Recommended
More Related Content
Similar to Project 2
Similar to Project 2 (20)
Recently uploaded
Recently uploaded (20)
Project 2
- 1. Bluebikes Bikeshare By Drew Jones and Michael McCluskey
- 3. Purpose “Redistribution is the single most costly part of running a bikeshare program” Purpose •Identify areas to implement redistribution incentives. Benefit • Decrease daily redistribution costs.
- 4. What affects customer participation? ● Distance to drop-off station ● Elevation & Gradient ● Time of day ● Weather ● Motivation
- 5. Specific Questions - Breaking it Down • How many trips start and end at each station? • What is the difference in supply? Supply • What are the elevations of all the stations? Elevation • What is the supply of stations at various elevations? • Which are over-supplied higher elevation stations nearby undersupplied lower elevation stations? Both
- 6. Data Each row represents a single trip that an individual took with one bike. Key Fields
- 8. Assumptions & Limitations • Bluebikes is currently rebalancing using trucks. • Customers will prefer riding downhill. Assumptions • Elevation data was not in the same database as trip/station data. Limitations
- 9. Elevation
- 11. Supply
- 13. Top 5 over/under supplied Stations
- 15. Analysis Overview Station Supply • Count start_station_id • Count end_station_id Supply Difference • End Count – Start Count High Imbalance • More than 5 bikes over/under supplied on an average day Elevation Data • Google API Target Stations • Over-supplied at higher elevation • Under-supplied at lower elevation
- 16. Conclusion Elevation tends to align with rebalancing needs. ○ Low Elevation Over supplied ○ High Elevation Under supplied ○ This seems to support our hypothesis that elevation deterred people from dropping the bikes off at certain stations.
- 17. Recommendations ● Target South Station and Ames St at Main. ● Trial run incentive redistribution program.
- 18. Station Cluster 1 ○South Station Undersupplied • 10 bikes average under supplied Arch St at Franklin St Oversupplied • 8 bikes average surplus Post Office Square Oversupplied • 7 bikes average surplus
- 19. Station Cluster 2 ○Ames St at Main St Oversupplied • 20 bike average surplus MIT Stata Center at Vassar St / Main St Undersupplied • 15 bikes average undersupplied Kendall T Undersupplied • 12 bikes average undersupplied
- 20. Next Steps ● We recommend A-B testing to see if customers will engage in an incentive program. ● Ideally we would look for a relationship between the size of an incentive and how far/high a customer will travel to earn it. ● Identify cost savings of a rewards program vs current redistribution methods.
- 21. Data Dictionary for bluebikes_stations Field Name Description Use Latitude , Longitude Coordinates Map the stations. Input for Google Elevation API id Id of station Pk to match to other station IDs in the bluebikes_2019 table name Name of station Used in tooltips to differentiate stations by name instead of ID
- 22. Data Dictionary for bluebikes_2019 Field Name Description Use start_time Coordinates Map the stations. end_time District that station is in. Differentiate between stations in legend on map. start_station_id Id of station Pk to match to other station IDs in the bluebikes_2019 table. end_station_id Name of station Used in tooltips to differentiate stations by name instead of ID.
- 23. Questions?
Editor's Notes
- have the opportunity to implement customer incentives to move bikes from over-supplied higher elevation stations to nearby undersupplied lower elevation stations
- Cleaning: Had to remove 3 stations with NULL ids Removed 9 stations from map visualizations which were not used in 2019
- Got elevation from Google API Used a python script to extract elevations of each station by the latitude and longitude pairs provided in our initial dataset. Automatically entered the latitude and longitude of each station to get an elevation value Matched that elevation value
- What are the elevations of all the stations?
- How many trips start and end at each station? Count start_id, end_id What is the difference in supply? Count (end_id – start_id)
- Counted trips leaving each station and coming to each station every day to get a measure of where bikes needed to be relocated. Assigned a measure of over, under, or adequately supplied. Identified stations of high imbalance More than 5 bikes over/under supplied on an average day Pulled elevation data from outside source Searched for over-supplied stations which had lower elevation under-supplied stations nearby Wanted customers to have an easy bike trip so they could be easily motivated to move bikes for small incentives
- Elevation tends to align with rebalancing needs Most stations with too many bikes were at low elevation Most stations with too few bikes were at high elevation This seems to support our hypothesis that elevation deterred people from dropping the bikes off at certain stations
- There are two groups of stations where we found opportunities for rebalancing without making customers bike uphill or over long distances We recommend targeting these areas for trialing an incentive program to encourage customers to rebalance bikes.
- Bigger picture to see where it is relative to other stations. Significant differences in Elevation make rebalancing an easy ride for customers The two stations with a surplus of bikes are about 1.5 - 2.5 meters higher in elevation Stations are relatively close together
- Extra Bikes from Ames St at Main St (+20b) make up a large chunk of deficit at MIT Stata Center at Vassar St / Main St (-15b) and Kendall T (-12b) Stations are at roughly the same elevation and are within a few blocks of each other