Abstract

Cities around the world vary in terms of their urban forms, transportation networks, and travel demand patterns; these variations affect opportunities for travelers to share trips, and the viability of shared mobility services. This study proposes metrics to quantify the maximum shareability of person-trips in a city, or region of a city, as a function of two inputs—the transportation network structure and origin-destination (OD) travel demand. The study first conceptualizes a fundamental shareability unit, ‘flow overlap’. Flow overlap denotes, for a person-trip traversing a given path, the weighted (by link distance) average number of other person-trips sharing the links along the original person-trip’s path. The study extends this concept to the network level and formulates the Maximum Network Flow Overlap Problem (MNFLOP) to assign all OD person-trips to network paths that maximize flow overlap in the whole network. The study also proposes an MNFLOP variant with a second objective function term, detour distance, to capture the trade-off between minimizing travel distance and maximizing shareability. The study utilizes the MNFLOP output to calculate metrics of shareability at various levels of aggregation: person-trip level, OD level, origin or destination level, network level, and link level. The study applies the MNFLOP and associated shareability metrics to different OD demand scenarios in the Sioux Falls network. The computational results verify that (i) MNFLOP assigns person-trips to paths such that flow overlaps significantly increase relative to shortest path assignment, (ii) MNFLOP and its associated shareability metrics can meaningfully differentiate between different OD trip matrices in terms of maximum shareability, and (iii) an MNFLOP-based metric can quantify demand dispersion—a metric of the directionality of demand—in addition to the magnitude of demand, for trips originating or terminating from a single node/location in the network. The paper also includes an extensive discussion of potential future uses of the MNFLOP and its associated shareability metrics.