A collective incentive strategy to manage ridership rebound and consumer surplus in mass transit systems

This paper models and analyzes a collective incentive strategy with passenger departure time equilibrium to maximize the total passenger surplus, which could further enhance the sustainability of transit systems in the post-pandemic era. In the proposed collective incentive strategy, passengers with transit passes have dynamic discounts where the discount value depends on the total number of passengers enrolled in the incentive program. A bi-level optimization model is established by considering passengers' demand elasticity and responses to the strategy, which eventually determines the starting time and duration of the collective incentive strategy during the time of the day. We further analyze the properties of the proposed collective incentive model to identify the analytical solutions and performance. Analytical results indicate that there exists a threshold demand elasticity below which the total surplus of commuters with and without transit passes can be improved simultaneously. Properties also show that there could exist multiple local optimal solutions of incentive strategy duration for a given starting time. A case study is conducted with the metro system in Toronto: the results show that the proposed collective incentive strategy could improve both the fare revenues and ridership by 33.31 % and 83.56 %, respectively.