Peak and off-peak pricing strategies are an important policy tool used to spread peak demand in public transportation systems. This study uses an agent-based simulator (SimMobility Mid-term) to examine the impact of pricing (off-peak fare discounts) strategies used in Singapore. The aim of the paper is to demonstrate the capabilities of the simulator, and types of detailed performance indicators it can provide, in order to examine the effects of complex public transport pricing policies. Behavioral models within the simulator are calibrated with relevant datasets such as household travel survey, smart card, GPS probe data from taxis and traffic counts for the Singapore network. Nine (09) time-based pricing strategies are examined that consist of a combination of free pre-peak travel on Mass Rapid Transit (MRT) and an off-peak discount for integrated transit (public buses, MRT and Light Rail Transit (LRT)).

Changes in public transport ridership, mode shares, operator's revenue and denied boarding are used as indicators to examine the impacts of pricing strategies. The effects of these policies are also examined on segments of the population in terms of income level, person type and gender. Results indicate that off-peak discounts spread PM peak demand and attract individuals to public transportation. However, the availability of fare discounts in all off-peak periods results in adverse impacts during the AM peak because many commuters shift the return leg rather than the initial leg of their journey. The study concludes with suggestions on how to explore more effective pricing strategies, i.e. providing fare discounts only during off-peak periods that surround AM peak.

高峰和非高峰定价策略是用于在公共交通系统中分散高峰需求的重要政策工具。本研究使用基于代理的模拟器（SimMobility Mid-term）来检查新加坡使用的定价（非高峰票价折扣）策略的影响。本文的目的是演示模拟器的功能以及它可以提供的详细性能指标的类型，以检查复杂的公共交通定价政策的影响。模拟器中的行为模型已通过相关数据集进行了校准，例如家庭旅行调查，智能卡，出租车的GPS探测数据以及新加坡网络的交通流量。

公共交通出行量，方式份额，运营商的收入和拒绝登机的变化被用作检查定价策略影响的指标。还从收入水平，人员类型和性别等方面检查了这些政策对部分人群的影响。结果表明，非高峰折扣降低了PM高峰需求并吸引了人们乘坐公共交通工具。但是，在所有非高峰时段都可享受票价折扣，这会在AM高峰期产生不利影响，因为许多通勤者会改变回程而不是旅行的最初行程。该研究的结论是关于如何探索更有效的定价策略的建议，即仅在围绕AM高峰的非高峰时段提供票价折扣。