Algorithms supporting Active Transportation

Motivation: Active Trasnportation:   Public health researchers know that changes in infrastructure (like adding bike lanes, widening sidewalks, and retiming lights) sometimes increase the number of people who choose to walk and bike, but often do not. Previous studies of transportation behavior changes have relied on retrospective surveys, which are known to be unreliable. The Computing for Active Transportation project is working with public health researchers at UCLA to develop tools and algorithms to bring more objective data analysis into these important public health discussions.

Student Activities:   Students on this project will develop tools and algorithms for analyzing transportation activity logs. GPS data loggers and smartphone apps can generate massive quantities of real-time time-location data. Students on the Computing for Active Transportation project will develop algorithms to automatically classify each trip recorded by these sensors into categories. For example, we will separate work trips, school trips, childcare trips, and grocery trips from other types of trips, and we will also categorize trips by their total distance. This categorization will enable finer grained analysis of behavior changes by type of trip, rather than just by overall rate of use of each transportation type. To complete this categorization task, we will explore unsupervised (clustering-based) machine learning algorithms and, when needed, supervised (classification-based) algorithms.

Kinetic Typography Project:   Kinetic typography puts text in motion, resulting in new and creative storytelling (for example, see this video) This project will investigate automating the process of creating kinetic typography animations. We will work towards a live, interactive resource that demonstrate the principles and approaches emerging from this project.

Mentor: Professor Julie Medero

Ben Professor Medero's research expands what's possible in natural language processing. In recent projects Prof. Medero and her students have created novel methods to measure text readability and readers' comfort level through feedback from an iPad's or iPhone's accelerometer. She and her students also develop algorithms for summarizing texts' content and re-rendering texts at different reading levels. As part of a family of avid cyclists, Prof. Medero's 2017 research also includes algorithm development for active-transportation projects in the region. Prof. Medero got her REU start as a participant at Swarthmore College, and she still considers Claremont a suburb of Philadelphia.

Required Background

Background including a thorough data-structures course, e.g., at the level of CS3, would help participants contribute to this project. An algorithms background would be a plus.