Analysing Traffic flow indicator

The project is about finding some key indicators to hel us analyse flow of traffic I-94 Interstate Highway, from the given data set.
The goal of this project is to practice skills that we have learnt so far, also this project will be helpful getting more fluent on using using different trechniques
In [1]:
# Import Pandas to read file
import pandas as pd
traffic_flow = pd.read_csv('Metro_Interstate_Traffic_Volume.csv')
traffic_flow.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 48204 entries, 0 to 48203
Data columns (total 9 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   holiday              48204 non-null  object 
 1   temp                 48204 non-null  float64
 2   rain_1h              48204 non-null  float64
 3   snow_1h              48204 non-null  float64
 4   clouds_all           48204 non-null  int64  
 5   weather_main         48204 non-null  object 
 6   weather_description  48204 non-null  object 
 7   date_time            48204 non-null  object 
 8   traffic_volume       48204 non-null  int64  
dtypes: float64(3), int64(2), object(4)
memory usage: 3.3+ MB
In [2]:
# importy MatPlotLib to use it's features and adding '%matplotlib inline' to use its features in Jupyter
import matplotlib.pyplot as plt
%matplotlib inline
traffic_flow['traffic_volume'].plot.hist()
traffic_flow['traffic_volume'].describe()
Out[2]:
count    48204.000000
mean      3259.818355
std       1986.860670
min          0.000000
25%       1193.000000
50%       3380.000000
75%       4933.000000
max       7280.000000
Name: traffic_volume, dtype: float64
Comparing Traffic flow day time vs night time
In [3]:
traffic_flow['date_time'] = pd.to_datetime(traffic_flow['date_time'])
day_time = traffic_flow[(traffic_flow['date_time'].dt.hour >= 7) & (traffic_flow['date_time'].dt.hour < 19)]
night_time = traffic_flow[(traffic_flow['date_time'].dt.hour >= 19) & (traffic_flow['date_time'].dt.hour <= 23)]
day_time.head()
Out[3]:
holiday temp rain_1h snow_1h clouds_all weather_main weather_description date_time traffic_volume
0 None 288.28 0.0 0.0 40 Clouds scattered clouds 2012-10-02 09:00:00 5545
1 None 289.36 0.0 0.0 75 Clouds broken clouds 2012-10-02 10:00:00 4516
2 None 289.58 0.0 0.0 90 Clouds overcast clouds 2012-10-02 11:00:00 4767
3 None 290.13 0.0 0.0 90 Clouds overcast clouds 2012-10-02 12:00:00 5026
4 None 291.14 0.0 0.0 75 Clouds broken clouds 2012-10-02 13:00:00 4918
In [4]:
night_time.head()
Out[4]:
holiday temp rain_1h snow_1h clouds_all weather_main weather_description date_time traffic_volume
10 None 290.97 0.0 0.0 20 Clouds few clouds 2012-10-02 19:00:00 3539
11 None 289.38 0.0 0.0 1 Clear sky is clear 2012-10-02 20:00:00 2784
12 None 288.61 0.0 0.0 1 Clear sky is clear 2012-10-02 21:00:00 2361
13 None 287.16 0.0 0.0 1 Clear sky is clear 2012-10-02 22:00:00 1529
14 None 285.45 0.0 0.0 1 Clear sky is clear 2012-10-02 23:00:00 963
Plotting Histogram to compare both Night and Day time data
In [5]:
plt.figure(figsize = (10, 10))
plt.subplot(1,2,1)
plt.hist(day_time['traffic_volume'])
plt.title('Day Time Traffic Volume')
plt.xlim([0, 7500])
plt.ylim([0, 8000])
plt.subplot(1,2,2)
plt.hist(night_time['traffic_volume'])
plt.title('Night Time Traffic Volume')
plt.xlim([0, 7500])
plt.ylim([0, 8000])
traffic_flow['traffic_volume'].describe()
Out[5]:
count    48204.000000
mean      3259.818355
std       1986.860670
min          0.000000
25%       1193.000000
50%       3380.000000
75%       4933.000000
max       7280.000000
Name: traffic_volume, dtype: float64

Isolating Data by months for the Day time because we previously saw that there is not much traffic at night time as compare to Day Time so keeping in mind goal of our project we will stick to Day Time.

In [6]:
day_time['month'] = day_time['date_time'].dt.month
by_month = day_time.groupby('month').mean()
by_month['traffic_volume']

<ipython-input-6-0d0913a858f7>:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
Out[6]:
month
1     4495.613727
2     4711.198394
3     4889.409560
4     4906.894305
5     4911.121609
6     4898.019566
7     4595.035744
8     4928.302035
9     4870.783145
10    4921.234922
11    4704.094319
12    4374.834566
Name: traffic_volume, dtype: float64
In [7]:
plt.plot(by_month['traffic_volume'])
plt.show()

Isolating data by Week Days

In [8]:
day_time['dayofweek'] = day_time['date_time'].dt.dayofweek
by_dayofweek = day_time.groupby('dayofweek').mean()
by_dayofweek['traffic_volume']  # 0 is Monday, 6 is Sunday

<ipython-input-8-e42a3e605df7>:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
Out[8]:
dayofweek
0    4893.551286
1    5189.004782
2    5284.454282
3    5311.303730
4    5291.600829
5    3927.249558
6    3436.541789
Name: traffic_volume, dtype: float64
In [9]:
plt.plot(by_dayofweek['traffic_volume'])
plt.show()

Isolating Data by Hour time , Business Days and weekends

In [32]:
day_time['hour'] = day_time['date_time'].dt.hour
bussiness_days = day_time.copy()[day_time['dayofweek'] <= 4] # 4 == Friday
weekend = day_time.copy()[day_time['dayofweek'] >= 5] # 5 == Saturday
by_hour_business = bussiness_days.groupby('hour').mean()
by_hour_weekend = weekend.groupby('hour').mean()

print(by_hour_business['traffic_volume'])
print(by_hour_weekend['traffic_volume'])

hour
7     6030.413559
8     5503.497970
9     4895.269257
10    4378.419118
11    4633.419470
12    4855.382143
13    4859.180473
14    5152.995778
15    5592.897768
16    6189.473647
17    5784.827133
18    4434.209431
Name: traffic_volume, dtype: float64
hour
7     1589.365894
8     2338.578073
9     3111.623917
10    3686.632302
11    4044.154955
12    4372.482883
13    4362.296564
14    4358.543796
15    4342.456881
16    4339.693805
17    4151.919929
18    3811.792279
Name: traffic_volume, dtype: float64

<ipython-input-32-634df4e6b376>:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
In [35]:
plt.figure(figsize = (8, 3))
plt.subplot(1 , 2, 1)
plt.plot(by_hour_business['traffic_volume'])
plt.title('Business Days Traffic Volume')
plt.xlim([7, 18])
plt.ylim([1500, 6300])
plt.subplot(1 , 2, 2)
plt.plot(by_hour_weekend['traffic_volume'])
plt.title('WeekEnds Traffic Volume')
plt.xlim([7, 18])
plt.ylim([1500, 4500])
Out[35]:
(1500.0, 4500.0)

Comparing all correlation Values to find higher one

In [62]:
corr_temp = day_time['traffic_volume'].corr(day_time['temp'])
print("Temp vs Traffic Volume:", corr_temp)

#traffic_flow['traffic_volume'].corr(traffic_flow['weather_main'])
#traffic_flow['traffic_volume'].corr(traffic_flow['weather_description'])

Temp vs Traffic Volume: 0.12831656852303905
In [63]:
corr_snow = day_time['traffic_volume'].corr(day_time['snow_1h'])
print("Snow vs Traffic Volume:", corr_snow)

Snow vs Traffic Volume: 0.001265349847175986
In [64]:
corr_clouds = day_time['traffic_volume'].corr(day_time['clouds_all'])
print("Clouds vs Traffic Volume:", corr_clouds)

Clouds vs Traffic Volume: -0.03293234866163948
In [65]:
corr_rain = day_time['traffic_volume'].corr(day_time['rain_1h'])
print("Rain vs Traffic Volume:", corr_rain)

Rain vs Traffic Volume: 0.0036965098350734324
We compared 4 coumns correlation value and found highest one is with temperature so will will plot a graph between traffic volume and Temperature
In [67]:
plt.scatter(x = day_time['temp'], y = day_time['traffic_volume'])
plt.title('Traffic Flow vs. Temp')
plt.xlabel('Temperature')
plt.ylabel('Traffic Volume')
plt.show()
In [68]:
by_weather_main = day_time.groupby('weather_main').mean()
by_weather_description = day_time.groupby('weather_description').mean()
In [69]:
by_weather_main['traffic_volume'].plot.barh()
Out[69]:
<matplotlib.axes._subplots.AxesSubplot at 0x7ff3c65e0610>
In [75]:
by_weather_description['traffic_volume'].plot.barh(figsize = (6, 9))
Out[75]:
<matplotlib.axes._subplots.AxesSubplot at 0x7ff3c6254f70>
Finally, We found that traffic volume during day times is gihy when it light rain or shower snow or its a drizzling weather.
In [ ]: