designed during my Purdue University period ,May 2022-August 2022
- Designed and developed a prediction tool using python which helps in forecasting bike traffic during different time periods and recommended positions to install sensors for real time safety monitoring..
- Developed the prediction model using multivariate linear regression model by analyzing relationships between daily weather forecast data (5 attributes including temperature, (low/high temperature and precipitation and bicycle traffic using sklearn.
- Implemented R square algorithm on calculating positions with predicted maximum traffic flow for installing sensors.
When we designed this project, we were asked about these three questions.
Q1. You want to install sensors on the bridges to estimate overall traffic across all the bridges. But you only have enough budget to install sensors on three of the four bridges. Which bridges should you install the sensors on to get the best prediction of overall traffic?
Q2. The city administration is cracking down on helmet laws, and wants to deploy police officers on days with high traffic to hand out citations. Can they use the next day's weather forecast(low/high temperature and precipitation) to predict the total number of bicyclists that day?
Q3. Can you use this data to predict what day(Monday to Sunday) is today based on the number of bicyclists on the bridges?
Our question1re.py,question2re.py and question3.py answer these questions accordingly.