1. Juan Mejia Rosario Antunez Michael Safdieh
Senior Design Project
Juan Mejia
Rosario Antunez
Michael Safdieh

2. Counting the number of passengers in MTA city buses.
There is a need to know how many people are traveling in MTA city buses at a specific time in order to excel the service that is currently being provided.

3. Does not solve the problem
We have a problem!
There is a need to know how many people are traveling in MTA city buses at a specific time.
The current method: Some MTA city buses have sensors on the doors.
Does not solve the problem

4. Does not solve the problem
We have a problem!
There is a need to know how many people are traveling in MTA city buses at a specific time.
An approach is to count the number of metro-card swipes and coin fares.
Does not solve the problem

5. Who will explain this to New Yorkers?!!!
We have a problem!
There is a need to know how many people are traveling in MTA city buses at a specific time.
There are express buses --->> Simply cannot slow down the service just for the purpose of counting customers.
Who will explain this to New Yorkers?!!!

6. We have a problem! Oh well :(
There is a need to know how many people are traveling in MTA city buses at a specific time.
People get into the bus without paying the fare.
Oh well :(

7. Little kids and handicapped people.
We have a problem!
There is a need to know how many people are traveling in MTA city buses at a specific time.
Not all passengers pay for the bus
Little kids and handicapped people.

8. We have a problem!
There is a need to know how many people are traveling in MTA city buses at a specific time.
How do we control when people leave the bus?
Installing hardware sensors in each city bus is not economically feasible.

9. Solution to the problem
Most MTA city buses have 6 cameras.
Use of image recognition to count the number of distinct faces in a bus at a specific time.
Use of machine learning to be able to increase the accuracy of the data.

10. Specification/main ideas of the product
Web application.
User: MTA bus dispatcher and MTA yard dispatcher.
Aiming for real time processing.

11. Conclusion
It is our main purpose to create a product that can solve the current problem in an efficient way.
We plan to use Machine Learning and Image Processing techniques in order to make the product elegant and efficient.

12. Questions?