On March 24 2026, Angie Torres will present her Innolab results on ” People’s Perception of Cable Car Transportation Using Twitter Data” at 12:00 in seminar room 3, John-Skilton-Str. 4a.
From the abstract: Urban mobility is commonly analysed through geospatial methods that make it possible to assess transport infrastructure, travel times, and accessibility. Yet spatial data alone cannot reveal how these systems are perceived and experienced in everyday life, leaving an important gap in understanding their broader impacts and effectiveness. This Innolab addressed this gap by exploring how social media data can complement geospatial analysis through insights into users’ lived experiences and perceptions of public transportation. Using tweets about cable car systems in Mexico City and Medellín posted between 2019 and 2022, the study applied Natural Language Processing (NLP), including topic modelling and sentiment analysis, to identify dominant themes and public attitudes. The results show clear differences in perception between the two cities and highlight how service, infrastructure, politics, and everyday experience shape public opinion. The study demonstrates the potential of social media data to enrich the evaluation of urban mobility projects and support more socially informed transport planning.
Supervisor: Dr. Richard Lemoine-Rodríguez
From the abstract: Urban mobility is commonly analysed through geospatial methods that make it possible to assess transport infrastructure, travel times, and accessibility. Yet spatial data alone cannot reveal how these systems are perceived and experienced in everyday life, leaving an important gap in understanding their broader impacts and effectiveness. This Innolab addressed this gap by exploring how social media data can complement geospatial analysis through insights into users’ lived experiences and perceptions of public transportation. Using tweets about cable car systems in Mexico City and Medellín posted between 2019 and 2022, the study applied Natural Language Processing (NLP), including topic modelling and sentiment analysis, to identify dominant themes and public attitudes. The results show clear differences in perception between the two cities and highlight how service, infrastructure, politics, and everyday experience shape public opinion. The study demonstrates the potential of social media data to enrich the evaluation of urban mobility projects and support more socially informed transport planning.
Supervisor: Dr. Richard Lemoine-Rodríguez
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