The objective of this study is to mine and analyze large-scale social media data (rich spatio-temporal data unlike traditional surveys) and develop comparative infographics of emerging transportation trends and mobility indicators by adopting natural language processing and data-driven techniques. As such, first, around 13 million tweets for about 20 days (16 December 2019–4 January 2020) from North America were collected, and tweets closely aligned with emerging transportation and mobility trends (such as shared mobility, vehicle technology, built environment, user fees, telecommuting, and e-commerce) were identified. Data analytics captured spatio-temporal differences in social media user interactions and concerns about such trends, as well as topics of discussions formed through such interactions. California, Florida, Georgia, Illinois, New York are among the highly visible cities discussing such trends. Being positive overall, people carried more positive views on shared mobility, vehicle technology, telecommuting, and e-commerce, while being more negative on user fees, and the built environment. Ride-hailing, fuel efficiency, trip navigation, daily as well as shopping and recreational activities, gas price, tax, and product delivery were among the emergent topics. The social media data-driven framework would allow real-time monitoring of transportation trends by agencies, researchers, and professionals.
