The Deerfoot Trail commute analysis project involves the calculation of commute time statistics along with the prediction of commute times given a set number of inputs. This project will also determine how accurate a machine learning model is in predicting commute times given these inputs. The analysis is interesting since it uses readily-available public data to provide predictions that could potentially benefit a large number of people. While the analysis focuses on a fixed time period for one specific roadway, it could potentially be expanded to predict commute times for major roadways across the country in real-time. Besides providing the project team with an opportunity to learn and apply Spark concepts, the results of the project could have real-world applications in transportation forecasting, planning, and safety.
These are jupyter notebook files. You should have jupyter installed on your machine.
'deerfoot.csv' file
'deerfoot_part2-1.csv', 'eng-daily-01012013-12312013.csv' and 'eng-daily-01012014-12312014.csv' files.