u63a5\u53e3\uff0c\u60a8\u4e0d\u9700\u8981\u51c6\u5907\u4efb\u4f55\u8def\u7f51\u548cGPS\u6570\u636e\u5373\u53ef\u73a9\u8f6c\u5730\u56fe\u5339\u914d\uff1b\r\n- \u63d0\u4f9bGPS\u6837\u4f8b\u6570\u636e\u751f\u4ea7\u6a21\u5757\uff0c\u89e3\u51b3\u6ca1\u6709GPS\u6570\u636e\u7684\u96be\u9898\uff1b\r\n- \u63d0\u4f9bGPS\u6570\u636e\u6e05\u6d17\u63a5\u53e3\uff0c\u5305\u62ec\u6ed1\u52a8\u7a97\u53e3\u964d\u566a\u3001\u6570\u636e\u964d\u9891\u3002\r\n\r\n**\u6587\u6863\u9f50\u5168**\r\n\r\n- \u4e2d\u6587\u6587\u6863\uff0c\u6709\u8be6\u7ec6\u7684\u64cd\u4f5c\u6307\u5f15\uff1b\r\n- \u7b97\u6cd5\u539f\u7406\u8bb2\u89e3\u90e8\u5206\u4e0d\u6d89\u53ca\u590d\u6742\u7684\u516c\u5f0f\u63a8\u5bfc\uff0c\u4f7f\u7528\u52a8\u753b\u5f62\u5f0f\u5256\u6790\u7b97\u6cd5\u539f\u7406,\u7b80\u6d01\u660e\u4e86\u3002\r\n\r\n**\u5339\u914d\u7ed3\u679c\u81ea\u52a8\u4f18\u5316**\r\n- \u5bf9\u57fa\u4e8eHMM\u5339\u914d\u7684\u521d\u6b65\u8def\u5f84\u8fdb\u884c\u4e86\u4f18\u5316\uff0c\u5bf9\u4e8e\u4e0d\u8fde\u901a\u7684\u4f4d\u7f6e\u4f1a\u81ea\u52a8\u8865\u8def\uff0c\u5bf9\u4e8e\u5b9e\u9645\u8def\u7f51\u4e0d\u8fde\u901a\u7684\u4f4d\u7f6e\u4f1a\u8f93\u51fa\u8b66\u544a\uff0c\u65b9\u4fbf\u7528\u6237\u68c0\u67e5\u8def\u7f51\u3002\r\n\r\n\r\n\r\n### 1.1. \u5982\u4f55\u5b89\u88c5gotrackit\r\n\r\n#### __\u6240\u9700\u524d\u7f6e\u4f9d\u8d56__\r\n\r\n- geopy(2.4.1)\r\n- gdal(3.4.3)\r\n- shapely(2.0.3)\r\n- fiona(1.9.5)\r\n- pyproj(3.6.1)\r\n- geopandas(0.14.3)\r\n- networkx(3.2.1)\r\n- pandas(2.0.3)\r\n- numpy(1.26.2)\r\n- keplergl(0.3.2)\r\n\r\n\u62ec\u53f7\u4e2d\u4e3a\u4f5c\u8005\u4f7f\u7528\u7248\u672c(\u57fa\u4e8epython3.11), \u4ec5\u4f9b\u53c2\u8003\r\n\r\n#### __\u4f7f\u7528pip\u5b89\u88c5__\r\n\r\n\u5b89\u88c5\uff1a\r\n\r\n``` shell\r\npip install -i https://pypi.org/simple/ gotrackit\r\n```\r\n\r\n\u66f4\u65b0\uff1a\r\n``` shell\r\npip install --upgrade -i https://pypi.org/simple/ gotrackit\r\n```\r\n\r\n### 1.2 \u7528\u6237\u624b\u518c\u4e0e\u89c6\u9891\u6559\u7a0b\r\n\r\n[\u7528\u6237\u624b\u518c](https://gotrackit.readthedocs.io/en/latest/)\r\n\r\n[\u57fa\u4e8e\u9690\u9a6c\u5c14\u53ef\u592b\u6a21\u578b(HMM)\u7684\u5730\u56fe\u5339\u914d\u7b97\u6cd5\u52a8\u753b\u7248\uff01\u5b66\u4e0d\u4f1a\u4f60\u6765\u6253\u6211\uff01](https://www.bilibili.com/video/BV1gQ4y1w7dC)\r\n\r\n[\u4e00\u4e2apython\u5305\u641e\u5b9a\u8def\u7f51\u83b7\u53d6+\u5730\u56fe\u5339\u914d\uff01](https://www.bilibili.com/video/BV1nC411z7Vg)\r\n\r\n[gotrackit\u5730\u56fe\u5339\u914d\u5305\u53c2\u6570\u8be6\u89e3\u4e0e\u95ee\u9898\u6392\u67e5](https://www.bilibili.com/video/BV1qK421Y7hV)\r\n\r\n[QGIS\u8def\u7f51\u62d3\u6251\u663e\u793a\u3001\u5e95\u56fe\u52a0\u8f7d\u3001\u6837\u5f0f\u590d\u7528\u3001map\u4fdd\u5b58](https://www.bilibili.com/video/BV1Sq421F7QX)\r\n\r\n\r\n## 2. 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