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During recent years there have been several efforts from city and transportation planners, as well as, port authorities, to design multimodal transport systems, covering the needs of the population to be served. However, before designing such a system, the first step is to understand the current gaps. Does the current system meet the transit demand of the geographic area covered? If not, where are the gaps between supply and demand? To answer this question, the notion of transit desert has been introduced. A transit desert is an area where the supply of transit service does not meet the demand for it. While there is little ambiguity on what constitutes transit demand, things are more vague when it comes to transit supply. Existing efforts often define transit supply using volume metrics (e.g., number of bus stops within a pre-defined distance). However, this does not necessarily capture the quality of the transit service. In this study, we introduce a network-based transit desert index (which we call TDI) that captures not only the quantity of transit supply in an area, but also the connectivity that the transit system provides for an area within the region of interest. In particular, we define a network between areas based on the transit travel time, distance, and overall quantity of connections. We use these measures to examine two notions of transit quality: connectivity and availability. To quantify the connectivity of an area i we utilize the change observed in the second smallest eigenvalue of the Laplacian when we remove node i from the network. To quantify availability of an area i, we examine the number of routes which pass through this area as given by an underlying transit network. We further apply and showcase our approach with data from Allegheny County, Pennsylvania, USA. Finally, we discuss current limitations of TDI and how we can tackle them as part of our future research. 

The amount of food waste generated by the U.S. is staggering, both expensive in economic cost and environmental side effects. Surplus food, which could be used to feed people facing food insecurity, is instead discarded and placed in landfills. Institutions, universities, and non-profits have noticed this issue and are beginning to take action to reduce surplus food waste, typically by redirecting it to food banks and other organizations or having students transport or eat the food. These approaches present challenges such as transportation, volunteer availability, and lack of prioritization of those in need. In this paper, we introduce PittGrub, a notification system to intelligently select users to invite to events that have leftover food. PittGrub was invented to help reduce food waste at the University of Pittsburgh. We use reinforcement learning to determine how many notifications to send out and a valuation model to determine whom to prioritize in the notifications. Our goal is to produce a system that prioritizes feeding students in need while simultaneously eliminating food waste and maintaining a fair distribution of notifications. As far as we are aware, PittGrub is unique in its approach to eliminating surplus food waste while striving for social good. We compare our proposed techniques to multiple baselines on simulated datasets to demonstrate effectiveness. Experimental results among various algorithms show promise in eliminating food waste while helping those facing food insecurity and treating users fairly. Our prototype is currently in beta and coming soon to the Apple App Store.