Inspiration

Accessibility to bus stops ,efficient bus network , identifying the reasons of delays.

What it does

From the model we are able to identify the station names which on average have the maximum delays( that is maximum difference between the expected time to departure and actual time of departure)

As the Staten Island bus network is quite old so we have build the model that identifies the acceesibility of the bus stop (walking to bus stop). accessibility was defined to be bus stop within 1000 feet. This helps in identifying the areas where does not have a good accessibility to bus stops.

How we built it

Data analysis was done in python. The time series were plotted for the delta(difference in scheduled departure -actual departure of each stop for all the days of October 2014) we found that the day (weekday or weekend ) does not make much change in the delta. Further we analyzed the delta vs the stops using the line plot and we found that there are some stops(bad stops) where the delay is always greater as compared to the other stops on the route.Then in order to visualize the entire stops vs delta we did count of delta on each stop for entire month and found that some stops have very steep (up-word) peak. Which means that these are the stops which are causing lot of delay in the schedule of bus.

Challenges we ran into

Selection of correct data and converting to proper format took lot of time.

Accomplishments that we're proud of

  1. We were able to identify the areas where the accessibility of the bus is poor.
  2. We were able to identify and plot (on Map) the stations which are causing the delay in overall bus schedule.

What we learned

How to conclude in time bound scenario based on various data sets.

What's next for SI_HACK_BAD_STOPS

To reallocate the bus stops and the route of busses to reduce the bad stops(greater delay in schedule)

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