Real-Time Riders: A First Look at User Interaction Data from the Back End of a Transit and Shared Mobility Smartphone App

Abstract

A fundamental component of transit planning is understanding passenger travel patterns. However, traditional data sources used to study transit travel have some noteworthy drawbacks. For example, manual collection of travel surveys can be expensive, and data sets from automated fare collection systems often include only one transit system and do not capture multimodal trips (e.g., access and egress mode). New data sources from smartphone applications offer the opportunity to study transit travel patterns across multiple metropolitan regions and transit operators at little to no cost. Moreover, some smartphone applications integrate other shared mobility services, such as bikesharing, carsharing, and ride-hailing, which can provide a multimodal perspective not easily captured in traditional data sets. The objective of this research was to take a first look at an emerging data source: back-end data from user interactions with a smartphone application. The specific data set used in this paper was from a widely used smartphone application called Transit that provides real-time information about public transit and shared mobility services. Visualizations of individuals’ interactions with the Transit app were created to demonstrate three unique aspects of this data set: the ability to capture multicity transit travel, the ability to capture multiagency transit travel, and the ability to capture multimodal travel, such as the use of bikeshare to access transit. This data set was then qualitatively compared with traditional transit data sources, including travel surveys and automated fare collection data. The findings suggest that the data set has potential advantages over traditional data sources and could help transit planners better understand how passengers travel.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 7 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.