MBTA Project
I analyzed how fare increases affect ridership in different modes (bus, subway, commuter rail) and evaluated whether these fare increases were equitable. Furthermore, I analyzed the elasticity of each mode of the MBTA to understand how these fare increases change travel behavior.
Main Python Skills Used
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Graphing/data visualization using matplotlib
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Using numerical operations on Pandas dataframes
Tasks
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Plotted graphs to visualize base fares, incomes of low-income & not low-income riders, and number of low-income and not low-income riders using Pandas
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Created an impact factor and created graphs to visualize the impact of a fare increase of $0.50 across all modes
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Varied fare increases to see how it would affect the impact factor values and visualizations and found new combinations to get the impact factor as close to 1 as possible (most equitable)
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Calculated the elasticity of demand for ridership vs. fares for the bus and graphed it on a line chart
Outputs + Conclusions
Default Data:
Impact factor with $0.5 increase across all modes:
The impact factor is around 1.8736 when raising each fare by $0.5. When the impact factor is >1, low-income households are more negatively affected. The low-income riders are more affected if all modes of transportation get an "equal" bump in price as the slight change in price is a higher percentage of a low-income rider's income. In contrast, this fare increase negatively affects not low-income riders by around half the amount it affects low-income riders.
Most Equitable Fare Increase: Bus: 0.25, Subway: 0.3, Commuter Rail: 3.5. Impact factor: 1.0006.
The strategy for achieving equity was to increase the price of the commuter rail significantly more than the bus or subway because fewer low-income people frequently take the commuter rail. Therefore, the people most affected by this price change would be on the wealthier side.
Elasticity of Demand for Bus:
