Civil Engineering Departmental Seminar - Modeling and optimization of a collective incentive strategy for ridership rebound with peak congestion management in mass transit systems

Modeling and optimization of a collective incentive strategy for ridership rebound with peak congestion management in mass transit systems

Peak and valleys in demand is one of the most significant challenges for transit authorities to manage demand and improve system efficiency. The post pandemic further induced the imbalanced peak and off-peak hour ridership. This study models and analyzes a collective incentive strategy with passenger departure time equilibrium aiming to maximize the total passenger surplus. In the proposed collective incentive strategy, passengers have dynamic off-peak period discounts where the discount value depends on the total number of passengers enrolled in the incentive program. The proposed model considers passengers’ demand elasticity and responses to the strategy and determines the starting time and duration of the collective incentive strategy during the time of the day. We 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 peak and off-peak commuters 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 Toronto metro system where results indicated that the proposed collective incentive strategy is capable of improving total passenger surplus, increasing off-peak ridership, reducing crowding and congestion and improving transit revenues simultaneously.