Assessing public transport passenger attitudes towards a dynamic fare model based on in-vehicle crowdedness levels and additional waiting time

Abstract

Public Transport (PT) provides passenger mobility and contributes to sustainable transportation. To achieve this a PT system must provide continuous accessible service and connections for passengers. PT reliability is considered a major obstacle to growing its market share. Current solutions primarily address travel time reliability through methods like priority lanes and traffic signal priority. Dwell time reliability improvement, in turn, can be achieved by the use of smart cards which reduce the variability in boarding and alighting times. Another factor affecting reliability is in-vehicle crowdedness which causes delays and increases dwell time variability. To mitigate crowdedness, we propose a monetary approach that dynamically changes the fare based on the in-vehicle crowdedness level in a manner similar to congestion pricing. This approach would shift some passengers from boarding the over-crowded vehicle to waiting for the next, less crowded vehicle, while compensating them for the additional waiting. Passengers unwilling to wait might pay a penalty if the additional waiting time is reasonable. To assess the attitude of passengers towards a dynamic fare model, a stated preference questionnaire was developed to assess the factors that affect the choice of whether or not to board an over-crowded vehicle. Based on panel data and the fixed effect logit model it was revealed that the higher the waiting time, the lower the willingness to board the next vehicle. However, monetary schemes (penalties or discounts) increased the willingness to wait and board the next vehicle. Moreover, the willingness to wait was higher when a penalty was introduced compared to a discount, which is in line with the prospect theory. The results suggest that it is possible to construct a dynamic fare model that using data on vehicle crowdedness levels and waiting times obtained from advanced data collection systems, which is integrated within a mobile payment application. This approach could reduce crowdedness and increase reliability.

Keywords

• Public transport
• Fare policy
• Discrete choice model
• Panel data
• Binary logistic regression