Road safety measures analysis Kyiv.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Аналіз ефективності заходів безпеки дорожнього руху в Києві"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Спочатку скачуємо і відкриваємо датасет з аваріями (він взятий [звідси](https://docs.google.com/spreadsheets/d/1Dk-oB-lXYNFzSMGUtO4iSUnYMWmLQnH9/edit#gid=291616867)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20067 entries, 0 to 20066\n",
      "Data columns (total 15 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   id               8168 non-null   float64\n",
      " 1   date             20067 non-null  object \n",
      " 2   time             20067 non-null  object \n",
      " 3   district         18812 non-null  object \n",
      " 4   street           20067 non-null  object \n",
      " 5   xtra             17335 non-null  object \n",
      " 6   type             20067 non-null  object \n",
      " 7   person           20067 non-null  object \n",
      " 8   injury           20067 non-null  object \n",
      " 9   dma_street_code  4314 non-null   float64\n",
      " 10  dma_str_house    1391 non-null   object \n",
      " 11  dma_lat          757 non-null    float64\n",
      " 12  dma_lng          757 non-null    float64\n",
      " 13  drv_street_code  4062 non-null   float64\n",
      " 14  full_address     1391 non-null   object \n",
      "dtypes: float64(5), object(10)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv('DTP Kyiv 2016-2019.csv')\n",
    "data.info()   #узагальнений опис даних, які є в таблиці"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Бачимо, що за три роки і п'ять місяців (від початку серпня $2016$ до кінця $2019$) поліція зафіксувала $8168$ аварій з $20067$ учасниками. Всі записи в таблиці містять інформацію про дату, час, вулицю, тип учасника (водій, пішохід тощо) та ушкодження. Це добре. В той же час, поле _xtra_ відсутнє у трохи менше ніж $3000$ записах, і це не дуже добре оскільки це означає, що точну адресу цих ДТП встановити не вдасться. Всі наступні поля рідко бувають заповненими і не дають нової інформації, давайте видалимо їх. Також видалимо не дуже потрібне поле _district_ і додамо поле _year_ для зручності:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Без ушкоджень',\n",
       " 'Легко травмований',\n",
       " 'Помер на місці ДТП',\n",
       " 'Помер по дорозі в лікарню',\n",
       " 'Помер у лікарні протягом 30 діб',\n",
       " 'Тяжко травмований'}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(columns = ['district', 'dma_street_code', 'dma_str_house', 'dma_lat',\n",
    "                     'dma_lng', 'drv_street_code', 'full_address'], inplace = True)\n",
    "data['year'] = data.apply (lambda row: int(row['date'][-4:]), axis=1)\n",
    "\n",
    "set(data['injury'].values)   #як саме виглядають описи ушкоджень?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Задротська частина"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ми будемо бачити різну кількість ДТП до та після запровадження певного рішення. Але як переконатися, що зміна кількості ДТП - не випадковість? Для цього потрібно перевірити статистичну гіпотезу про те, що ймовірності ДТП до та після дійсно різні. Відомо, що за фіксованих умов час очікування наступного ДТП дуже добре моделюється [експоненційним розподілом](https://en.wikipedia.org/wiki/Exponential_distribution) (але кількість постраждалих - ні, саме тому при перевірці гіпотези ми дивитимемося на кількість ДТП, але виводити будемо кількість постраждалих як більш важливий показник). Будемо перевіряти, чи не перетинаються $90\\%$ - довірчі інтервали для матсподівання, відповідну формулу можна знайти [тут](https://en.wikipedia.org/wiki/Exponential_distribution#Confidence_intervals)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import chi2\n",
    "\n",
    "def do_confints_overlap(time1, dtp1, time2, dtp2, alpha = 0.1):\n",
    "    lower1, upper1 = ((2 * time1) / chi2.ppf(q = 1 - alpha / 2, df = 2 * dtp1),\n",
    "                      (2 * time1) / chi2.ppf(q = alpha / 2, df = 2 * dtp1))\n",
    "    lower2, upper2 = ((2 * time2) / chi2.ppf(q = 1 - alpha / 2, df = 2 * dtp2),\n",
    "                      (2 * time2) / chi2.ppf(q = alpha / 2, df = 2 * dtp2))\n",
    "    \n",
    "    return (lower1 - upper2) * (lower2 - upper1) > 0   #перевірка на перетин інтервалів"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Зниження максимальної дозволеної швидкості"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Починаючи з $1$ січня $2018$ року максимальна швидкість у населених пунктах була [знижена](https://www.pravda.com.ua/news/2018/01/1/7167256/) з $60$ до $50$ км/год. Та чи допомогло це? Давайте подивимось на кількість смертельних випадків, тяжких та легких травм."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "death_labels = ['Помер на місці ДТП', 'Помер по дорозі в лікарню', 'Помер у лікарні протягом 30 діб']\n",
    "speed_60 = [len(data[(data['year'] < 2018) & (data['injury'].isin(death_labels))]),\n",
    "            len(data[(data['year'] < 2018) & (data['injury'] == 'Тяжко травмований')]),\n",
    "            len(data[(data['year'] < 2018) & (data['injury'] == 'Легко травмований')]),\n",
    "            data[(data['year'] < 2018)]['id'].count()]\n",
    "speed_50 = [len(data[(data['year'] >= 2018) & (data['injury'].isin(death_labels))]),\n",
    "            len(data[(data['year'] >= 2018) & (data['injury'] == 'Тяжко травмований')]),\n",
    "            len(data[(data['year'] >= 2018) & (data['injury'] == 'Легко травмований')]),\n",
    "            data[(data['year'] >= 2018)]['id'].count()]\n",
    "\n",
    "years_60, years_50 = 1 + 151 / 356, 2   #час (в роках), протягом якого швидкість була 60 і 50 відповідно\n",
    "stat_signif = [do_confints_overlap(years_50, i, years_60, j) for i, j in zip(speed_50, speed_60)]\n",
    "\n",
    "def accidents_on_street(streetname):\n",
    "    for _, row in data.iterrows():\n",
    "        if streetname in row['street']:\n",
    "            print(row['date'], row['street'], row['xtra'], row['type'], row['injury'], row['person'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "matplotlib.rcParams['figure.figsize'] = [12, 8]\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "plt.rcParams.update({'font.size': 14})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#наступний код генерує візуалізації\n",
    "\n",
    "def form_text(dtp_per_year_1, dtp_per_year_2, \n",
    "              year, significant):\n",
    "    text = ' '\n",
    "    if year:\n",
    "        text += str(int(dtp_per_year_1))\n",
    "    else:\n",
    "        text += str(\"{0:.2f}\".format(dtp_per_year_1))\n",
    "    \n",
    "    if (dtp_per_year_2 > 0 and dtp_per_year_1 > 0):\n",
    "        text += ' (' + str(int(100 * (dtp_per_year_1 / dtp_per_year_2 - 1))) + '%)'\n",
    "        if significant:\n",
    "            text += '*'\n",
    "            \n",
    "    return text\n",
    "\n",
    "def plot_results(dtps, times, lab, title, statsign, year, colors = ['red', 'tomato', 'coral', 'salmon'], \n",
    "                 labels = ['Загиблі', 'Тяжко травмовані', 'Легко травмовані', 'Всього ДТП']):\n",
    "    for n, color in enumerate(colors):\n",
    "        plt.barh(np.arange(dtps.shape[0]) + 0.2 * n, width = dtps[:, n] / times, \n",
    "                 height = 0.2, color = color, label = labels[n], tick_label = lab)\n",
    "    \n",
    "    for i, dtp_ in enumerate(dtps):\n",
    "        for j, dtp in enumerate(dtp_):\n",
    "            text = form_text(dtp / times[i], 0 if i % 2 == 1 else (dtps[i + 1, j] / times[i + 1]), \n",
    "                             year, not statsign[int(i / 2), j])\n",
    "            plt.text(dtp / times[i], i + j * 0.2, text, ha='left', va='center')\n",
    "    \n",
    "    plt.title(title)\n",
    "    plt.xlabel('Кількість, на рік')\n",
    "    plt.legend(loc = 4)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_results(np.array([speed_50, speed_60]), np.array([years_50, years_60]), ['50 км/год', '60 км/год'],\n",
    "             'До та після зменшення максимальної швидкості', np.array([stat_signif]), True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Зірочкою тут і далі позначено ті цифри, зміна яких є статистично значимою. Можливо, існують і відмінні від зменшення максимальної швидкості джерела зменшення смертності, травматичності і загальної кількості ДТП на дорогах Києва. Але враховуючи те, наскільки схожа статистика за $2016$ і $2017$, а також статистика за $2018$ і $2019$ роки, це зменшення відбулося скачкоподібно, саме між $2017$ і $2018$ роками. Є всі підстави вважати, що __зменшення максимальної швидкості в містах позитивно вплинуло на безпеку на дорогах__. Зауважте, що найбільше знизилась смертність, а найменше - кількість тяжко травмованих жертв ДТП. Ймовірно, частина з них могла б не вижити за обмеження в $60$ км/год. Кількість легко травмованих та загальна кількість ДТП також показали статистично значущий спад."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Острівці безпеки"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Тепер, коли ми розуміємо, що безпека на київських дорогах була суттєво різною до та після $1$ січня $2018$ року, було б неправильно оцінювати ефективність локальних інженерних рішень, використовуючи всі наявні дані (тоді б ми найімовірніше завищили цю ефективність). Отже, надалі якщо щось було встановлено до початку $2018$ року, до уваги будуть братись лише дані до початку $2018$ року і навпаки. Також будуть братися до уваги не лише ДТП безпосередньо на переході, який розглядається, але й на ділянці дороги, що розташована найближче саме до цього переходу."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Я розгляну всі острівці безпеки, дату встановлення яких можна більш-менш точно визначити. Далі доведеться вручну перебрати всі ДТП на потрібній нам вулиці, результати представлені далі:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_from_matrix(matrix, text_before, text_after, title):\n",
    "    dtp = np.sum(matrix[:, 2:], axis = 0).reshape(2, 4)\n",
    "    years_before = np.sum(matrix[:, 1])\n",
    "    years_after = np.sum(matrix[:, 0])\n",
    "    stat_signif = [do_confints_overlap(years_after, i, years_before, j) for i, j in zip(dtp[0], dtp[1])]\n",
    "\n",
    "    plot_results(dtp, [years_after, years_before], [text_before, text_after],\n",
    "                 title, np.array([stat_signif]), False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#в кожному рядку таблиці - кількість років з острівцем-кількість років без нього-статистика по ДТП після і до\n",
    "islands = np.array([[1, 1, 0, 1, 3, 4, 0, 0, 0, 0],   #Зої Гайдай, 7\n",
    "                    [1, 1, 0, 0, 0, 0, 0, 0, 1, 1],   #Героїв Дніпра, 38\n",
    "                    [122/365, 1 + 243/365, 0, 0, 0, 0, 0, 3, 4, 6],   #Ревуцького, 5-13\n",
    "                    [1 + 131/365, 234/365, 0, 0, 0, 0, 0, 0, 1, 1],   #Грушевського, 4\n",
    "                    [257/365, 1 + 108/365, 0, 0, 1, 1, 0, 0, 3, 3],   #Миру, 17\n",
    "                    [1, 1, 0, 1, 0, 1, 0, 0, 0, 0],   #Йорданська, 20\n",
    "                    [1, 1, 0, 0, 2, 2, 0, 1, 0, 1],   #Малиновського, 27\n",
    "                    [257/365, 1 + 108/365, 0, 0, 0, 0, 0, 0, 0, 0],   #Кловський, 8\n",
    "                    [106/356, 1 + 250/365, 0, 0, 0, 0, 0, 2, 1, 3],   #Васильківська, 90\n",
    "                    [76/365, 1 + 280/356, 0, 0, 0, 0, 0, 0, 1, 1],    #Ризька, 6\n",
    "                    [76/356, 1 + 280/365, 0, 0, 0, 0, 0, 0, 0, 0]])    #Стеценка\n",
    "    \n",
    "plot_from_matrix(islands, 'Після \\nострівців', 'До \\nострівців', 'До та після встановлення острівців безпеки')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Тут кількість усереднена по всім розглянутим переходам. Виглядає непогано, але звісно, даних замало, щоб щось стверджувати напевно - жодна зі змін не є статистично значущою. Дійсно, прогнозувати майбутню аварійність ці дані не дозволяють - взимку $2020$ на одному з переходів, обладнаним острівцем, [насмерть збили жінку](https://espreso.tv/news/2020/02/11/dtp_u_kyyevi_vodiy_zbyv_nasmert_zhinku_na_pishokhidnomu_perekhodi). Також цей аналіз не враховує обставини встановлення конкретних острівців, доводиться агрегувати всі дані щоб отримати хоч якийсь усереднений результат."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## \"Лежачі поліцейські\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Встановленням \"лежачих поліцейських\" займається КП \"ЦОДР\". На своїй [фейсбук-сторінці](https://www.facebook.com/kyivtrafficmanagement) вони часто звітують про виконані роботи. Ігноруючи \"поліцейських\", встановлених у дворах та тих, дату встановлення яких визначити не виходить, маємо таке:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#в кожному рядку таблиці - кількість років з \"поліцейським\"-кількість років без нього-статистика по ДТП після і до\n",
    "bumps =  np.array([[254/365, 1 + 111/365, 0, 0, 0, 0, 0, 0, 0, 0],  #Челябінська, 1\n",
    "                   [249/365, 1 + 116/365, 0, 0, 0, 0, 0, 0, 0, 0],  #Лісова, 64-66\n",
    "                   [231/365, 1 + 134/365, 0, 0, 0, 0, 0, 0, 0, 0],  #Тороповського, 49/5\n",
    "                   [208/365, 1 + 157/365, 0, 0, 0, 0, 0, 0, 1, 1],  #Стуса, 23\n",
    "                   [204/365, 1 + 161/365, 0, 0, 0, 0, 0, 0, 0, 0],  #Драгомирова, 10-А\n",
    "                   [200/365, 1 + 165/365, 0, 0, 0, 0, 0, 0, 3, 3],  #Вокзальна, 1\n",
    "                   [200/365, 1 + 165/365, 0, 0, 0, 0, 0, 0, 0, 0],  #пров. Хоткевича, 6\n",
    "                   [191/365, 1 + 174/365, 0, 0, 0, 0, 0, 0, 0, 0],  #Котарбінського, 20\n",
    "                   [178/365, 1 + 187/365, 0, 0, 0, 0, 0, 0, 1, 1],  #Лаврухіна, 10\n",
    "                   [172/365, 1 + 193/365, 0, 0, 0, 0, 0, 0, 1, 1],  #Білицька, 12\n",
    "                   [157/365, 1 + 208/365, 0, 0, 0, 0, 0, 0, 0, 0],  #Ломоносова, 26\n",
    "                   [108/365, 1 + 257/365, 0, 0, 1, 1, 0, 2, 2, 3],  #Григоренка, 9-18\n",
    "                   [91/365, 1 + 276/365, 0, 0, 0, 0, 0, 0, 0, 0],  #Данченка/Брестська\n",
    "                   [35/365, 1 + 330/365, 0, 0, 0, 0, 0, 0, 0, 0],  #Товарна/Дружби народів\n",
    "                   [34/365, 1 + 331/365, 0, 0, 0, 0, 0, 0, 1, 1],  #Бульварно-Кудрявська, 2\n",
    "                   [32/365, 1 + 333/365, 0, 0, 0, 0, 0, 0, 0, 0],  #Кіото, 9\n",
    "                   [32/365, 1 + 333/365, 0, 0, 0, 0, 0, 0, 0, 0],  #Сєркова, 14-Є\n",
    "                   [27/365, 1 + 338/365, 0, 0, 0, 0, 0, 1, 0, 1]])  #Корольова, 7\n",
    "\n",
    "plot_from_matrix(bumps, 'Після \\n\"поліцейських\"', 'До \\n\"поліцейських\"', 'До та після встановлення \"лежачих поліцейських\"')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Дуже мала вибірка не дозволяє зробити статистично значущих висновків, але попередньо \"лежачі поліцейські\" здаються дуже ефективними. Їх багато критикують, іноді пропонуючи підняті на рівень тротуару переходи як альтернативу. Але на жаль такі рішення не стали популярними в Києві, тож огляд їх ефективності зробити не вийде."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Пластикові школярі"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Проєкт Громадського Бюджету-2019](https://gb.kyivcity.gov.ua/projects/archive/10/show/1000), що передбачав встановлення ростових фігур школярів біля небезпечних нерегульованих переходів на Сирці, переміг на голосуванні і був реалізований в кінці вересня $2019$ року. Чи допомогло це?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#в кожному рядку таблиці - кількість років з муляжами-кількість років без них-статистика по ДТП після і до\n",
    "kids = np.array([[99/365, 1 + 266/365, 0, 0, 0, 0, 0, 0, 0, 0],  #Бакинська, 12\n",
    "                 [99/365, 1 + 266/365, 0, 0, 1, 1, 0, 1, 1, 2],  #Ольжича, 3\n",
    "                 [99/365, 1 + 266/365, 0, 0, 0, 0, 0, 0, 0, 0],  #Ольжича/Берлинського\n",
    "                 [99/365, 1 + 266/365, 0, 0, 0, 0, 0, 0, 0, 0],  #Берлинського/Сальського\n",
    "                 [99/365, 1 + 266/365, 0, 0, 0, 0, 0, 0, 0, 0],  #Сальського/Вавилових\n",
    "                 [99/365, 1 + 266/365, 0, 0, 0, 0, 0, 0, 0, 0],  #Житкова/Глушка\n",
    "                 [99/365, 1 + 266/365, 0, 0, 0, 0, 0, 0, 0, 0],  #Вавилових/Глушка\n",
    "                 [99/365, 1 + 266/365, 0, 0, 0, 0, 0, 1, 1, 2],  #Берлинського/Глушка\n",
    "                 [99/365, 1 + 266/365, 0, 0, 0, 0, 0, 0, 0, 0],  #Житкова/Щусєва\n",
    "                 [99/365, 1 + 266/365, 0, 0, 0, 0, 0, 0, 1, 1],  #Ризька/Грекова\n",
    "                 [99/365, 1 + 266/365, 0, 0, 0, 0, 0, 0, 0, 0],  #Парково-Сирецька, 5\n",
    "                 [99/365, 1 + 266/365, 0, 0, 0, 0, 0, 0, 0, 0],  #Парково-Сирецька, 9\n",
    "                 [99/365, 1 + 266/365, 0, 0, 0, 0, 0, 0, 0, 0],  #Парково-Сирецька/Гонти\n",
    "                 [99/365, 1 + 266/365, 0, 0, 0, 0, 0, 0, 0, 0],  #Табірна/Артилерійський\n",
    "                 [99/365, 1 + 266/365, 0, 0, 0, 0, 0, 0, 0, 0],  #Сікорського, 6\n",
    "                 [99/365, 1 + 266/365, 0, 0, 0, 0, 0, 0, 0, 0],  #Берлинського/Грекова\n",
    "                 [99/365, 1 + 266/365, 0, 0, 0, 0, 0, 0, 1, 1]])  #Сікорського/Жабаєва\n",
    "\n",
    "plot_from_matrix(kids, 'Після \\nшколярів', 'До \\nшколярів', 'До та після встановлення фігурок школярів')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Знову, жодних впевнених висновків зробити не можна, адже до кінця $2019$ року кожен зі школярів простояв лише трішки більше трьох місяців і статистика про аварійність після їх встановлення складається лише з одного ДТП. Побачивши статистику за $2020$ рік, можна було б сказати більше."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Висновки"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Обмеження максимальної швидкості дійсно робить місто безпечнішим. Навіть коли контроль за дотриманням цього обмеження слабкий."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) З приводу острівців безпеки, попередньо можна зробити висновок про їх ефективність, але зменшення кількості ДПТ не є дуже значним. Було б неправильно думати, що київські переходи, обладнані острівцями, є справді безпечними. Комунальним службам не варто про них забувати, а міським активістам імовірно варто просувати посилення острівців безпеки іншими засобами заспокоєння руху на найбільш проблемних нерегульованих переходах."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) \"Лежачі поліцейські\" попередньо виглядають дійсно ефективними в плані безпеки руху (хоча можливості коректно порівняти їх з піднятими переходами на основі даних, що маємо, немає)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4) Я не знаю, чи працюють пластикові макети дітей. Треба чекати, доки назбирається ще даних."
   ]
  }
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