update readme and contribution v1.0 (#216)

* update readme and contribution

* update readme and contribution

Author: aptx1231

contribution_list.md

@@ -2,6 +2,8 @@
 
 The model reproduction work in the library has been assisted by several developers. The specific contribution list is shown below.
 
+For a list of all models reproduced in LibCity, see [Docs](https://bigscity-libcity-docs.readthedocs.io/en/latest/user_guide/model.html), where you can see the abbreviation of the model and the corresponding papers and citations.
+
 | Task | Model  | Contributor                                                  |
 | ------ | ------------------------------------------------------------ | ------ |
 |Time Series Prediction|HA|[XBR-1111](https://github.com/XBR-1111), [aptx1231](https://github.com/aptx1231)|
@@ -43,14 +45,16 @@ The model reproduction work in the library has been assisted by several develope
 |                                     | GTS            | [aptx1231](https://github.com/aptx1231), [buaacjw](https://github.com/buaacjw), [t123yh](https://github.com/t123yh), [h9tyf](https://github.com/h9tyf) |
 |                                     | GMAN           | [huangjiawei128](https://github.com/huangjiawei128), [fry412](https://github.com/fry412) |
 |                                     | ATDM           | [zhangjiahui-buaa](https://github.com/zhangjiahui-buaa)      |
-|                                     | HGCN           | [XBR-1111](https://github.com/XBR-1111), [Matty-blu](https://github.com/Matty-blu), [nan0054](https://github.com/nan0054) |
+|                                     | HGCN           | [XBR-1111](https://github.com/XBR-1111), [Matty-blu](https://github.com/Matty-blu), [nan0054](https://github.com/nan0054), [PotassiumWings](https://github.com/PotassiumWings) |
 |                                     | STAGGCN        | [a-l-r](https://github.com/a-l-r1), [aptx1231](https://github.com/aptx1231) |
-| | DKFN | [a-l-r](https://github.com/a-l-r1), [aptx1231](https://github.com/aptx1231) |
+| | DKFN | [a-l-r](https://github.com/a-l-r1), [aptx1231](https://github.com/aptx1231), [NickHan-cs](https://github.com/NickHan-cs) |
 | | STTN | [NickHan-cs](https://github.com/NickHan-cs) |
 |On-Demand Service Prediction|CCRNN|[aptx1231](https://github.com/aptx1231)|
 |                                     |STG2Seq|[aptx1231](https://github.com/aptx1231), [Stevezzy](https://github.com/Stevezzy)|
 ||DMVSTNet|[l782993610](https://github.com/l782993610), [WANDY666](https://github.com/WANDY666)|
 |OD Matrix Prediction|GEML|[l782993610](https://github.com/l782993610)|
+||CSTN|[l782993610](https://github.com/l782993610)|
+|Traffic Accidents Prediction|GSNet|[excelsior399](https://github.com/excelsior399)|
 | Trajectory Next-Location Prediction | FPMC   | [WenMellors](https://github.com/WenMellors) |
 |   | RNN-1  | [WenMellors](https://github.com/WenMellors) |
 |  | STRNN | [BugMakerzzz](https://github.com/BugMakerzzz), [xcw-1010](https://github.com/xcw-1010), [WenMellors](https://github.com/WenMellors) |
@@ -62,9 +66,12 @@ The model reproduction work in the library has been assisted by several develope
 ||LSTPM|[ssdrywz](https://github.com/ssdrywz), [Jerry18231174](https://github.com/Jerry18231174), [WenMellors](https://github.com/WenMellors)|
 ||GeoSAN|[Reinhard-Tichy](https://github.com/Reinhard-Tichy)|
 ||HSTLSTM|[Creddittale](https://github.com/Creddittale), [WenMellors](https://github.com/WenMellors)|
+|Estimated Time of Arrival|DeepTTE|[NickHan-cs](https://github.com/NickHan-cs)|
 |Map Matching|STMatching|[XBR-1111](https://github.com/XBR-1111)|
 ||IVMM|[excelsior399](https://github.com/excelsior399)|
+||HMMM|[XBR-1111](https://github.com/XBR-1111)|
 |Road Network Representation Learning|ChebConv|[aptx1231](https://github.com/aptx1231)|
+||LINE|[l782993610](https://github.com/l782993610)|
 
 
 

readme.md

@@ -10,21 +10,22 @@ LibCity is a unified, comprehensive, and extensible library, which provides rese
 
 LibCity currently supports the following tasks:
 
-* Time Series Prediction
 * Traffic State Prediction
   * Traffic Flow Prediction
   * Traffic Speed Prediction
   * On-Demand Service Prediction
-  * OD Matrix Prediction
+  * Origin-destination Matrix Prediction
+  * Traffic Accidents Prediction
 * Trajectory Next-Location Prediction
+* Estimated Time of Arrival
 * Map Matching
 * Road Network Representation Learning
 
 ## Features
 
 * **Unified**: LibCity builds a systematic pipeline to implement, use and evaluate traffic prediction models in a unified platform. We design basic spatial-temporal data storage, unified model instantiation interfaces, and standardized evaluation procedure.
 
-* **Comprehensive**: 54 models covering 8 traffic prediction tasks have been reproduced to form a comprehensive model warehouse. Meanwhile, LibCity collects 32 commonly used datasets of different sources and implements a series of commonly used evaluation metrics and strategies for performance evaluation. 
+* **Comprehensive**: 60 models covering 9 traffic prediction tasks have been reproduced to form a comprehensive model warehouse. Meanwhile, LibCity collects 35 commonly used datasets of different sources and implements a series of commonly used evaluation metrics and strategies for performance evaluation. 
 
 * **Extensible**: LibCity enables a modular design of different components, allowing users to flexibly insert customized components into the library. Therefore, new researchers can easily develop new models with the support of LibCity.
 
@@ -67,13 +68,36 @@ This script will run the GRU model on the METR_LA dataset for traffic state pred
 
 More details is represented in [Docs](https://bigscity-libcity-docs.readthedocs.io/en/latest/get_started/quick_start.html).
 
+## Reproduced Model List
+
+For a list of all models reproduced in LibCity, see [Docs](https://bigscity-libcity-docs.readthedocs.io/en/latest/user_guide/model.html), where you can see the abbreviation of the model and the corresponding papers and citations.
+
+## Tutorial
+
+In order to facilitate users to use LibCity, we provide users with some tutorials:
+
+- We gave lectures on both ACM SIGSPATIAL 2021 Main Track and Local Track. For related lecture videos and Slides, please see our [HomePage](https://libcity.ai/#/tutorial) (Chinese and English).
+- We provide entry-level tutorials (in Chinese and English) in the documentation.
+  - [Install and quick start](https://bigscity-libcity-docs.readthedocs.io/en/latest/tutorial/install_quick_start.html)  & [安装和快速上手](https://bigscity-libcity-docs.readthedocs.io/zh_CN/latest/tutorial/install_quick_start.html)
+  - [Run an existing model in LibCity](https://bigscity-libcity-docs.readthedocs.io/en/latest/tutorial/run_model.html) & [运行LibCity中已复现的模型](https://bigscity-libcity-docs.readthedocs.io/zh_CN/latest/tutorial/run_model.html)
+  - [Add a new model to LibCity](https://bigscity-libcity-docs.readthedocs.io/en/latest/tutorial/add_model.html)  & [在LibCity中添加新模型](https://bigscity-libcity-docs.readthedocs.io/zh_CN/latest/tutorial/add_model.html)
+  - [Tuning the model with automatic tool](https://bigscity-libcity-docs.readthedocs.io/en/latest/tutorial/hyper_tune.html) & [使用自动化工具调参](https://bigscity-libcity-docs.readthedocs.io/zh_CN/latest/tutorial/hyper_tune.html)
+  - [Visualize Atomic Files](https://bigscity-libcity-docs.readthedocs.io/en/latest/tutorial/data_visualization.html) & [原子文件可视化](https://bigscity-libcity-docs.readthedocs.io/zh_CN/latest/tutorial/data_visualization.html)
+- In order to facilitate the use of domestic users in China, we provide an introductory tutorial (in Chinese) on Zhihu.
+  - [LibCity：一个统一、全面、可扩展的交通预测算法库](https://zhuanlan.zhihu.com/p/401186930)
+  - [LibCity入门教程（1）——安装与快速上手](https://zhuanlan.zhihu.com/p/400814990)
+  - [LibCity入门教程（2）——运行LibCity中已复现的模型](https://zhuanlan.zhihu.com/p/400819354)
+  - [LibCity入门教程（3）——在LibCity中添加新模型](https://zhuanlan.zhihu.com/p/400821482)
+  - [LibCity入门教程（4）—— 自动化调参工具](https://zhuanlan.zhihu.com/p/401190615)
+  - [北航BIGSCity课题组提出LibCity工具库：城市时空预测深度学习开源平台](https://zhuanlan.zhihu.com/p/436191860)
+
 ## Contribution
 
 The LibCity is mainly developed and maintained by Beihang Interest Group on SmartCity ([BIGSCITY](https://www.bigcity.ai/)). The core developers of this library are [@aptx1231](https://github.com/aptx1231) and [@WenMellors](https://github.com/WenMellors). 
 
 Several co-developers have also participated in the reproduction of  the model, the list of contributions of which is presented in the [reproduction contribution list](./contribution_list.md).
 
-If you encounter a bug or have any suggestion, please contact us by [raising an issue](https://github.com/LibCity/Bigscity-LibCity/issues).
+If you encounter a bug or have any suggestion, please contact us by [raising an issue](https://github.com/LibCity/Bigscity-LibCity/issues). You can also contact us by sending an email to [email protected].
 
 ## Cite
 

readme_zh.md

@@ -10,21 +10,22 @@ LibCity 是一个统一、全面、可扩展的代码库，为交通预测领域
 
 LibCity 目前支持以下任务：
 
-* 时间序列预测
 * 交通状态预测
   * 交通流量预测
   * 交通速度预测
   * 交通需求预测
-  * OD矩阵预测
+  * 起点-终点（OD）矩阵预测
+  * 交通事故预测
 * 轨迹下一跳预测
+* 到达时间预测
 * 路网匹配
 * 路网表征学习
 
 ## Features
 
 * **统一性**：LibCity 构建了一个系统的流水线以在一个统一的平台上实现、使用和评估交通预测模型。 我们设计了统一的时空数据存储格式、统一的模型实例化接口和标准的模型评估程序。
 
-* **全面性**：复现覆盖 8 个交通预测任务的 54 个模型，形成了全面的模型库。 同时，LibCity 收集了 32 个不同来源的常用数据集，并实现了一系列常用的性能评估指标和策略。
+* **全面性**：复现覆盖 9 个交通预测任务的 60 个模型，形成了全面的模型库。 同时，LibCity 收集了 35 个不同来源的常用数据集，并实现了一系列常用的性能评估指标和策略。
 
 * **可扩展性**：LibCity 实现了不同组件的模块化设计，允许用户灵活地加入自定义组件。 因此，新的研究人员可以在 LibCity 的支持下轻松开发新模型。
 
@@ -65,13 +66,36 @@ python run_model.py --task traffic_state_pred --model GRU --dataset METR_LA
 
 更多细节请访问 [文档](https://bigscity-libcity-docs.readthedocs.io/zh_CN/latest/get_started/quick_start.html) 。
 
+## Reproduced Model List
+
+LibCity 中所复现的全部模型列表见[文档](https://bigscity-libcity-docs.readthedocs.io/en/latest/user_guide/model.html)，在这里你可以看到模型的简称和对应的论文及引用文献。
+
+## Tutorial
+
+为了方便用户使用 LibCity，我们为用户提供了一些入门教程：
+
+- 我们在 ACM SIGSPATIAL 2021 Main Track 以及 Local Track 上都进行了演讲，相关的演讲视频和Slide见我们的[主页](https://libcity.ai/#/tutorial)（中英文）。
+- 我们在文档中提供了入门级教程（中英文）。
+  - [Install and quick start](https://bigscity-libcity-docs.readthedocs.io/en/latest/tutorial/install_quick_start.html)  & [安装和快速上手](https://bigscity-libcity-docs.readthedocs.io/zh_CN/latest/tutorial/install_quick_start.html)
+  - [Run an existing model in LibCity](https://bigscity-libcity-docs.readthedocs.io/en/latest/tutorial/run_model.html) & [运行LibCity中已复现的模型](https://bigscity-libcity-docs.readthedocs.io/zh_CN/latest/tutorial/run_model.html)
+  - [Add a new model to LibCity](https://bigscity-libcity-docs.readthedocs.io/en/latest/tutorial/add_model.html)  & [在LibCity中添加新模型](https://bigscity-libcity-docs.readthedocs.io/zh_CN/latest/tutorial/add_model.html)
+  - [Tuning the model with automatic tool](https://bigscity-libcity-docs.readthedocs.io/en/latest/tutorial/hyper_tune.html) & [使用自动化工具调参](https://bigscity-libcity-docs.readthedocs.io/zh_CN/latest/tutorial/hyper_tune.html)
+  - [Visualize Atomic Files](https://bigscity-libcity-docs.readthedocs.io/en/latest/tutorial/data_visualization.html) & [原子文件可视化](https://bigscity-libcity-docs.readthedocs.io/zh_CN/latest/tutorial/data_visualization.html)
+- 为了便于国内用户使用，我们在知乎上提供了入门教程（中文）。
+  - [LibCity：一个统一、全面、可扩展的交通预测算法库](https://zhuanlan.zhihu.com/p/401186930)
+  - [LibCity入门教程（1）——安装与快速上手](https://zhuanlan.zhihu.com/p/400814990)
+  - [LibCity入门教程（2）——运行LibCity中已复现的模型](https://zhuanlan.zhihu.com/p/400819354)
+  - [LibCity入门教程（3）——在LibCity中添加新模型](https://zhuanlan.zhihu.com/p/400821482)
+  - [LibCity入门教程（4）—— 自动化调参工具](https://zhuanlan.zhihu.com/p/401190615)
+  - [北航BIGSCity课题组提出LibCity工具库：城市时空预测深度学习开源平台](https://zhuanlan.zhihu.com/p/436191860)
+
 ## Contribution
 
 LibCity 主要由北航智慧城市兴趣小组 ([BIGSCITY](https://www.bigcity.ai/)) 开发和维护。 该库的核心开发人员是 [@aptx1231](https://github.com/aptx1231) 和 [@WenMellors](https://github.com/WenMellors)。
 
 若干共同开发者也参与了模型的复现，其贡献列表在 [贡献者列表](./contribution_list.md) 。
 
-如果您遇到错误或有任何建议，请通过以下方式与我们联系： [提交issue](https://github.com/LibCity/Bigscity-LibCity/issues)。
+如果您遇到错误或有任何建议，请通过 [提交issue](https://github.com/LibCity/Bigscity-LibCity/issues) 的方式与我们联系。您也可以通过发送邮件的方式联系我们，邮箱为[email protected]。
 
 ## Cite