The heart of this project is data analysis. In order to have meaningful and plot-able data, the team has to first
sort through massive amounts of data. For example, the table which measures the representative layer thickness of
various sections of highways throughout the country has over 38,000 entries, each entry with a dozen attributes.
Traffic data, likewise, has over 16,000 entries spanning over a 20 year period. To identify the road section with the
most traffic, we crossed referenced yet another database containing over 2,500 entries.
Data processing was done using the Python dataframe method.
Because the scope of the data is so large, we balance analysis efforts between national average and focus studies on specific states.
For obvious reasons, we are interested in the roads in Hawaii. Keep in mind the DoT manages federal highways, and
there are only three such roads in the state of Hawaii: H-1, H-2 (201), and H-3. All other roads are maintained by the State and County.
Since the sample size of Hawaii roads is so small, we also explored other states such as California.