This research is a collaboration with Transit, which is an app that provides real-time transit and shared mobility information in more than 125 cities. The following studies examine how travelers use the Transit app.
1. Transit Information Utilization during an Extreme Weather Event: An Analysis of Smartphone App Data (2018). Remy, Brakewood, Ghahramani, Kwak, and Peters.
Summary: Extreme weather events such as heavy snow can severely disrupt urban transportation systems. When this occurs, travelers often seek information about the status of transportation services. This study aims to assess information utilization during an extreme weather event by analyzing data from a smartphone application (“app”) called Transit, which provides real-time transit and shared mobility information in many cities. This research focuses on a snowstorm that hit the northeastern USA in January 2016 and severely disrupted transit and shared mobility services. An analysis of Transit app data is conducted in four parts for New York City, Philadelphia, and Washington, D.C. For more information, read the paper here.
2. Real-Time Riders: A First Look at User Interaction Data from the Backend of a Transit and Shared Mobility Smartphone App (2017). Authors: Brakewood, Ghahramani, Peters, Kwak and Sion.
Summary: New data sources from smartphone apps offer the opportunity to study transit travel patterns across multiple metropolitan regions and transit operators at little to no cost. Also, some smartphone apps integrate other shared mobility services, such as bikesharing, car-sharing, and ridehailing, which provide a multimodal perspective. The objective of this research is to take a first look at an emerging data source, which is backend data from user interactions with an app called “Transit”. For more info, read the paper here.
3. An Exploratory Analysis of Intercity Travel Patterns Using Backend Data from a Transit Smartphone Application (2017). Authors: Ghahramani, Brakewood and Peters.
Summary: Many of these apps are available in multiple cities and automatically detect a user’s location via the location services in the smartphone. The multi-city nature of these apps provides a unique opportunity to understand how transit riders seek information as they travel between cities. The objective of this paper is to identify intercity travelers to/from the New York metropolitan region using one month of backend data from an application called “Transit”. Intercity travelers are identified based on the number of days each user has opened the app inside and outside of the New York region. For more information, download the paper here.
4. Interactive Travel Modes: Uber, Transit and Mobility in New York City (2017). Authors: Davidson, Peters and Brakewood.
Summary: Using two unique data sets this paper explores how smartphone applications may enable multi-modal transport behavior. The data sets are user-level interactions from a smartphone application called Transit (which seeks to easily informs users of transit, bikeshare, carshare, and Uber access based on their geographic position), and Uber ride-hail origin data released publicly through the New York City Taxi and Limousine Commission. For more information, read the paper here.
5. Trends in Mobile Transit Information Utilization: An Exploratory Analysis of Transit App in New York City (2016). Authors: Ghahramani and Brakewood.
Summary: The objective of this research was to perform an exploratory analysis of the use of a smartphone application known as Transit App, which provides real-time transit information and trip planning (schedule) functionality. Backend data from Transit App were examined by time of day and day of week in the New York City metropolitan area. For more information, read the paper here.