Q. How do you recommend we start?
A. We recommend you start by researching the topics and coming up with examples for nominal and edge cases in each
category. Discuss with your team what safety problems are presented in the edge cases for each category. Discuss what
features of that edge case separate it from nominal cases (is it more likely to be on a curved road? Is it found in urban or
rural areas? etc.). What technology might be used to identify these edge cases (neural networks, road geometry,
proximity, etc.).
Next, start small. Prototype a model that can classify a specific edge case for your category based on its features.
Continue to expand your model to make it more robust to different forms of that edge case. Add other edge case
detection if you have time!
Q. What data sets should we use?
A. We recommend using the following portals due to the variety of data sets they offer. We’ve also included the link to
Overpass-Turbo which is an online query interface to the OpenStreetMap project. If you find other data sets online, you
are welcome to use them to enhance your project.
Data.gov: https://www.data.gov/open-gov/
Detroit: https://data.detroitmi.gov/
Ann Arbor: https://www.a2gov.org/services/data/Pages/default.aspx
Chicago: https://data.cityofchicago.org/
Pittsburgh: http://www.wprdc.org/
New York: https://opendata.cityofnewyork.us/data/
Washington DC: https://opendata.dc.gov/
Boston: https://www.ctps.org/
San Francisco: https://datasf.org/opendata/
Palo Alto: https://data.cityofpaloalto.org/home
MTCF: https://www.michigantrafficcrashfacts.org/
Overpass-Turbo: https://overpass-turbo.eu/
Q. What format are the data sets in?
A. Depends on the source, but most data sets follow this structure: entry ID, latitude, longitude, attribute ID/name. The
file types could be csv, kml/xml or json file types. Libraries can be used to read these file types.
Q. How can I use the data sets in my model?
A. In Python3, you can use the Pandas or GeoPandas library to read data from files into data frames. Your model can
then be constructed to query, filter, visualize, and perform calculations on the data frames. Examples can be found
online.
Q. What regions should we target?
A. We would like you to focus on the continuous 48 Unites States, excluding Hawaii and Alaska.
Q. What are you looking for in our final presentation?
A. We would like to understand your thought process for an end-to-end solution. Why did you choose the object and
edge case you did? What features exist for that edge case? Show a map of your data set with classifications for the
nominal and edge cases. We’ve included a representation of an edge case study below for you to reference. The layout
is only a suggestion, feel free to format your presentation how you see fit!