Photo: Mostphotos.

K2

Dynamic causal modeling for policy interventions in public transport: A use case on fare policy change in Stockholm

When fares or other policy measures change, public transport authorities need to understand both how travel demand develops over time and why different groups respond in different ways. Discrete choice models can explain the factors influencing behaviour, while time-series analysis shows how quickly and how much demand changes. Establishing causal relationships, however, remains difficult. The project will therefore combine the methods in a dynamic causal model. Working with SL, the researchers will apply the model to a fare policy change in Stockholm using smartcard, census and land-use data. The aim is to explain how quickly different groups adapt and what drives their behavioural changes.