Washington Metro Area Transportation Analysis
The Washington Metropolitan Transit Authority (WMATA) provides bus and rail transit to the Washington region
Transporting over 300,000,000 passengers a year WMATA is facing many challenges concerning safety, ridership, and funding
Is metro getting better or other ways of transportation are taking over its customers?
WMATA API provides only live data Use DC area transportation data from WMATA, Taxi and Uber to understand relationships between metro performance and the use of other modes of transportation Gather monthly ridership data for determining time of year impacts
Q1: Does time of year impact demand for transportation resources? Yes
The graph of the ridership data is characteristic of a time series.
- Long Term Trend or Movement
- Seasonal Movement
- Long-Term Cyclical Movement
- Irregular Movement
Seasonal Variation Found by the Average Percentage Method
- Each month ridership data expressed as a percentages for the whole year ridership.
- Percentages from corresponding months of different years are averaged.
- The resulting twelve percentages are the seasonal index.
Q2: Does Metro performance impact the demand for other transportation resources?
Q3: Can one predict the availability of transportation resources based on metro past performance?
No strong relationships between KPIs and Ridership
Outcome & Recommendations
Time of year impacts Metro demand
Metro ridership loosing customers to other means of transportation even though economy is strong, and government employees get 100% refunds for rides
Metro&Bus lost 600K customers and Uber got 1.4M customer. Where those additional customers come from?
Current metro KPIs are not significant factors in ridership numbers, however the Rail On Time Performance KPI has the strongest impact on Metro Ridership
Metro needs to share their data better and if metro wants more customers it should probably measure different set of KPIs
What if we had more time? :)
Find individual vehicles/Lyft/other Taxi ridership data and compare to other cities
- Yegor Kryukov (solution architecture, polynomial regression functions, bus data)
- Marc J. Pitarys (presentation, statistics and models, metro data)
- Sonya Smirnova (linear regression functions and plots, ride-sharing companies data)
- Abubeker Mohammed (idea, taxi data)