Funded by the National Institute of Justice, this web application was created to aid law enforcement agencies help alleviate organized act of human trafficking and other associated crimes. Traffic Jam organizes scraped data from various escort or classified advertising websites and finds patterns of movements and information. The information is further studied via machine learning in order to help detect human trafficking related advertisements. Currently Traffic Jam 2.0 is utilized by law enforcers in America (FBI, CIA, etc) and in parts of Canada. 

Having found usability problems with Traffic Jam 1.0, the team decided to build a mySQL database that would replace flat files of 50 million records. For optimal flexibility we decided to build the database in a Entity-Atrribute-Value model. For this part of the project, I was in charge of designing the database of processed data as well as building the app fit for the machine learning system.

The challenge

I developed Traffic Jam 2.0 in Django, which was a challenge because I was initially not familiar with this particular framework. However my knowledge of Ruby on Rails helped me quickly understand the workings of Django. One interesting aspect that differentiated this project from other web applications that I've built was that I had to keep in mind of the user imput for machine learning at all times. With the knowledge that I had about interaction design and user models, I had to stratigically plan out the features of the application and update the database accordingly. 

reflections

I am glad to have had the opportunity to work with the Auton Lab to help society in greater means and hopefully prevent trajedies of many young women and men, especially of my age, from the United States, Canada and many other countries. I am planning on continuing to help this problem through other means. 

Unfortunately the details of this cannot be publicly viewed as a security measure.