diff --git a/机器学习竞赛实战_优胜解决方案/智慧城市-道路通行时间预测/.ipynb_checkpoints/道路通行时间预测-checkpoint.ipynb b/机器学习竞赛实战_优胜解决方案/智慧城市-道路通行时间预测/.ipynb_checkpoints/道路通行时间预测-checkpoint.ipynb
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+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/机器学习竞赛实战_优胜解决方案/智慧城市-道路通行时间预测/道路通行时间预测.ipynb b/机器学习竞赛实战_优胜解决方案/智慧城市-道路通行时间预测/道路通行时间预测.ipynb
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+++ b/机器学习竞赛实战_优胜解决方案/智慧城市-道路通行时间预测/道路通行时间预测.ipynb
@@ -0,0 +1,374 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 数据展示"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from scipy.interpolate import UnivariateSpline\n",
+    "from sklearn import linear_model\n",
+    "import xgboost as xgb\n",
+    "# from ultis import *"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "道路通行时间："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>link_ID</th>\n",
+       "      <th>date</th>\n",
+       "      <th>time_interval</th>\n",
+       "      <th>travel_time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4377906283422600514</td>\n",
+       "      <td>2017-05-06</td>\n",
+       "      <td>[2017-05-06 11:04:00,2017-05-06 11:06:00)</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3377906289434510514</td>\n",
+       "      <td>2017-05-06</td>\n",
+       "      <td>[2017-05-06 10:42:00,2017-05-06 10:44:00)</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3377906285934510514</td>\n",
+       "      <td>2017-05-06</td>\n",
+       "      <td>[2017-05-06 11:56:00,2017-05-06 11:58:00)</td>\n",
+       "      <td>35.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3377906285934510514</td>\n",
+       "      <td>2017-05-06</td>\n",
+       "      <td>[2017-05-06 17:46:00,2017-05-06 17:48:00)</td>\n",
+       "      <td>26.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3377906287934510514</td>\n",
+       "      <td>2017-05-06</td>\n",
+       "      <td>[2017-05-06 10:52:00,2017-05-06 10:54:00)</td>\n",
+       "      <td>10.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               link_ID        date                              time_interval  \\\n",
+       "0  4377906283422600514  2017-05-06  [2017-05-06 11:04:00,2017-05-06 11:06:00)   \n",
+       "1  3377906289434510514  2017-05-06  [2017-05-06 10:42:00,2017-05-06 10:44:00)   \n",
+       "2  3377906285934510514  2017-05-06  [2017-05-06 11:56:00,2017-05-06 11:58:00)   \n",
+       "3  3377906285934510514  2017-05-06  [2017-05-06 17:46:00,2017-05-06 17:48:00)   \n",
+       "4  3377906287934510514  2017-05-06  [2017-05-06 10:52:00,2017-05-06 10:54:00)   \n",
+       "\n",
+       "   travel_time  \n",
+       "0          3.0  \n",
+       "1          1.0  \n",
+       "2         35.2  \n",
+       "3         26.2  \n",
+       "4         10.4  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('new_gy_contest_traveltime_training_data_second.txt',delimiter=';',dtype={'link_ID':object})\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "time_interval时间间隔，两分钟为单位\n",
+    "\n",
+    "travel_time平均通行时间"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "道理长宽情况："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>link_ID</th>\n",
+       "      <th>length</th>\n",
+       "      <th>width</th>\n",
+       "      <th>link_class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4377906289869500514</td>\n",
+       "      <td>57</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4377906284594800514</td>\n",
+       "      <td>247</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4377906289425800514</td>\n",
+       "      <td>194</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4377906284525800514</td>\n",
+       "      <td>839</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4377906284422600514</td>\n",
+       "      <td>55</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               link_ID  length  width  link_class\n",
+       "0  4377906289869500514      57      3           1\n",
+       "1  4377906284594800514     247      9           1\n",
+       "2  4377906289425800514     194      3           1\n",
+       "3  4377906284525800514     839      3           1\n",
+       "4  4377906284422600514      55     12           1"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "link_df = pd.read_csv('gy_contest_link_info.txt',delimiter=';',dtype={'link_ID':object})\n",
+    "link_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "length长度    width宽度    link_class类别"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "道路之间连接情况："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>link_ID</th>\n",
+       "      <th>in_links</th>\n",
+       "      <th>out_links</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4377906289869500514</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4377906284594800514</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4377906289425800514</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4377906284525800514</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4377906284422600514</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               link_ID  in_links  out_links\n",
+       "0  4377906289869500514         1          1\n",
+       "1  4377906284594800514         1          1\n",
+       "2  4377906289425800514         1          1\n",
+       "3  4377906284525800514         1          1\n",
+       "4  4377906284422600514         2          1"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "link_tops = pd.read_csv('gy_contest_link_top_update.txt',delimiter=',',dtype={'link_ID':object})\n",
+    "link_tops.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 任务：预测未来一个月平均通行结果，每两分钟一次\n",
+    "回归任务\n",
+    "\n",
+    "构建时间序列，基于前几天或者前几十天的数据预测"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}