Starts Monday: Last Beginner Python & Math Course of 2019! Enroll Now

Made at Metis: Data Science on the Move – Improving Cycling Safety and Forecasting Rideshare Use

By Emily Wilson • April 25, 2018

              Recent Metis graduate Rebekah Cunningham riding with a group near Mt Zion, Utah

This post features two final projects created by recent graduates of our data science bootcamp.
Take a look at what's possible in just 12 weeks.

_____

In this month's edition of the Made at Metis blog series, we're highlighting two recent student projects that focus on the intersection between transportation and data science. One project is a video-based car detector to improve safety for city cyclists, and the other presents a way to better forecast hourly Uber demand across New York City neighborhoods. Read more about both below:

Car Back! A Video-Based Car Detector for Cyclists
Rebekah Cunningham
Metis Graduate

Rebekah Cunningham loves to hit the open road on her bicycle, enjoying the fresh air while exercising and taking in the views. But the hobby can be a dangerous one, especially when navigating city roads, where cars generally rule the roost. To address the dangers associated with city cycling, Rebekah created Car Back!, a video-based car detector for cyclists as her final project at Metis. 

In a recent blog post about the project, she explained that the phrase "Car Back!" is what one cyclist shouts to another to alert them of an approaching car from behind. In the post, she detailed the project's ambitious goal: "My vision is to be able to attach a camera to the back of my bike, near the seat which captures video in real time and alerts of any cars that are approaching from behind. The alert would be an audio cue that is played in one of the apps that is already running -- Strava, Spotify, or Audible as examples. " 

To get started...she went cycling, of course! "I strapped a GoPro to the back of my bike and set out for a number of routes to collect video data to train a model.  I needed to be thorough in capturing a variety of weather conditions, lighting conditions, and traffic conditions.  From these videos, I extracted frames at 6 frames per second using ffmpeg and set about hand-labeling these frames for approaching cars.  I drew rectangles around approaching and not-approaching cars and labeled them appropriately using a tool called RectLabel," she wrote.

Read the full post here to learn how she got from that first step to the end result – a model with 97% recall. (And see a demo video, too!)

_____

Forecasting Uber Demand in NYC
Ankur Vishwakarma
Metis Graduate

Ankur Vishwakarma wanted to blend three things he likes into his final bootcamp project: urban transportation, geographic visualizations, and time series forecasting. To make it all work together, he decided to focus on forecasting hourly Uber demand across New York City neighborhoods. This type of improved forecasting could help customers and companies alike in a number of ways, including alerting drivers of upcoming demand, improving customer satisfaction, and aiding traffic planning. 

"In addition to time-lagged features (such as previous week’s demand), I added information specific to each neighborhood to improve my predictions," he wrote in a blog post about the project. "As a final result, I obtained relatively accurate unique forecasts for all neighborhoods in NYC."

How'd he do it? Read the full post for a detailed breakdown of each step (see his project pipeline pictured below), including what went right, what went wrong, and how it all turned out. 

_____

Curious what else Metis graduates have created as final projects? See more examples here


Similar Posts

alumni
Made at Metis: Predicting and Mapping Using Geographic Data

By Emily Wilson • February 14, 2019

Bootcamp graduates Joyce Lee and Matt Maresca covered a lot of ground using geography as a framework to design their final projects. Lee did on a county-by-county analysis to predict mortality rates from overdoses, while Maresca used satellite imagery to map farmland, urban development, and natural resources in Shanghai. Read the post for more!

alumni
How the Data Science for Social Good Model Guided this Grad's Career Path

By Emily Wilson • July 12, 2019

Considering her career path and where she is now, it’s remarkable to think that Tiffany Moeller has never taken a college-level math course. Read how she went from earning a degree in counseling to finding her way into data science and engineering, all the while finding motivation from the idea of using data for social good.

alumni
Bootcamp Grad Finds a Home at the Intersection of Data & Journalism

By Emily Wilson • July 03, 2019

Bootcamp graduate Jeff Kao knows that we’re living in a time of heightened media distrust – and that’s precisely why he relishes his job in the media. “It's heartening to work at an organization that cares so much about producing excellent work,” he said of the nonprofit news organization ProPublica, where he works as a Computational Journalist. Read Kao's full story here.