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A Bayesian Additive Model for Understanding Public Transport Usage in Special Events
KTH, Centra, Nordic Institute for Theoretical Physics NORDITA. Singapore-MIT Alliance for Research and Technology, Singapore; Stockholm Univ, Roslagstullsbacken 23, SE-10691 Stockholm, Sweden.
2017 (engelsk)Inngår i: IEEE Transaction on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, E-ISSN 1939-3539, Vol. 39, nr 11, s. 2113-2126Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Public special events, like sports games, concerts and festivals are well known to create disruptions in transportation systems, often catching the operators by surprise. Although these are usually planned well in advance, their impact is difficult to predict, even when organisers and transportation operators coordinate. The problem highly increases when several events happen concurrently. To solve these problems, costly processes, heavily reliant on manual search and personal experience, are usual practice in large cities like Singapore, London or Tokyo. This paper presents a Bayesian additive model with Gaussian process components that combines smart card records from public transport with context information about events that is continuously mined from the Web. We develop an efficient approximate inference algorithm using expectation propagation, which allows us to predict the total number of public transportation trips to the special event areas, thereby contributing to a more adaptive transportation system. Furthermore, for multiple concurrent event scenarios, the proposed algorithm is able to disaggregate gross trip counts into their most likely components related to specific events and routine behavior. Using real data from Singapore, we show that the presented model outperforms the best baseline model by up to 26 percent in R-2 and also has explanatory power for its individual components.

sted, utgiver, år, opplag, sider
IEEE COMPUTER SOC , 2017. Vol. 39, nr 11, s. 2113-2126
Emneord [en]
Additive models, transportation demand, Gaussian processes, expectation propagation
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-215777DOI: 10.1109/TPAMI.2016.2635136ISI: 000412028600001PubMedID: 28113999Scopus ID: 2-s2.0-85032272313OAI: oai:DiVA.org:kth-215777DiVA, id: diva2:1151176
Merknad

QC 20171023

Tilgjengelig fra: 2017-10-23 Laget: 2017-10-23 Sist oppdatert: 2020-03-09bibliografisk kontrollert

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