{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Nobody Likes Traffic\n", "## Signs It's Slowing Down on I-94" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "This analysis is attempting to identify factors associated with heavy traffic.\n", "\n", "The dataset is comprised of daily and hourly entries for westbound traffic midway between Minneapolis and St. Paul, the largest and capital cities, respectively, in Minnesota. It was collected from a Department of Transportation station on interstate 94 between October 2012 and September 2018.\n", "\n", "The dataset was created by John Hogue from the UCI Machine Learning Repository.\n", "https://archive.ics.uci.edu/ml/datasets/Metro+Interstate+Traffic+Volume\n", "\n", "The colors used are the University of Minnesota school colors, gold for graphs and maroon for line plots. Go Gophers!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# import libraries, open file, set display options\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import numpy as np\n", "import pprint\n", "pd.options.display.float_format = '{:20,.4f}'.format\n", "traffic = pd.read_csv(\"Metro_Interstate_Traffic_Volume.csv\")\n", "pd.set_option('display.max_rows', None)\n", "pd.set_option('display.max_columns', None)\n", "pd.set_option('display.width', 1000)\n", "pd.set_option('display.colheader_justify', 'center')\n", "pd.set_option('display.precision', 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Examine Data\n", "A quick look at the beginning and ending of the data set shows nine columns with a mix of categorical text and numeric information. The source of the data set states that an automatic traffic recorder (ATR) was used to tabulate traffic volume. It is expressed in the number of vehicles per hour.\n", "* There don't appear to be any missing values.\n", "* The statistical description looks unusual for rain and snow. There aren't any quartile values listed.\n", "* The units for temperature don't look familiar and the max rain value seems high as well." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
holidaytemprain_1hsnow_1hclouds_allweather_mainweather_descriptiondate_timetraffic_volume
0None288.28000.00000.000040Cloudsscattered clouds2012-10-02 09:00:005545
1None289.36000.00000.000075Cloudsbroken clouds2012-10-02 10:00:004516
2None289.58000.00000.000090Cloudsovercast clouds2012-10-02 11:00:004767
3None290.13000.00000.000090Cloudsovercast clouds2012-10-02 12:00:005026
4None291.14000.00000.000075Cloudsbroken clouds2012-10-02 13:00:004918
\n", "
" ], "text/plain": [ " holiday temp rain_1h snow_1h clouds_all weather_main weather_description date_time traffic_volume\n", "0 None 288.2800 0.0000 0.0000 40 Clouds scattered clouds 2012-10-02 09:00:00 5545 \n", "1 None 289.3600 0.0000 0.0000 75 Clouds broken clouds 2012-10-02 10:00:00 4516 \n", "2 None 289.5800 0.0000 0.0000 90 Clouds overcast clouds 2012-10-02 11:00:00 4767 \n", "3 None 290.1300 0.0000 0.0000 90 Clouds overcast clouds 2012-10-02 12:00:00 5026 \n", "4 None 291.1400 0.0000 0.0000 75 Clouds broken clouds 2012-10-02 13:00:00 4918 " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
holidaytemprain_1hsnow_1hclouds_allweather_mainweather_descriptiondate_timetraffic_volume
48199None283.45000.00000.000075Cloudsbroken clouds2018-09-30 19:00:003543
48200None282.76000.00000.000090Cloudsovercast clouds2018-09-30 20:00:002781
48201None282.73000.00000.000090Thunderstormproximity thunderstorm2018-09-30 21:00:002159
48202None282.09000.00000.000090Cloudsovercast clouds2018-09-30 22:00:001450
48203None282.12000.00000.000090Cloudsovercast clouds2018-09-30 23:00:00954
\n", "
" ], "text/plain": [ " holiday temp rain_1h snow_1h clouds_all weather_main weather_description date_time traffic_volume\n", "48199 None 283.4500 0.0000 0.0000 75 Clouds broken clouds 2018-09-30 19:00:00 3543 \n", "48200 None 282.7600 0.0000 0.0000 90 Clouds overcast clouds 2018-09-30 20:00:00 2781 \n", "48201 None 282.7300 0.0000 0.0000 90 Thunderstorm proximity thunderstorm 2018-09-30 21:00:00 2159 \n", "48202 None 282.0900 0.0000 0.0000 90 Clouds overcast clouds 2018-09-30 22:00:00 1450 \n", "48203 None 282.1200 0.0000 0.0000 90 Clouds overcast clouds 2018-09-30 23:00:00 954 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 48204 entries, 0 to 48203\n", "Data columns (total 9 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 holiday 48204 non-null object \n", " 1 temp 48204 non-null float64\n", " 2 rain_1h 48204 non-null float64\n", " 3 snow_1h 48204 non-null float64\n", " 4 clouds_all 48204 non-null int64 \n", " 5 weather_main 48204 non-null object \n", " 6 weather_description 48204 non-null object \n", " 7 date_time 48204 non-null object \n", " 8 traffic_volume 48204 non-null int64 \n", "dtypes: float64(3), int64(2), object(4)\n", "memory usage: 3.3+ MB\n" ] }, { "data": { "text/plain": [ "None" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
temprain_1hsnow_1hclouds_alltraffic_volume
count48,204.000048,204.000048,204.000048,204.000048,204.0000
mean281.20590.33430.000249.36223,259.8184
std13.338244.78910.008239.01581,986.8607
min0.00000.00000.00000.00000.0000
25%272.16000.00000.00001.00001,193.0000
50%282.45000.00000.000064.00003,380.0000
75%291.80600.00000.000090.00004,933.0000
max310.07009,831.30000.5100100.00007,280.0000
\n", "
" ], "text/plain": [ " temp rain_1h snow_1h clouds_all traffic_volume \n", "count 48,204.0000 48,204.0000 48,204.0000 48,204.0000 48,204.0000\n", "mean 281.2059 0.3343 0.0002 49.3622 3,259.8184\n", "std 13.3382 44.7891 0.0082 39.0158 1,986.8607\n", "min 0.0000 0.0000 0.0000 0.0000 0.0000\n", "25% 272.1600 0.0000 0.0000 1.0000 1,193.0000\n", "50% 282.4500 0.0000 0.0000 64.0000 3,380.0000\n", "75% 291.8060 0.0000 0.0000 90.0000 4,933.0000\n", "max 310.0700 9,831.3000 0.5100 100.0000 7,280.0000" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# display head, tail, and basic info\n", "display(traffic.head())\n", "display(traffic.tail())\n", "display(traffic.info())\n", "display(traffic.describe())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Clean Data\n", "* Looking at the frequency of no rain and no snow verses some of either shows that the data set statistics for both columns are dominated by hours in which there was no precipitation. Both of these columns report millimeters per hour. The number of hours with some snow seems particularly low for this geographical region.\n", " * Creating a column with all positive rain values shows one outlier of 9,800 mm of rain that can be removed.\n", " * Creating a similar snow column doesn't reveal any outliers.\n", "* The temperature column is in degrees Kelvin, which is useful in chemistry or physics but not traffic analysis. Converting to degrees Fahrenheit would be standard in the US.\n", " * The temperature column has ten rows with outlier temperature values that can be removed." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hours with no rain: 44737\n", "Hours with some rain 3467\n", "Hours with no snow: 48141\n", "Hours with some snow: 63\n" ] } ], "source": [ "# separate rain column by no rain or some rain\n", "no_rain = traffic[traffic[\"rain_1h\"] == 0]\n", "some_rain = traffic[traffic[\"rain_1h\"] > 0]\n", "print(\"Hours with no rain: \", len(no_rain))\n", "print(\"Hours with some rain\", len(some_rain))\n", "# separate snow column by no snow or some snow\n", "no_snow = traffic[traffic[\"snow_1h\"] == 0]\n", "some_snow = traffic[traffic[\"snow_1h\"] > 0]\n", "print(\"Hours with no snow: \", len(no_snow))\n", "print(\"Hours with some snow: \", len(some_snow))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 3,467.0000\n", "mean 4.6475\n", "std 166.9703\n", "min 0.2500\n", "25% 0.2500\n", "50% 0.6400\n", "75% 1.7800\n", "max 9,831.3000\n", "Name: some_rain, dtype: float64" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# create a some_rain column with any rain measurements greater than 0\n", "traffic[\"some_rain\"] = traffic[\"rain_1h\"][traffic[\"rain_1h\"] > 0]\n", "# basic stats for some_rain\n", "display(traffic[\"some_rain\"].describe())" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "count 3,466.0000\n", "mean 1.8123\n", "std 3.3100\n", "min 0.2500\n", "25% 0.2500\n", "50% 0.6400\n", "75% 1.7800\n", "max 55.6300\n", "Name: some_rain, dtype: float64" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# remove row with outlier rain values\n", "traffic = traffic[traffic[\"rain_1h\"]<100]\n", "# basic stats for some_rain\n", "display(traffic[\"some_rain\"].describe())" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 63.0000\n", "mean 0.1702\n", "std 0.1499\n", "min 0.0500\n", "25% 0.0600\n", "50% 0.1000\n", "75% 0.2500\n", "max 0.5100\n", "Name: some_snow, dtype: float64" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# create a some_snow column with any snow measurements greater than 0\n", "traffic[\"some_snow\"] = traffic[\"snow_1h\"][traffic[\"snow_1h\"] > 0]\n", "# basic stats for some_snow\n", "display(traffic[\"some_snow\"].describe())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# convert temperature column from Kelvin to Fahrenheit\n", "def k_to_f(k_temp):\n", " f_temp = ((k_temp-273.15)*(9/5)) + 32\n", " return f_temp\n", "\n", "traffic[\"temp\"] = traffic[\"temp\"].map(k_to_f)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "count 48,203.0000\n", "mean 46.4998\n", "std 24.0085\n", "min -459.6700\n", "25% 30.2180\n", "50% 48.7400\n", "75% 65.5808\n", "max 98.4560\n", "Name: temp, dtype: float64" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# basic stats for temp column\n", "display(traffic[\"temp\"].describe())" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# # look at outlier temp rows\n", "# cold_days = traffic[traffic[\"temp\"]<-100]\n", "# display(cold_days)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "count 48,193.0000\n", "mean 46.6048\n", "std 22.8769\n", "min -21.5680\n", "25% 30.2540\n", "50% 48.7580\n", "75% 65.5880\n", "max 98.4560\n", "Name: temp, dtype: float64" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# remove rows with outlier temperatures\n", "traffic = traffic[traffic[\"temp\"]>-100]\n", "# basic stats for temp column\n", "display(traffic[\"temp\"].describe())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overall traffic volume\n", "\n", "The previous statistics for traffic volume shows that the minimum value is 0, the maximum value is 7280, and that traffic is usually somewhere in the middle. A histogram shows an interesting distribution. The mean is heavily influenced by how often the traffic is really light and how often the traffic is fairly bad." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# create histogram of traffic volume for entire data set\n", "traffic[\"traffic_volume\"].plot.hist(color=\"#FFCC33\")\n", "plt.title(\"Vehicles per Hour\")\n", "plt.xticks([1000,3000,5000,7000], [1000,3000,5000,7000])\n", "plt.yticks([2000,4000,6000,8000], [2000,4000,6000,8000])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Aggregate by day or night\n", "Creating a daytime group (7:00 am to 6:00 pm) and a nighttime group (7:00 pm to 6:00 am) reveals unsurprising differences in traffic volume.\n", "* The histograms clearly show:\n", " * a left skewed normal distribution for daytime traffic, with the mean traffic volume of about 4,600 vehicles per hour.\n", " * a right skewed distribution for nighttime traffic. The mean traffic volume is a little less than 1,800 vehicles per hour.\n", "* This would suggest that including the nighttime data could invalidate any conclusion. That being said, there is some occurrence of high traffic in the evenings. It would be worthwhile to take a look and see if this is just overlap from the day and what the conditions where that caused the increase." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The number of rows and columns for the day and night series.\n" ] }, { "data": { "text/plain": [ "(23874, 11)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "(24319, 11)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# transform column to datetime\n", "traffic[\"date_time\"] = pd.to_datetime(traffic[\"date_time\"])\n", "# create date_time series\n", "day_night = traffic[\"date_time\"].dt.hour\n", "# create day traffic series from 0700 to 1800\n", "day_traffic = day_night.between(7, 18)\n", "day_traffic = traffic.loc[day_traffic].copy()\n", "# create night traffic series from 1900 to 0600\n", "night_traffic = day_night.between(19, 24) | day_night.between(0, 6)\n", "night_traffic = traffic.loc[night_traffic].copy()\n", "# verify\n", "print(\"The number of rows and columns for the day and night series.\")\n", "display(day_traffic.shape)\n", "display(night_traffic.shape)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# create two graphs\n", "plt.figure(figsize=(10,7))\n", "# daytime traffic volume histogram\n", "plt.subplot(2,2,1)\n", "day_traffic[\"traffic_volume\"].plot.hist(color=\"#FFCC33\")\n", "plt.title(\"Daytime Vehicles per Hour\")\n", "plt.xticks([1000,3000,5000,7000], [1000,3000,5000,7000])\n", "plt.xlim(0,7500)\n", "plt.ylim(0,8000)\n", "plt.ylabel(\"\")\n", "# nighttime traffic volume histogram\n", "plt.subplot(2,2,2)\n", "night_traffic[\"traffic_volume\"].plot.hist(color=\"#FFCC33\")\n", "plt.title(\"Nighttime Vehicles per Hour\")\n", "plt.xticks([1000,3000,5000,7000], [1000,3000,5000,7000])\n", "plt.xlim(0,7500)\n", "plt.ylim(0,8000)\n", "plt.ylabel(\"\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
temprain_1hsnow_1hclouds_alltraffic_volumesome_rainsome_snow
count23,874.000023,874.000023,874.000023,874.000023,874.00001,770.000034.0000
mean48.43470.12130.000353.12554,762.30381.63650.1779
std23.48230.88040.008937.56351,174.18162.82490.1553
min-21.56800.00000.00000.00000.00000.25000.0500
25%31.15400.00000.00005.00004,253.00000.25000.0600
50%51.13400.00000.000075.00004,820.00000.64000.1000
75%68.52200.00000.000090.00005,559.00001.69000.3025
max98.456044.45000.5100100.00007,280.000044.45000.5100
\n", "
" ], "text/plain": [ " temp rain_1h snow_1h clouds_all traffic_volume some_rain some_snow \n", "count 23,874.0000 23,874.0000 23,874.0000 23,874.0000 23,874.0000 1,770.0000 34.0000\n", "mean 48.4347 0.1213 0.0003 53.1255 4,762.3038 1.6365 0.1779\n", "std 23.4823 0.8804 0.0089 37.5635 1,174.1816 2.8249 0.1553\n", "min -21.5680 0.0000 0.0000 0.0000 0.0000 0.2500 0.0500\n", "25% 31.1540 0.0000 0.0000 5.0000 4,253.0000 0.2500 0.0600\n", "50% 51.1340 0.0000 0.0000 75.0000 4,820.0000 0.6400 0.1000\n", "75% 68.5220 0.0000 0.0000 90.0000 5,559.0000 1.6900 0.3025\n", "max 98.4560 44.4500 0.5100 100.0000 7,280.0000 44.4500 0.5100" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
temprain_1hsnow_1hclouds_alltraffic_volumesome_rainsome_snow
count24,319.000024,319.000024,319.000024,319.000024,319.00001,696.000029.0000
mean44.80840.13920.000245.68701,785.53091.99590.1610
std22.12021.11110.007440.04641,441.86813.74200.1455
min-20.07400.00000.00000.00000.00000.25000.0500
25%29.40800.00000.00001.0000530.50000.25000.0500
50%46.81400.00000.000040.00001,287.00000.67000.1000
75%63.59000.00000.000090.00002,819.00001.85000.2500
max94.154055.63000.5100100.00006,386.000055.63000.5100
\n", "
" ], "text/plain": [ " temp rain_1h snow_1h clouds_all traffic_volume some_rain some_snow \n", "count 24,319.0000 24,319.0000 24,319.0000 24,319.0000 24,319.0000 1,696.0000 29.0000\n", "mean 44.8084 0.1392 0.0002 45.6870 1,785.5309 1.9959 0.1610\n", "std 22.1202 1.1111 0.0074 40.0464 1,441.8681 3.7420 0.1455\n", "min -20.0740 0.0000 0.0000 0.0000 0.0000 0.2500 0.0500\n", "25% 29.4080 0.0000 0.0000 1.0000 530.5000 0.2500 0.0500\n", "50% 46.8140 0.0000 0.0000 40.0000 1,287.0000 0.6700 0.1000\n", "75% 63.5900 0.0000 0.0000 90.0000 2,819.0000 1.8500 0.2500\n", "max 94.1540 55.6300 0.5100 100.0000 6,386.0000 55.6300 0.5100" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# basic daytime and nighttime stats\n", "display(day_traffic.describe())\n", "display(night_traffic.describe())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Daytime Group\n", "* Looking at daytime values initially will provide most of the analysis.\n", "* The mean traffic volume by month is not that far off the mean traffic volume for all day entries, 4762. The standard deviation between the months is only 190. The graph does show two dips, with one corresponding to winter months in the US. The other looks like it occurs during July, which is unusual because that's during the summer when traffic tends to be high.\n", " * A closer look at the annual distribution for the month of July shows an atypical decrease in 2016. A quick google search points to a large highway construction project taking place that year. https://www.mprnews.org/story/2016/07/22/i94-stpaul-shutdown-twin-cities-weekend-road-woes\n", "* The mean daytime traffic volume does change considerably depending on the day of the week. Saturday shows a considerable dip and Sunday a little more. But there are still plenty of cars making the drive!\n", "* Looking at the traffic volume for weekdays by hour indicates a peak in the morning (by 7:00 am if not earlier) and again in the afternoon at 4:00 pm.\n", "* A similar look at weekend hours shows a slow build in the morning with fairly steady traffic until evening." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "count 12.0000\n", "mean 4,767.5037\n", "std 189.9449\n", "min 4,374.8346\n", "25% 4,676.7308\n", "50% 4,880.0964\n", "75% 4,907.9511\n", "max 4,928.3020\n", "Name: traffic_volume, dtype: float64" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# create month column and convert info in date_time column to months 1-12\n", "day_traffic[\"month\"] = day_traffic[\"date_time\"].dt.month\n", "# group daytime by month and get the mean column values for each month\n", "day_traffic_month_group = day_traffic.groupby(\"month\").mean()\n", "# basic stats for daytime month group mean traffic volume\n", "display(day_traffic_month_group[\"traffic_volume\"].describe())\n", "# line plot of daytime month group mean traffic volumes\n", "day_traffic_month_group[\"traffic_volume\"].plot.line(c=\"#7A0019\")\n", "plt.title(\"Daytime Mean Traffic Volume by Month\")\n", "plt.yticks([4400,4600,4800], [4400,4600,4800])\n", "plt.xlabel(\"\")\n", "plt.xticks([1,2,3,4,5,6,7,8,9,10,11,12],[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\n", " \"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# group July by year\n", "july_months = traffic[traffic[\"date_time\"].dt.month == 7]\n", "july_yearly_group = july_months.groupby(traffic[\"date_time\"].dt.year)\n", "# line plot of July yearly group mean traffic volume\n", "july_yearly_group[\"traffic_volume\"].mean().plot.line(c=\"#7A0019\")\n", "plt.title(\"July Mean Traffic Volume by Year\")\n", "plt.xlabel(\"\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# create day_of_week column and convert info in date_time column to days 0-6\n", "day_traffic[\"day_of_week\"] = day_traffic[\"date_time\"].dt.dayofweek\n", "# group daytime by day of week and get the mean column values for each day\n", "day_traffic_day_group = day_traffic.groupby(\"day_of_week\").mean()\n", "# line plot of daytime week of day group mean traffic volumes\n", "day_traffic_day_group[\"traffic_volume\"].plot.line(c=\"#7A0019\")\n", "plt.title(\"Daytime Mean Traffic Volume by Day\")\n", "plt.yticks([3500,4000,4500,5000],[3500,4000,4500,5000])\n", "plt.xlabel(\"\")\n", "plt.xticks([0,1,2,3,4,5,6],[\"Mon\",\"Tue\",\"Wed\",\"Thur\",\"Fri\",\"Sat\",\"Sun\"])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# create an hour column and convert info in date_time column to hours 0 to 23\n", "day_traffic[\"hour\"] = day_traffic[\"date_time\"].dt.hour\n", "# group daytime into weekdays (4=Friday) and weekends (5=Saturday)\n", "weekdays = day_traffic[day_traffic[\"day_of_week\"] <= 4]\n", "weekends = day_traffic[day_traffic[\"day_of_week\"] >= 5]\n", "# group weekday entries by hour and get the mean column values for each hour\n", "weekdays_hours_group = weekdays.groupby(\"hour\").mean()\n", "# group weekend entries by hour and get the mean column values for each hour\n", "weekends_hours_group = weekends.groupby(\"hour\").mean()\n", "# basic stats for daytime weekday hour group mean traffic volume\n", "# display(weekdays_hours_group[\"traffic_volume\"].describe())\n", "# create two graphs\n", "plt.figure(figsize=(10,6))\n", "# line plot of weekdays hours group mean traffic volumes \n", "plt.subplot(2,2,1)\n", "weekdays_hours_group[\"traffic_volume\"].plot.line(c=\"#7A0019\")\n", "plt.title(\"Weekday Traffic Volume by Hour\")\n", "plt.ylim(0,6500)\n", "# line plot of weekends hours group mean traffic volumes\n", "plt.subplot(2,2,2)\n", "weekends_hours_group[\"traffic_volume\"].plot.line(c=\"#7A0019\")\n", "plt.title(\"Weekend Traffic Volulme by Hour\")\n", "plt.ylim(0,6500)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Nightime Group" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# line plot for nighttime traffic vollume by hour\n", "night_traffic[\"hour\"] = night_traffic[\"date_time\"].dt.hour\n", "nighttime_hours_group = night_traffic.groupby(\"hour\").mean()\n", "nighttime_hours_group[\"traffic_volume\"].plot.bar(color=\"#FFCC33\")\n", "plt.ylim(0,6000)\n", "plt.title(\"Nightime Mean Traffic Volume by Hour\")\n", "plt.xlabel(\"\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Weather\n", "### Part 1\n", "There are small correlations between daytime traffic volume and measurable amounts of snow and rain. Neither of these associations would be obvious though.\n", "* The snow correlation is probably just an artifact due to the low sample number.\n", "* The subtle relationship between rain and traffic volume appears from the scatter chart to be less causation than simple correlation. It is likely that there are simply a fair number of rainy days in this area." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
temprain_1hsnow_1hclouds_alltraffic_volumesome_rainsome_snowmonthday_of_weekhour
temp1.00000.0849-0.0197-0.14080.12550.20650.55900.22400.00300.1632
rain_1h0.08491.00000.00680.0885-0.04071.00000.79230.0194-0.0047-0.0168
snow_1h-0.01970.00681.00000.02770.0013-0.01111.00000.0268-0.00880.0039
clouds_all-0.14080.08850.02771.0000-0.03330.08500.12730.0004-0.04180.0235
traffic_volume0.1255-0.04070.0013-0.03331.0000-0.17230.4014-0.0227-0.41630.1724
some_rain0.20651.0000-0.01110.0850-0.17231.0000NaN0.00840.0343-0.0429
some_snow0.55900.79231.00000.12730.4014NaN1.00000.21910.01100.0840
month0.22400.01940.02680.0004-0.02270.00840.21911.00000.01380.0080
day_of_week0.0030-0.0047-0.0088-0.0418-0.41630.03430.01100.01381.0000-0.0025
hour0.1632-0.01680.00390.02350.1724-0.04290.08400.0080-0.00251.0000
\n", "
" ], "text/plain": [ " temp rain_1h snow_1h clouds_all traffic_volume some_rain some_snow month day_of_week hour \n", "temp 1.0000 0.0849 -0.0197 -0.1408 0.1255 0.2065 0.5590 0.2240 0.0030 0.1632\n", "rain_1h 0.0849 1.0000 0.0068 0.0885 -0.0407 1.0000 0.7923 0.0194 -0.0047 -0.0168\n", "snow_1h -0.0197 0.0068 1.0000 0.0277 0.0013 -0.0111 1.0000 0.0268 -0.0088 0.0039\n", "clouds_all -0.1408 0.0885 0.0277 1.0000 -0.0333 0.0850 0.1273 0.0004 -0.0418 0.0235\n", "traffic_volume 0.1255 -0.0407 0.0013 -0.0333 1.0000 -0.1723 0.4014 -0.0227 -0.4163 0.1724\n", "some_rain 0.2065 1.0000 -0.0111 0.0850 -0.1723 1.0000 NaN 0.0084 0.0343 -0.0429\n", "some_snow 0.5590 0.7923 1.0000 0.1273 0.4014 NaN 1.0000 0.2191 0.0110 0.0840\n", "month 0.2240 0.0194 0.0268 0.0004 -0.0227 0.0084 0.2191 1.0000 0.0138 0.0080\n", "day_of_week 0.0030 -0.0047 -0.0088 -0.0418 -0.4163 0.0343 0.0110 0.0138 1.0000 -0.0025\n", "hour 0.1632 -0.0168 0.0039 0.0235 0.1724 -0.0429 0.0840 0.0080 -0.0025 1.0000" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Pearson correlation coefficient for all daytime columns\n", "day_traffic.corr()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# scatter plot for daytime traffic volume and some snow\n", "plt.scatter(day_traffic[\"traffic_volume\"],day_traffic[\"some_snow\"],c=\"#FFCC33\")\n", "plt.title(\"Traffic Volume and Snow\")\n", "plt.xlabel(\"Vehicles per Hour\")\n", "plt.ylabel(\"Snow in mm per Hour\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# scatter plot for daytime traffic volume and some rain\n", "plt.scatter(day_traffic[\"traffic_volume\"], day_traffic[\"some_rain\"],c=\"#FFCC33\")\n", "plt.title(\"Traffic and Rain\")\n", "plt.xlabel(\"Vehicles per Hour\")\n", "plt.ylabel(\"Rain in mm per Hour\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Weather\n", "### Part 2\n", "* Grouping daytime traffic by the categorical options in the ```weather_main``` column provides an interesting graph but no real insight. The traffic volume seems to be fairly similar across the group.\n", "* Grouping daytime traffic by the categorical options in the ```weather_description``` doesn't reveal any surprises either. The highest traffic volumes were on days with snow, so maybe we can conclude the Minnesotans are pretty resilient to bad weather!" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# group day traffic by weather main and weather description\n", "weather_main = day_traffic.groupby(\"weather_main\").mean()\n", "weather_description = day_traffic.groupby(\"weather_description\").mean()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# bar chart for weather main\n", "weather_main[\"traffic_volume\"].plot.barh(color=\"#FFCC33\")\n", "plt.xticks([1000,3000,5000], [1000,3000,5000])\n", "plt.ylabel(\"\")\n", "plt.title(\"Weather and Daytime Traffic Volume\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# bar chart for weather description\n", "weather_description[\"traffic_volume\"].plot.barh(figsize=(5,10), color=\"#FFCC33\")\n", "plt.xticks([1000,3000,5000], [1000,3000,5000])\n", "plt.ylabel(\"\")\n", "plt.title(\"Weather and Daytime Traffic Volume\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "\n", "* It's a pretty safe bet that if it's a weekday, especially during rush hour that your going to see a lot of traffic on I-94. On the weekends there is less traffic, and in the evenings even less.\n", "* There is also a decrease in traffic volume the winter months.\n", "* Looking at weather conditions showed no reliable correlations. Even clear skies did not show a relationship with high traffic volumes.\n", "* Nighttime traffic volumes are heavily influenced by hour. Traffic increases dramatically at 5:00 am and 6:00 am, then tapers off after 7:00 pm.\n", "* It is notable that using mean values makes it difficult to be aware of larger values. Because traffic is such a consequential and daily factor for so many entities, it might be worth reducing the sample to a smaller set of high traffic volume rows.\n", "* Lastly, because this data set has a large amount of granular weather information, comparing similar hours with good weather and bad weather could be informative." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }