In the right hands, automated systems like computer vision and artificial intelligence can act as tools for humans to make better decisions. For transportation planners and advocates, they can lead to better use of resources and better distribution of space for all road users.
At Transportation Techies’ Playing with Traffic Meetup, locally- and internationally-focused coders shared how they have combined machine learning with human analysis to build safer streets around the world.
A bot that aggregates unpaid driving citations
Daniel Schep and Mark Sussman shared their Twitter bot, @howsmydrivingDC. When someone tweets a license plate number at the bot, typically in response to the car’s driver behaving dangerously, the bot accesses Washington, DC’s Department of Motor Vehicles’ (DMV’s) database on unpaid tickets for parking and automated enforcement – like speed cameras – infractions. The bot then replies to the tweet with a spreadsheet of the vehicle’s entire tab of unpaid tickets.
The #BikeDC community has interacted enthusiastically with the bot due to the high rate of cars blocking bike lanes in the District, and this has led to a wealth of data for Schep and Sussman to explore. From 459 tweets, the bot has uncovered over $300,000 in unpaid fines from 2,069 citations.