"You know that feeling where you are on time in the morning and everything is going great and your commute is actually 10 minutes shorter than expected? Yeah, I don't either."
That's why I, along with some really smart people worked all summer on a project to help congestion in downtown Seattle. We had access to sensor data at some Seattle intersections and we worked to develop algorithms to identify:
Individuals Looking For Parking
For-Hire Vehicles Operating Without a Passenger
This is known as "vehicle cruising" and it is a major contributor to traffic congestion in downtown Seattle. To get a better understanding of where vehicles cruise, we used traffic sensor data to generate most-likely paths traversed using a modified Djikstra's algorithm. We manually segmented the paths by time and method of transportation to create ground-truth data. After segmentation, we introduced metadata to describe the trip and used a semi-supervised machine learning approach to label the data. Ultimately, we built a data pipeline using RethinkDB that fed a Python Flask web application. This interactive heat map of downtown Seattle can be used in real-time to visualize the relative levels of vehicle cruising in the city.