Pi Weather – Will Cuddihy (SMUS)
TL;DR: My project is a black ice predictor program written in Python, and is designed to be run on a Raspberry Pi computer with a Sense HAT display attached. See the bottom for important instructions.
Introduction and Background:
Black ice is a dangerous phenomenon each winter, and I wanted to provide a way to reduce that danger. In the US, on average, there were 156,164 car crashes, 41,860 people injured, and 521 people killed, all from icy roads alone. In fact, bridges are the most dangerous road surfaces for black ice to form because they are cooled from both the top and bottom, giving them more surface area to freeze. In Fort Worth, on February 11, 2021, the danger of black ice was proven yet again: There was a 133-car pileup on a bridge, caused by black ice, killing 6. Black ice also tends to form at the end of a weather system and most likely to form in the morning or at night because of the colder temperatures experienced by those times.
Purpose: The purpose of this project is to provide an easy and convenient way to tell users the likelihood of the presence of black ice using a program written in Python. The program displays a colour, between blue (cold) and red (hot), on a small LED screen. The colour is based on the average temperature over the past 24 hours.
Design Criteria: A successful design will include a program that can quickly, easily, and clearly tell a user if there is black ice present using a Raspberry Pi computer and a Sense HAT display/sensor module.
Methodology: The success of this idea was fully dependent on working code. The first program was simple and mostly just an outline of where I wanted to go next: The unit was only able to show 3 colours on the display and had no way to tell the user what the danger level was. The second program was more successful, because the unit was able to display colour between blue (cold) and red (hot). The final program made the display flash if the average temperature over the past 24 hours was below 0 C, helping the user to know what the possibility of black ice was even more easily.
Results: Overall, my innovation was successful, clearly showing the user if black ice could be a risk for them. One change that I was unable to make but would eventually like to implement is to ‘weight’ the most recent temperatures so that the user can be more confident in the information given.
Conclusion: In conclusion, I created a program that can show a person if there is a risk of black ice on sidewalks or roadways. This will hopefully save lives by allowing people to make an informed decision as to travel in dangerous conditions.
Acknowledgements: Thanks to Mr. DeMerchant and Mr. Floyd for their help and support throughout this project.
Full report with install instructions: tinyurl.com/rbd7yydn