Analysis of Heavy Traffic Indicators on I-94 Interstate Highway

From my analysis of this project, I learned how to discover the indicators for heavy traffic along I-94 Interstate highway.

The traffic data was recorded by a station located approximately midway between Minneapolis and Saint Paul. The station records westbound traffic only which means, they record cars moving from east to west.
It mainly focused on the westbound traffic near that station and throughout the analysis I discovered two major indicators that coontribute to heavy traffic on I-94 Interstate highway.

These are:

  • Time indicators
  • Weather indicators

The time indicators are:

  • When compared to cold months, traffic is usually heavier during the warmer seasons (March-October) and (November–February).
  • Business days typically have more traffic than weekends.
  • The rush hours on business days are at 7 hrs and 16 hrs.

The weather indicators are:

  • Snowfall is forecast.
  • Thunderstorm with drizzle in the vicinity
  • Snow and light rain

You can find the project on my github repository at: I-94 heavy traffic analysis.

The notebook can be found here also:
i_94_interstate_traffic_analysis.ipynb (362.8 KB)

Click here to view the jupyter notebook file in a new tab

1 Like

Hi @o.abucheri,

Great job finishing your analysis on traffic indicators. The above summary of your major findings was really helpful. Did you consider adding the above summary to your project introduction?

You may have already answered this since you listed weather as a major contributor to traffic volume but is there a way to look at the time data on the bad weather days. Maybe since it will rain or snow, people adjust their schedule and do errands earlier or at different times or go to the store more to stock up on supplies.

Great explanation of dataset and your analysis approach. Great job using markdown cells to provide more details about your analysis.

Great use of histogram charts to compare day and nighttime traffic volume. You did a great job adding charts to explain your analysis. You may consider adding a chart title to Out [13] line plot and Out [14] line plot.

Great job explaining inconsistencies in the data set:

From this we can see that between rows 176 and 177 , We’re missing 4 to 5 hours of time. That’s why we have a difference in the number of rows in the dataset.

Great explanation of the traffic volume range and the percentile breakdown: This made the project analysis easy to follow and did a great job of guiding me through your analysis:

The traffic volume varried from 0 to 7,280 with an average of approximately 3,260 cars hourly.

Only 1,193 cars or fewer passed the station for every hour around 25% of the time, this most likely happens at night or during road construction. However, around 75% of the time, traffic volume was four times as high with about 4,933 cars or even more than that. From this observation, we are going to analyze and compare the data in terms of daytime and nighttime

Please click the triangle bullet for additional suggestions:

Minor spelling errors:

Markdown section above In [4]
original sentence - The station records westbound traffic only wich
spelling correction - The station records westbound traffic only which

Markdown section above In [14]
original sentence- experienced mostly during the winter time between Nivember
spelling correction - experienced mostly during the wintertime between November

Markdown section below In[7]
original sentence - difference in the nubmer
spelling correction - difference in the number

In [6] and In [7] may be self explanatory but you may consider adding inline comments to explain your code.

In [7] print statement is explained in the markdown following In [7] but you may consider adding a little more description as to what the print result means

daytime ==>  (23877, 9)
nighttime ==>  (24327, 9)

As you can see, there is a difference in the nubmer of rows, where daytime rows are less than that of nighttime rows. This might be because of some missing data in the dataset.

Great job and I enjoyed reading your project!

1 Like