Revenue Maximizing Tariff Zone Planning for Public Transport Companies

Abstract

This paper presents the tariff zone planning problem, which aims to maximize ridership and revenue for public service providers. We propose an optimization approach basis on partitioning the service area into zones and finding a price per zone such that the total expected revenue is maximized. It is assumed that the price per zone takes a discrete set of values. Public transport trips depend on the price system; public transport passengers always choose the time-shortest path. We propose new mixed-integer programming (MIP) that can optimally solve instances of sizes of more than 120 stops and enforce tariff zones to specific spatial patterns (rings and stripes, for example). The results demonstrate that expected revenue can be maximized without decreasing transit ridership. However, results show enforcing tariff zones to a specif spatial pattern reduces R and CPU time. This paper sheds light on urban public service providers enforcing contiguous tariff zones to maximize expected revenue.