Local and regional planners struggle to keep up with rapid changes in mobility patterns. This exploratory research is framed with the overarching goal of asking if and how geo-social network data (GSND), in this case, Twitter data, can be used to understand and explain commuting and non-commuting travel patterns. The research project set out to determine whether GSND may be used to augment US Census LODES data beyond commuting trips and whether it may serve as a short-term substitute for commuting trips. It turns out that the reverse is true and the common practice of employing LODES data to extrapolate to overall traffic demand is indeed justified. This means that expensive and rarely comprehensive surveys are now only needed to capture trip purposes. Regardless of trip purpose (e.g., shopping, regular recreational activities, dropping kids at school), the LODES data is an excellent predictor of overall road segment loads.
Digital Object Identifier
Mineta Transportation Institute URL
Planning, Activities leading to information generation, Communication, Interdisciplinary studies, Methodology
Transportation | Urban Studies and Planning
Jochen Albrecht, Andreas Petutschnig, Laxmi Ramasubramanian, Bernd Resch, and Aleisha Wright. "Comparing Twitter and LODES Data for Detecting Commuter Mobility Patterns" Mineta Transportation Institute Publications (2021). https://doi.org/10.31979/mti.2021.2037