Demand for public transportation is growing faster than transit capacity in metropolises. Mass transit operators are thus facing significant challenges in finding feasible solutions to peak queuing and overcrowding. Most strategies for managing transit queuing and crowding have focused on transit service supply and fare pricing. Strategies for expanding capacity or adjusting scheduling are limited because most transit lines already operate at maximum capacity or minimum headway during peak hours. Furthermore, fare differential strategies are often regarded as publicly and politically unacceptable. Any peak fare surcharge imposed must be significant enough to be effective, but this may drive peak transit users not to use off-peak services but to shift to other modes such as private cars,...[ Read more ]

Demand for public transportation is growing faster than transit capacity in metropolises. Mass transit operators are thus facing significant challenges in finding feasible solutions to peak queuing and overcrowding. Most strategies for managing transit queuing and crowding have focused on transit service supply and fare pricing. Strategies for expanding capacity or adjusting scheduling are limited because most transit lines already operate at maximum capacity or minimum headway during peak hours. Furthermore, fare differential strategies are often regarded as publicly and politically unacceptable. Any peak fare surcharge imposed must be significant enough to be effective, but this may drive peak transit users not to use off-peak services but to shift to other modes such as private cars, which is counterproductive in terms of broader sustainability objectives. Meanwhile, off-peak fare discounts or rewards are likely to increase off-peak ridership but incur revenue loss.

To address the aforementioned complications, this research aims at proposing, modelling and optimizing Pareto-improving and revenue-neutral strategies to manage mass transit congestion, which has the objective of incentivizing a shift in scheduling decisions to the shoulder periods of the peak hours to relieve congestion at transit stations. The strategy developed in the thesis is incentive compatible and balances the competing claims of efficiency, simplicity and fairness. It is an individual-based charge and reward strategy rather than an anonymous fare-differential pricing policy.

A fare-reward scheme (FRS) is first introduced that rewards a commuter one free trip for shoulder periods after taking a certain number of paid trips in peak periods. A framework of the rail transit bottleneck is provided, and the user equilibrium is analyzed. The FRS determines the shoulder periods, new fare for revenue-neutrality and the reward ratio (the ratio of the free trips to the total number of trips). Analytical results indicate the efficiency and effectiveness of the cost reductions for the fare-reward scheme. In line with this, passengers’ heterogeneous scheduling flexibility intervals are modelled and incorporated, and a hybrid fare scheme is proposed that combines a FRS (H-FRS) and a uniform fare scheme (H-UFS). The fare differentials are selected to incentivize commuters with flexible departure times to join the H-FRS. The hybrid fare scheme is successful in achieving a win-win-win situation for the two sub-scheme passengers with reduction of trip costs and transit operator with revenue neutrality. Depending on the original fare, the hybrid fare results in an optimal reward ratio up to 50% and yields a reduction of system total time costs by at least 25%.

The analysis is further developed including effects of crowding and the existing surcharge strategy to establish a bi-level model of a surcharge-reward scheme optimization considering departure time distribution equilibrium. Equilibrium properties before and after the implementation of the surcharge-reward scheme are analyzed and proved. A solution algorithm is proposed to obtain the global optimal solution of the central period location, duration, the value of the surcharge and reward. The case study is extended to a many-to-one network which implies the potential of station-based designs of the scheme to ensure each station is no worse off. Finally, a feasibility analysis is conducted by using smartcard data and operation data with over 5 million trips recorded in Beijing Metro system. Passenger’s scheduling patterns are recognized inclusive of departure time flexibility interval and the distribution of departure times within the interval. The results indicate the practical acceptability and reflects the potential of the proposed Pareto-improving and revenue-neutral strategies to benefit commuters, transit agencies and society as a whole.