ToiletBuilder: A PU-Learning-Based Model for Selecting New Public Toilet Locations

Abstract

With increasing expansion and urbanization of cities, the gap is constantly widening between the current provision of urban public toilets and the fast-growing toileting demand. Building new ones becomes a promising way to alleviate such issue. Nevertheless, where to build them in a city is challenging. Different from other location selection (e.g., commercial sites) problems, the selection of public toilet locations is hard to quantify and evaluate. On one hand, the toileting demand that determines whether the new public toilet is needed cannot be measured accurately. On the other hand, the modeling of the toileting demand is also complicated, being influenced by multiple factors, e.g., human mobility, human activity, and geographical characteristics. In this article, we propose a novel data-driven framework named ToiletBuilder to address it, which consists of three components, i.e., region identification, region representation, and region classification. Specifically, region identification obtains many reachable regions with the reasonable size. Region representation extracts city-specific features from multiple urban data to characterize location selection influencing factors for each region. A deep embedding model is further applied to learn a high-order and concise semantic representation. By labeling some regions with the true positive label (i.e., having public toilets served in these regions) in advance, region classification trains a positive-unlabeled (PU) learning model from these samples to identify unlabeled positive ones. Finally, we conduct extensive experiments based on four real-world data sets including road network, river network, taxi trajectory, and POI data, in the city of Chongqing, China. Results demonstrate the effectiveness of our proposed approach.