In these sample papers from the 2016 M3 Challenge, students used mathematical models to categorize car usage habits of U.S. drivers and evaluate car-sharing business options. Each paper has a cover sheet that indicates what judges liked about the paper and what could have been improved.
“Activities of automobile are phenomenons that resulted by the keeping up with the speed of economy. Therefore, it is important for us to understand who’s driving and how that will relate to the carsharing area. In Part I, we used probability multiplication rule to calculate the percentage of current U.S. drivers in each category low, medium, and high for all combinations of the two specified factors, the amount of time using the car and miles driven per day, according to different age group.”
“This study seeks to portray the distribution of the proportion of drivers in the U.S. based on daily time spent driving and miles driven. These distributions are then applied to study the possible revenue a business could generate in the cities of Richmond, Riverside, Knoxville, and Poughkeepsie. The salient factor that was implemented into the models was the income bracket of the city; this determined the likelihood that the consumers would opt for the highest convenience option over the cheapest one. The results showed that Riverside had the highest potential for revenue, and the One-Way Sharing Floating service provided the best outcome.”
“Car sharing is one of the fastest growing areas of the transportation and automotive industry; it involves billions of dollars of invested capital, millions of participating users, and hundreds of thousands of cars. As these numbers are sure to increase in the future, it is vital to identify new markets and new strategies for providing car-sharing systems. In order to do so, the driving habits of Americans must be studied, and the merits of the various car-sharing systems must be compared. We were asked to find the percentages of current American drivers in nine separate categories: all possible combinations of low, medium, and high daily driving distance; and low, medium, and high daily driving time.”
