{"type":"ScholarlyArticle","title":"Estimating Latent People Flow without Tracking Individuals","authors":[{"name":"Yusuke Tanaka","slug":"yusuke-tanaka","given":"Yusuke","family":"Tanaka","affiliation":"NTT Service Evolution Laboratories , Kanagawa 239-0847 , Japan"},{"name":"Tomoharu Iwata","slug":"tomoharu-iwata","given":"Tomoharu","family":"Iwata","affiliation":"NTT Communication Science Laboratories , Kyoto 619-0237 , Japan"},{"name":"Takeshi Kurashima","slug":"takeshi-kurashima","given":"Takeshi","family":"Kurashima","affiliation":"NTT Service Evolution Laboratories , Kanagawa 239-0847 , Japan"},{"name":"Hiroyuki Toda","slug":"hiroyuki-toda","given":"Hiroyuki","family":"Toda","affiliation":"NTT Service Evolution Laboratories , Kanagawa 239-0847 , Japan"},{"name":"Naonori Ueda","slug":"naonori-ueda","given":"Naonori","family":"Ueda","affiliation":"NTT Communication Science Laboratories , Kyoto 619-0237 , Japan; RIKEN Center for AIP , Tokyo 103-0027 , Japan"}],"abstract":"Analyzing people flows is important for better navigation and location-based advertising. Since the location information of people is often aggregated for protecting privacy, it is not straightforward to estimate transition populations between locations from aggregated data. Here, aggregated data are incoming and outgoing people counts at each location; they do not contain tracking information of individuals. This paper proposes a probabilistic model for estimating unobserved transition populations between locations from only aggregated data. With the proposed model, temporal dynamics of people flows are assumed to be probabilistic diffusion processes over a network, where nodes are locations and edges are paths between locations. By maximizing the likelihood with flow conservation constraints that incorporate travel duration distributions between locations, our model can robustly estimate transition populations between locations. The statistically significant improvement of our model is demonstrated using real-world datasets of pedestrian data in exhibition halls, bike trip data and taxi trip data in New York City.","doi":"10.24963/ijcai.2018/494","url":"https://ijcai.org/proceedings/2018/494","pdf":"https://ijcai.org/proceedings/2018/0494.pdf","markdown":"https://ijcai.org/proceedings/2018/494.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/494","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning Applications","track":"Main track","number":494,"pages":{"first":3556,"last":3563},"keywords":["Machine Learning: Probabilistic Machine Learning","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Byrd et al., 1995] Richard H. 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