{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Введение в анализ данных\n", "\n", "## Парадокс времени ожидания: а что в реальной жизни?\n", "\n", "В предыдущем примере мы посмотрели на то, какие эффекты возникают в случае если автобусы прибывают на остановку с одинаковой интенсивностью независимо друг от друга. Выполняется ли это в реальности?\n", "\n", "Исследуем данные о запланированном и фактическом времени прибытия для автобусов RapidRide маршрутов C, D и E на автобусной остановке 3rd&Pike в центре Сиэтла. Ниже приведена схема общественного транспорта Сиэтла, на которой красными линиями отмечены рассматриваемые автобусные маршруты.\n", "\n", "Сами данные и идея исследования взяты из статьи на Хабре.\n", "\n", "\"Схема" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:28.784731Z", "start_time": "2021-03-20T13:00:27.516016Z" } }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from random import choices, shuffle\n", "import scipy.stats as sps\n", "import matplotlib.pyplot as plt\n", "\n", "import seaborn as sns\n", "sns.set(style='whitegrid', font_scale=1.3, palette='Set2')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Загрузим данные с помощью библиотеки `pandas`, с которой вы уже должны были познакомиться. Напечатаем также несколько первых строк таблицы. В данных есть следующая информация:\n", "* `OPD_DATE` — дата события;\n", "* `VEHICLE_ID` — номер транспортного средства;\n", "* `RTE` — номер маршрута;\n", "* `DIR` — направление движения (на север или на юг);\n", "* `TRIP_ID` — идентификатор рейса;\n", "* `STOP_ID` — идентификатор остановки;\n", "* `STOP_NAME` — наименование остановки;\n", "* `SCH_STOP_TM` — время прибытия по расписанию (**sch**edule);\n", "* `ACT_STOP_TM` — время прибытия по факту (**act**ual);" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:28.893860Z", "start_time": "2021-03-20T13:00:28.786642Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " OPD_DATE VEHICLE_ID RTE DIR TRIP_ID STOP_ID \\\n", "0 2016-03-26 6201 673 S 30908177 431 \n", "1 2016-03-26 6201 673 S 30908033 431 \n", "2 2016-03-26 6201 673 S 30908028 431 \n", "3 2016-03-26 6201 673 S 30908019 431 \n", "4 2016-03-26 6201 673 S 30908252 431 \n", "\n", " STOP_NAME SCH_STOP_TM ACT_STOP_TM \n", "0 3RD AVE & PIKE ST (431) 01:11:57 01:13:19 \n", "1 3RD AVE & PIKE ST (431) 23:19:57 23:16:13 \n", "2 3RD AVE & PIKE ST (431) 21:19:57 21:18:46 \n", "3 3RD AVE & PIKE ST (431) 19:04:57 19:01:49 \n", "4 3RD AVE & PIKE ST (431) 16:42:57 16:42:39 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('arrival_times.csv')\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Обработка и первичный анализ данных" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "В данных всего 39 157 записей" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:28.899963Z", "start_time": "2021-03-20T13:00:28.895571Z" } }, "outputs": [ { "data": { "text/plain": [ "(39157, 9)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Проверим, есть ли пропуски" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:28.926792Z", "start_time": "2021-03-20T13:00:28.901500Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " OPD_DATE VEHICLE_ID RTE DIR TRIP_ID STOP_ID STOP_NAME \\\n", "0 False False False False False False False \n", "1 False False False False False False False \n", "2 False False False False False False False \n", "3 False False False False False False False \n", "4 False False False False False False False \n", "... ... ... ... ... ... ... ... \n", "39152 False False False False False False False \n", "39153 False False False False False False False \n", "39154 False False False False False False False \n", "39155 False False False False False False False \n", "39156 False False False False False False False \n", "\n", " SCH_STOP_TM ACT_STOP_TM \n", "0 False False \n", "1 False False \n", "2 False False \n", "3 False False \n", "4 False False \n", "... ... ... \n", "39152 False False \n", "39153 False False \n", "39154 False False \n", "39155 False False \n", "39156 False False \n", "\n", "[39157 rows x 9 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isna()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "На первый взгляд не видно. Посчитаем долю пропусков по столбцам, усреднив логические константы. В данном случае сначала произошло преобразование к 0 и 1." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:28.952108Z", "start_time": "2021-03-20T13:00:28.928655Z" } }, "outputs": [ { "data": { "text/plain": [ "OPD_DATE 0.000000\n", "VEHICLE_ID 0.000000\n", "RTE 0.000000\n", "DIR 0.000000\n", "TRIP_ID 0.000000\n", "STOP_ID 0.000000\n", "STOP_NAME 0.000000\n", "SCH_STOP_TM 0.006129\n", "ACT_STOP_TM 0.000000\n", "dtype: float64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isna().mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Теперь уже видно, что для 0.6% рейсов не задано время прибытия.\n", "\n", "Посмотрим, что это за рейсы. Для этого сначала с помощью функции `any` определим для каждого рейса, есть для него *хотя бы один* пропуск." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:28.968451Z", "start_time": "2021-03-20T13:00:28.953994Z" } }, "outputs": [ { "data": { "text/plain": [ "0 False\n", "1 False\n", "2 False\n", "3 False\n", "4 False\n", " ... \n", "39152 False\n", "39153 False\n", "39154 False\n", "39155 False\n", "39156 False\n", "Length: 39157, dtype: bool" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isna().any(axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Теперь мы можем извлеть все строки таблицы с пропусками." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:29.004313Z", "start_time": "2021-03-20T13:00:28.970940Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " OPD_DATE VEHICLE_ID RTE DIR TRIP_ID STOP_ID \\\n", "383 2016-04-01 6206 673 S 0 431 \n", "471 2016-03-31 6207 673 S 0 431 \n", "588 2016-03-30 6208 673 S 0 431 \n", "599 2016-03-30 6200 673 S 0 431 \n", "685 2016-03-29 6213 673 S 0 431 \n", "... ... ... ... .. ... ... \n", "37327 2016-05-10 6215 674 N 0 578 \n", "37328 2016-05-10 6215 674 N 0 578 \n", "37570 2016-05-12 6218 674 N 0 578 \n", "37670 2016-05-13 6212 674 N 0 578 \n", "39107 2016-05-27 6099 674 N 0 578 \n", "\n", " STOP_NAME SCH_STOP_TM ACT_STOP_TM \n", "383 3RD AVE & PIKE ST (431) NaN 16:00:57 \n", "471 3RD AVE & PIKE ST (431) NaN 15:28:05 \n", "588 3RD AVE & PIKE ST (431) NaN 14:52:36 \n", "599 3RD AVE & PIKE ST (431) NaN 15:12:06 \n", "685 3RD AVE & PIKE ST (431) NaN 17:56:29 \n", "... ... ... ... \n", "37327 3RD AVE & PIKE ST (578) NaN 15:25:47 \n", "37328 3RD AVE & PIKE ST (578) NaN 17:44:05 \n", "37570 3RD AVE & PIKE ST (578) NaN 18:27:55 \n", "37670 3RD AVE & PIKE ST (578) NaN 19:04:25 \n", "39107 3RD AVE & PIKE ST (578) NaN 14:51:34 \n", "\n", "[240 rows x 9 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df.isna().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Давайте удали пропуски из данных, чтобы они не мешали нашему дальнейшему анализу. Параметры функции предписывают удалять *строки*, в которых есть *хотя бы один* пропуск в любой из колонок." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:29.028490Z", "start_time": "2021-03-20T13:00:29.006728Z" } }, "outputs": [], "source": [ "df = df.dropna(axis=0, how='any')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Посмотрим на диаппазон дат в датасете. Всего у нас есть данные за 2 месяца — с 26 марта 2016 по 27 мая 2016." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:29.039930Z", "start_time": "2021-03-20T13:00:29.030168Z" } }, "outputs": [ { "data": { "text/plain": [ "('2016-03-26', '2016-05-27')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['OPD_DATE'].min(), df['OPD_DATE'].max()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Уникальные идентификаторы автобусных маршрутов" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:29.046247Z", "start_time": "2021-03-20T13:00:29.041998Z" } }, "outputs": [ { "data": { "text/plain": [ "array([673, 675, 674])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['RTE'].unique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Направления движения" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:29.069411Z", "start_time": "2021-03-20T13:00:29.047863Z" } }, "outputs": [ { "data": { "text/plain": [ "array(['S', 'N'], dtype=object)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['DIR'].unique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Идентификаторы автобусных остановок" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:29.078389Z", "start_time": "2021-03-20T13:00:29.070771Z" } }, "outputs": [ { "data": { "text/plain": [ "array([431, 578])" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['STOP_ID'].unique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Как видим остановки две. Если мы составим с помощью функции `pd.crosstab` таблицу, в которой показано, сколько рейсов было на каждой остановке по каждому из направлений, то мы сможем догадаться, что два идентификатора означаю одну и ту же остановку по разные стороны движения." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:29.113705Z", "start_time": "2021-03-20T13:00:29.079811Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "DIR N S\n", "STOP_ID \n", "431 0 19318\n", "578 19599 0" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.crosstab(columns=df['DIR'], index=df['STOP_ID'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Также с помощью этой функции мы можем составить таблицу с количеством автобусов каждого маршрута за каждые сутки" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:29.147196Z", "start_time": "2021-03-20T13:00:29.115484Z" } }, "outputs": [ { "data": { "text/html": [ "
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\n", "
" ], "text/plain": [ "RTE 673 674 675\n", "OPD_DATE \n", "2016-03-26 176 178 159\n", "2016-03-27 153 153 140\n", "2016-03-28 232 227 219\n", "2016-03-29 231 231 221\n", "2016-03-30 228 230 221\n", "... ... ... ...\n", "2016-05-23 229 230 220\n", "2016-05-24 233 229 220\n", "2016-05-25 232 231 221\n", "2016-05-26 229 203 220\n", "2016-05-27 230 229 222\n", "\n", "[63 rows x 3 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "daily_counts = pd.crosstab(columns=df['RTE'], index=df['OPD_DATE'])\n", "daily_counts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "И построить с ее помощью график автобусов за день по маршрутам. На этом графике мы четко наблюдаем некоторую периодичность, которую называют **недельной сезонностью**. Действительно, мы видим, что 7-дневные интервалы, из которых в течении пяти дней наблюдается достаточно большое количество автобусов по каждому из маршрутов, а в остальные два дня — существенно меньше.\n", "\n", "В целом стоит отметить, что исследование сезонностей в данных — важная часть работы с временн*ы*ми данными." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:29.406236Z", "start_time": "2021-03-20T13:00:29.148731Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.8/dist-packages/pandas/plotting/_matplotlib/core.py:1192: UserWarning: FixedFormatter should only be used together with FixedLocator\n", " ax.set_xticklabels(xticklabels)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with sns.axes_style('darkgrid'):\n", " daily_counts.plot(figsize=(15, 5), lw=2);\n", " plt.ylabel('Количество автобусов')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Для удобства дальнейшего анализа объединим дату и время в одну метку времени." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:29.465146Z", "start_time": "2021-03-20T13:00:29.407983Z" } }, "outputs": [], "source": [ "df['scheduled'] = pd.to_datetime(df['OPD_DATE'] + ' ' + df['SCH_STOP_TM'])\n", "df['actual'] = pd.to_datetime(df['OPD_DATE'] + ' ' + df['ACT_STOP_TM'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Однако, такой операцией мы могли привнести ошибки в данные.\n", "\n", "Давайте посчитаем для каждого автобуса время между прибытием по расписанию и фактическим временем прибытия." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:29.471018Z", "start_time": "2021-03-20T13:00:29.466722Z" } }, "outputs": [], "source": [ "minute = np.timedelta64(1, 'm')\n", "hour = 60 * minute\n", "\n", "diff_hrs = (df['actual'] - df['scheduled']) / hour" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Посмотрим на гистограмму" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:30.268912Z", "start_time": "2021-03-20T13:00:29.472607Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12, 5))\n", "plt.hist(diff_hrs, bins=48, log=True)\n", "plt.xticks(range(-24, 25, 3))\n", "plt.xlabel('Отклонения от расп., час')\n", "plt.ylabel('Кол-во автобусов');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Оказывается, что несколько автобусов приезжали на сутки раньше, а еще несколько — на сутки позже. Как такое могло произойти?\n", "\n", "Вспомним, что мы искусственно склеили дату с временем суток. У нас имеется две отметки времени суток для каждого рейса, но дата одна, и она соответствует времени прибытия по расписанию. Таким образом, возможны две следующие ситуации.\n", "* Время прибытия по расписанию — сразу после полуночи, а автобус пришел немного раньше. Тем самым при склейке даты и времени мы получили, что как будто он пришел на сутки позже.\n", "* Время прибытия по расписанию — непосредственно до полуночи, а автобус немного опоздал. И при склейке даты и времени мы получили, что как будто он пришел на сутки раньше.\n", "\n", "Исправим фактическое время прибытия руками для тех рейсов, где отклонение по модулю слишком большое." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:30.279022Z", "start_time": "2021-03-20T13:00:30.270405Z" } }, "outputs": [], "source": [ "df.loc[diff_hrs > 20, 'actual'] -= 24 * hour\n", "df.loc[diff_hrs < -20, 'actual'] += 24 * hour" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "И теперь посчитаем время отклонения факта от расписания в минутах" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:30.293810Z", "start_time": "2021-03-20T13:00:30.280411Z" } }, "outputs": [], "source": [ "df['minutes_late'] = (df['actual'] - df['scheduled']) / minute" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Переобозначим технические идентификаторы маршрутов на те, которые видят пассажиры. Также переобозначим направления движения." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:30.323484Z", "start_time": "2021-03-20T13:00:30.301798Z" } }, "outputs": [], "source": [ "df['route'] = df['RTE'].replace({673: 'C', 674: 'D', 675: 'E'}).astype('category')\n", "df['direction'] = df['DIR'].replace({'N': 'север', 'S': 'юг'}).astype('category')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Оставим только интересующие нас столбцы" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:30.332880Z", "start_time": "2021-03-20T13:00:30.327792Z" } }, "outputs": [], "source": [ "df = df[['route', 'direction', 'scheduled', 'actual', 'minutes_late']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "И посмотрим на данные" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:30.350356Z", "start_time": "2021-03-20T13:00:30.335548Z" } }, "outputs": [ { "data": { "text/html": [ "
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4Cюг2016-03-26 16:42:572016-03-26 16:42:39-0.300000
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" ], "text/plain": [ " route direction scheduled actual minutes_late\n", "0 C юг 2016-03-26 01:11:57 2016-03-26 01:13:19 1.366667\n", "1 C юг 2016-03-26 23:19:57 2016-03-26 23:16:13 -3.733333\n", "2 C юг 2016-03-26 21:19:57 2016-03-26 21:18:46 -1.183333\n", "3 C юг 2016-03-26 19:04:57 2016-03-26 19:01:49 -3.133333\n", "4 C юг 2016-03-26 16:42:57 2016-03-26 16:42:39 -0.300000" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Исследование отклонения от расписания\n", "\n", "Для каждого направления и каждого маршрута визуализируем гистограмму отклонения от расписания в минутах.\n", "\n", "Визуализацию выполним в виде таблицы с помощью класса `FacetGrid`. Работа функции несколько похожа на функцию `pd.pivot_table`, только вместо применения аггрегирующией функции рисуется график. Функция построения каждого графика передается в метод `map`." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:32.188849Z", "start_time": "2021-03-20T13:00:30.353699Z" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g = sns.FacetGrid(df, row=\"direction\", col=\"route\", height=3, aspect=2)\n", "g.map(plt.hist, \"minutes_late\", bins=np.arange(-10, 15))\n", "g.set_titles('{col_name} {row_name}')\n", "g.set_axis_labels('Отклонения от расп., мин.', 'Кол-во автобусов');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Заметим, что **автобусы ближе к графику в начале маршрута и больше отклоняются от него к концу**. Если внимательно посмотреть на схему маршрутов, то можно увидеть, что исследуемая остановка находится на северной части маршрута С, и на южных частях машрутов D и Е. Тем самым, при следовании по маршруту C на юг наша остановка близка к началу маршрута, тем самым отклонения от расписания в основном небольшие. Аналогично с маршрутами D и E при движении на север. В остальных случаях наблюдается больший разброс отклонений.\n", "\n", "\n", "Однако нам хотелось бы еще посмотреть на интервалы между автобусами. Сначала посчитаем интервалы между автобусами по расписанию и фактические." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:32.239348Z", "start_time": "2021-03-20T13:00:32.190404Z" } }, "outputs": [], "source": [ "def compute_headway(times):\n", " \"\"\" Вычисляет интервалы между соседними моментами времени в минутах. \"\"\"\n", " minute = np.timedelta64(1, 'm')\n", " return times.sort_values().diff() / minute\n", "\n", "# сгруппируем по маршруту и по направлению\n", "grouped = df.groupby(['route', 'direction'])\n", "# для каждой группы посчитаем интервалы определенной выше функцией\n", "df['actual_interval'] = grouped['actual'].transform(compute_headway)\n", "df['scheduled_interval'] = grouped['scheduled'].transform(compute_headway)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Визуализируем фактические интервалы между автобусами" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:34.446410Z", "start_time": "2021-03-20T13:00:32.242525Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g = sns.FacetGrid(df.dropna(), row=\"direction\", col=\"route\", height=3, aspect=2)\n", "g.map(plt.hist, \"actual_interval\", bins=np.arange(40))\n", "g.set_titles('{col_name} {row_name}')\n", "g.set_axis_labels('Актуальные интервалы, мин.', 'Кол-во автобусов');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Видим, что **фактические интервалы даже по каждому отдельному маршруту, видимо, не подчиняются экспоненциальному распределению.**\n", "\n", "Однако, не все так просто. В прошлый раз мы рассматривали случай, при котором автобусы приходят на остановку независимо друг от друга с одинаковой интенсивностью. Но это не так, для этого достаточно посмотреть на графики временных рядов выше. Как мы ранее заметили, в выходные дни автобусы ходят реже. Кроме того, наверняка в час пик автобусы ходят чаще, чем в вечерне-ночное время.\n", "\n", "Посмотрим на интервалы между автобусами по расписанию" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:36.686019Z", "start_time": "2021-03-20T13:00:34.447983Z" } }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g = sns.FacetGrid(df.dropna(), row=\"direction\", col=\"route\", height=3, aspect=2)\n", "g.map(plt.hist, \"scheduled_interval\", bins=np.arange(40))\n", "g.set_titles('{col_name} {row_name}')\n", "g.set_axis_labels('Интервалы по расписанию, мин.', 'Кол-во автобусов');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Тут мы уже видим, что по расписанию есть **много разных значений запланированных интервалов между автобусами.**\n", "\n", "### 3. Исследование для однородных ожидаемых интервалов\n", "\n", "Тем не менее, есть частые интервалы по расписанию: 10, 12 и 15 минут. Например, есть почти 2000 автобусов в северную сторону с запланированным интервалом в 10 минут.\n", "Давайте посмотрим внимательнее на эти интервалы.\n", "\n", "Составим условие выбора строк из таблицы" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:36.706598Z", "start_time": "2021-03-20T13:00:36.697668Z" } }, "outputs": [ { "data": { "text/plain": [ "0 False\n", "1 False\n", "2 False\n", "3 False\n", "4 False\n", " ... \n", "39152 True\n", "39153 True\n", "39154 True\n", "39155 True\n", "39156 True\n", "Name: scheduled_interval, Length: 38917, dtype: bool" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['scheduled_interval'].isin([10, 12, 15])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Составим подтаблицу с помощью данного условия" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:36.722856Z", "start_time": "2021-03-20T13:00:36.708119Z" } }, "outputs": [], "source": [ "subset = df[df['scheduled_interval'].isin([10, 12, 15])]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Сгруппируем по маршруту, по направлению и по интервалам среди выбранных интервалов." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:36.735267Z", "start_time": "2021-03-20T13:00:36.724418Z" } }, "outputs": [], "source": [ "grouped = subset.groupby(['route', 'direction', 'scheduled_interval'])" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:36.744097Z", "start_time": "2021-03-20T13:00:36.737872Z" } }, "outputs": [], "source": [ "def stack_sequence(data):\n", " \"\"\" Перераспределение времени так, как будто события происходят последовательно. \"\"\"\n", " \n", " # сортируем по ожидаемому времени прибытия\n", " data = data.sort_values('scheduled')\n", " # переопределим время прибытия так, как будто они шли подряд\n", " data['scheduled'] = data['scheduled_interval'].cumsum()\n", " # соответствующе переопределим фактическое время на основе имеющегося отклонения\n", " data['actual'] = data['scheduled'] + data['minutes_late']\n", " # посчитаем фактические интервалы по скорректированному фактическому времени\n", " data['actual_interval'] = data['actual'].sort_values().diff()\n", " return data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Применим эту функцию к нашим группам" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:37.539460Z", "start_time": "2021-03-20T13:00:36.749103Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " route direction scheduled actual minutes_late actual_interval \\\n", "0 C север 10.0 12.400000 2.400000 NaN \n", "1 C север 20.0 27.150000 7.150000 0.183333 \n", "2 C север 30.0 26.966667 -3.033333 14.566667 \n", "3 C север 40.0 35.516667 -4.483333 8.366667 \n", "4 C север 50.0 53.583333 3.583333 18.066667 \n", "\n", " scheduled_interval \n", "0 10.0 \n", "1 10.0 \n", "2 10.0 \n", "3 10.0 \n", "4 10.0 " ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sequenced = grouped.apply(stack_sequence).reset_index(drop=True)\n", "sequenced.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Теперь визуализируем все отклонения от расписания в рамках выбранного интервала." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:43.192797Z", "start_time": "2021-03-20T13:00:37.541026Z" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for route in ['C', 'D', 'E']:\n", " g = sns.FacetGrid(sequenced.query(f\"route == '{route}'\"),\n", " row=\"direction\", col=\"scheduled_interval\", height=3, aspect=2)\n", " g.map(plt.hist, \"actual_interval\", bins=np.arange(40) + 0.5)\n", " g.set_titles('{row_name} ({col_name:.0f} мин.)')\n", " g.set_axis_labels('Актуальные интервалы, мин.', 'Кол-во автобусов');\n", " g.fig.suptitle(f'{route} line', y=1.05, fontsize=28)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Видим, что для каждого маршрута и направления при фиксированном интервале **распределение фактических интервалов близко к нормальному**. Оно достигает максимума около запланированного интервала и имеет стандартное отклонение (корень из дисперсии), которое меньше в начале маршрута (на юг для C, на север для D/E) и больше в конце. \n", "\n", "Также видно, что фактические интервалы прибытия определенно не соответствуют экспоненциальному распределению, что является основным предположением, на котором основан парадокс времени ожидания.\n", "\n", "### 4. Симуляция пассажиров\n", "\n", "Теперь оценим, сколько в среднем придется ждать пассажиру автобуса при данных интервалах движения. Для этого оформим код с предыдущего ноутбука в специальную функцию." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:43.197773Z", "start_time": "2021-03-20T13:00:43.194420Z" } }, "outputs": [], "source": [ "def simulate_wait_times(bus_arrival_times, n_passengers=1000000):\n", " \"\"\"\n", " Моделирование времени ожидание пассажиров.\n", " \n", " bus_arrival_times -- моменты времени прибытия автобусов.\n", " n_passengers -- количество пассажиров для семплирования.\n", " \"\"\"\n", " \n", " bus_arrival_times = np.array(bus_arrival_times)\n", "\n", " # сгенерируем для каждого пассажира время его прибытия на остановку\n", " passenger_times = sps.uniform(scale=bus_arrival_times.max()).rvs(size=n_passengers)\n", " # найдем время прибытия следующего автобуса поиском по отсортированному массиву\n", " i = np.searchsorted(bus_arrival_times, passenger_times, side='right')\n", " # вычислим интервал ожидания\n", " wait_times = bus_arrival_times[i] - passenger_times\n", "\n", " return wait_times" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Произведем семплирование и посчитаем интервалы вместе с наиболее вероятным диаппазоном, считая, что мы работаем с нормальным распределением. Как мы помним, интервал, определяемый среднем и двумя стандартными отклонениями содержит чуть более 95% вероятностной массы." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "ExecuteTime": { "end_time": "2021-03-20T13:00:45.381019Z", "start_time": "2021-03-20T13:00:43.199355Z" } }, "outputs": [ { "data": { "text/html": [ "
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actual
routedirectionscheduled_interval
Cсевер10.07.8 +/- 25.3
12.07.4 +/- 11.3
15.08.8 +/- 12.9
юг10.06.3 +/- 12.7
12.06.8 +/- 10.4
15.08.4 +/- 14.5
Dсевер10.06.1 +/- 14.3
12.06.5 +/- 9.3
15.07.9 +/- 10.6
юг10.06.7 +/- 10.6
12.07.5 +/- 11.8
15.08.8 +/- 12.9
Eсевер10.05.5 +/- 7.5
12.06.5 +/- 8.5
15.07.9 +/- 9.8
юг10.06.7 +/- 11.2
12.07.3 +/- 10.4
15.08.7 +/- 12.0
\n", "
" ], "text/plain": [ " actual\n", "route direction scheduled_interval \n", "C север 10.0 7.8 +/- 25.3\n", " 12.0 7.4 +/- 11.3\n", " 15.0 8.8 +/- 12.9\n", " юг 10.0 6.3 +/- 12.7\n", " 12.0 6.8 +/- 10.4\n", " 15.0 8.4 +/- 14.5\n", "D север 10.0 6.1 +/- 14.3\n", " 12.0 6.5 +/- 9.3\n", " 15.0 7.9 +/- 10.6\n", " юг 10.0 6.7 +/- 10.6\n", " 12.0 7.5 +/- 11.8\n", " 15.0 8.8 +/- 12.9\n", "E север 10.0 5.5 +/- 7.5\n", " 12.0 6.5 +/- 8.5\n", " 15.0 7.9 +/- 9.8\n", " юг 10.0 6.7 +/- 11.2\n", " 12.0 7.3 +/- 10.4\n", " 15.0 8.7 +/- 12.0" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# сгруппируем по маршрутам, направлениям и интервалам\n", "grouped = sequenced.groupby(['route', 'direction', 'scheduled_interval'])\n", "# применим семплирование\n", "simulated = grouped['actual'].apply(simulate_wait_times)\n", "\n", "# посчитаем и напечатаем интервалы\n", "simulated = simulated.apply(lambda times: \"{0:.1f} +/- {1:.1f}\".format(times.mean(), 2*times.std()))\n", "pd.DataFrame(simulated)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Видим, что среднее время ожидания, возможно, на минуту или две больше половины интервала расписанию, но не равно этому интервалу, как подразумевает парадокс времени ожидания. Другими словами, **парадокс инспекции подтвержден, но парадокс времени ожидания не соответствует действительности.**\n", "\n", "**Вывод.** \n", "\n", "Мы подтвердили, что в реальном мире автобусные маршруты подчиняются некоторой разновидности парадокса инспекции, однако основное предположение, лежащее в основе парадокса времени ожидания, — что прибытие автобусов происходит независимо друг от друга и с одинаковой интенсивностью — не является действительным.\n", "\n", "На самом же деле в хорошо управляемой системе общественного транспорта есть специально структурированные расписания, чтобы избежать такого поведения: автобусы не начинают свои маршруты в случайное время в течение дня, а стартуют по расписанию, выбранному для наиболее эффективной перевозки пассажиров." ] } ], "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 4 }