MULTIMODAL PUBLIC TRANSPORT DEMAND: A COINTEGRATION TIME-SERIES APPROACH

We investigate demand in a multimodal public transportation context for the city of Athens. Demand is expressed as a function of operational and macroeconomic factors and is analyzed using a time-series cointegration and error correction approach. This allows for treating non-stationary data, for determining short and long term elasticities and at the same time estimating the speed of convergence from the short to the long run. As expected, short run elasticities appear lower than the long run ones, possibly because in the short run changes in explanatory factors are smaller and because behavior is governed by resistance to change. Fare and GDP appear to have the greatest impact on public transport demand and also the greatest difference between short and long run elasticities.