teLLMe Why (Ain't Nothing but a Jam): Exploratory Causal Analysis of Urban Driving Data
Finding cause and effect in traffic using dashcam video and AI questions
Researchers built a system called teLLMe that answers causal questions about urban traffic by analyzing dashcam video data and natural-language queries. Instead of just showing correlations, the system uses causal inference techniques to estimate real effects — for example, how much rain actually increases traffic density — and explains its reasoning and uncertainty alongside each answer.
Traffic agencies have massive video datasets but can't easily extract causal insights about what truly causes congestion or unsafe conditions. This system lets non-experts ask plain-English questions about traffic and get transparent, reasoned answers that acknowledge uncertainty — speeding up hypothesis testing and helping agencies design better interventions without running expensive real-world experiments.