Getting Charlie off the MTA: a multiobjective optimization method to account for cost constraints in public transit accessibility metrics

Most analyses of accessibility by public transit have focused on travel time and not considered the cost of transit fares. It is difficult to include fares in shortest-path algorithms because fares are often path-dependent. When fare policies allow discounted transfers, for example, the fare for a given journey segment depends on characteristics of previous journey segments. Existing methods to characterize tradeoffs between travel time and monetary cost objectives do not scale well to complex networks, or they rely on approximations. Additionally, they often require assumed values of time, which may be problematic for evaluating the equity of service provision. We propose a new method that allows us to find Pareto sets of paths, jointly minimizing fare and travel time. Using a case study in greater Boston, Massachusetts, USA, we test the algorithm’s performance as part of an interactive web application for computing accessibility metrics. Potential extensions for journey planning and route choice models are also discussed.