Missed connections

Abstract

We present a methodology to measure multi-modal interconnectivity between different transportation modes that operate in a city. The interconnectivity of an urban network represents how well different services integrate to offer seamless transportation options to users. On the supply side, we leverage open data sources that cities provide to accurately model the services that they offer. On the demand side, we account for myopic user behavior through the use of journey planners and travel demand estimates. The reciprocal interaction between supply and demand is then used to characterize interconnectivity. In a multi-modal setting, we present different measures that can be employed to understand shortcomings in connectivity across the network. Using these metrics, improvements in service schedules are proposed using an optimization model that seeks to perturb existing schedules to improve transit connectivity. Real-world data from Washington, D.C. is used to demonstrate presented measures and optimal schedule perturbation for one route are presented which result in a 19% reduction in delays.

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