Crowding in public transport can be of major influence on passengers’ travel experience and therefore affect route and mode choice. The impact of crowding on passenger choices has been estimated in many studies by means of stated-preferences choice experiments. Respondents are then asked to make hypothetical choices under a range of scenarios based on which choice models can be estimated, including quantifying the impact of on-board crowding on route choice. This results in in-vehicle multiplier values ranging between 1-2.7 (!). Results from meta-analysis of these studies have been for example reported and used in evaluating capacity increase investments (see the case of our study of a metro line in Stockholm).
These estimates seem strangely high. They imply that passengers will rather travel twice as long if they can have a seat instead of to travelling in a densely crowded vehicle. These has severe ramifications for project appraisal – do you invest in increasing vehicle size, higher frequency or higher speed? In a choice experiment it is easy to indicate that you rather wait for the next vehicle or travel longer than to ride a busy vehicle. However, there was very scarce evidence that people actually do these trade-offs in reality. We therefore wanted to find out to what extent crowding impacts passenger route choices based on observed behavior. This is now possible thanks to large-scale smart card deployment.
See full paper here: “Crowding valuation in urban tram and bus transportation based on smart card data”
In this study, crowding valuation for urban tram and bus travelling is determined fully based on revealed preference data. Urban tram and bus crowding valuation is estimated in a European context based on a Dutch case study network. Based on the estimated discrete choice model, we conclude that crowding plays a significant role in passengers’ route choice in public transport. The average crowding multiplier of in-vehicle time equals 1.16 when all seats are occupied. For frequent travellers, this value is equal to 1.31. Our study results suggest that infrequent travellers do not incorporate expected crowding in their route choice. These values are significantly lower than those reported in past studies based on choice experiments.
The insights gained from our study can support the decision-making process of policy-makers, by quantifying the benefits of measures aiming to reduce crowding levels for example in a cost–benefit analysis framework.