Using the information given, analyzing and mapping it, it is possible to visualize the research question and put it in an objective matter for a broad discussion. According to the present visualization exercise, these are the following conclusions:

-Scenario 1: In a flat trip, an average bike trip using the data analysis is twice as seen in the internet search. But either bike or bus is equal regarding regular time according to Google maps. For this kind of condition, the regular user can profit from taking the bus or the bike at the same time. The "Bike on transit" feature makes the trip a simpler chose base on the climate or traffic condition of a particular day, and by forecasting in advance, it could represent an advance for the user. 

- Scenario 2: On a topographic trip, the average bike trip using the data analysis and the internet search takes the same time. Giving this similarity, the potential savings in time using a mix of bike and bus for overcoming the altitude difference is highly appreciated. 

- Scenario 3: On a trip with physical barriers, same as scenario 1, an average bike trip using the data analysis is twice as seen in the internet search. Giving that according to Google maps, the potential saving in time is equally effective, the question that a user should do is if the detours needed to trespass the barriers are better to take the bus. Here the question of climate or traffic is equally valid.

Final conclusions: Giving that the bike sharing and connectivity with the bus are relatively new programs, the possible outcome of the given data (second quarter of 2016) may be not entirely effective as of today. Extended and update data sets will be valuable for a future exploration. 

The potential change in the way people move in Pittsburgh could be highly productive when combining different means of transportation, and when the data can provide enough resources to make a good decision how to move in the city. I have chosen merely three scenarios out of the infinite possible outcomes connections between stations, and just assuming a traditional commute to downtown. Amplify this research to more places in the city, using update data, and doing it public would be very beneficial for everyone.