Attaching sensors to vehicles could provide a cheap and accurate way to monitor air pollution, road quality, or other aspects of a city’s health in real time. How can this be best utilized to monitor the health of a city? What if every vehicle with these sensors was feeding this information to a central database to be monitored in real time? Which vehicle fleet would be the most optimal, efficient and economical?

 This project aims to develop a simulator to test these ideas. Using GTFS data , we can experimentally simulates taxis, busses, ordinary consumer vehicles or just one vehicle owned by the company, to see which is the most economical and efficient. 

The problem we will focus on will be to experimentally simulate these vehicles driving within a city, sensing the health of the city's electrical grid by means of their electrical poles. If an electrical pole has fallen over or is otherwise damaged, the sensors on the car may be able to detect this and report it automatically to the electrical company, where instead of having to send someone to inspect every pole they could be monitored in real time 24/7. 

This simulation could also be applied to other structures of a city: like fire hydrants, streetlights or embassies. Or even use a different type of sensor altogether which would monitor air quality and assess this at different times of the day throughout the city. This air quality or electrical pole health data could then be viewed as a heatmap to identify problem areas of a city, where for air quality it could identify pollution sources or for electrical poles it could identify holes in the electrical grid.