Optimising public transport investment from a cost-neutral carpooling policy: A case study in Wellington, New Zealand

Seamless first- and last-mile connectivity is critical for encouraging public transport usage, yet such solutions often require substantial government investments. However, there is limited research on addressing the connectivity challenge through a cost-neutral approach. We propose free carpooling and paid single-occupancy parking at train stations on working days, in the context of daily commuting. Additional revenue could be anticipated from two sources: paid parking spaces and increased train ridership, as new commuters may be attracted to the network by the free carpooling incentive. This study formulates an integer programming model to reinvest this revenue towards increasing rail service frequency during peak commuting hours. The model is constrained by operating costs, rail capacity, and timetable symmetry. Using rail-world data from the Wellington Metro Railway Network, we compute line-specific capacity limits and identify optimal revenue allocations. The results show that peak-hour service intervals can be significantly reduced within the available reinvestment budget. This model demonstrates a scalable, data-driven strategy for improving public transport operational efficiency through behavioural incentives and revenue reinvestment. • A cost-neutral carpooling policy is proposed to support public transport reinvestment. • An integer programming model optimises rail service frequency within revenue constraints. • Line-specific capacity is derived from real-world timetables and operational structures. • Results show significant peak-hour service improvements can be achieved without subsidies. • It supports scalable investment planning aligned with behavioural incentives.