Tomography of the London Underground : a Scalable Model for Origin-Destination Data. / Colombo, Nicolò; Silva, Ricardo; Kang, Soong.

In: ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS, Vol. 2017-December, 12.2017, p. 3063-3074.

Research output: Contribution to journalConference articlepeer-review

Published

Standard

Tomography of the London Underground : a Scalable Model for Origin-Destination Data. / Colombo, Nicolò; Silva, Ricardo; Kang, Soong.

In: ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS, Vol. 2017-December, 12.2017, p. 3063-3074.

Research output: Contribution to journalConference articlepeer-review

Harvard

APA

Vancouver

Colombo N, Silva R, Kang S. Tomography of the London Underground: a Scalable Model for Origin-Destination Data. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS. 2017 Dec;2017-December:3063-3074.

Author

Colombo, Nicolò ; Silva, Ricardo ; Kang, Soong. / Tomography of the London Underground : a Scalable Model for Origin-Destination Data. In: ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS. 2017 ; Vol. 2017-December. pp. 3063-3074.

BibTeX

@article{3cb35c8ba7dc495995834dce8da080d8,
title = "Tomography of the London Underground: a Scalable Model for Origin-Destination Data",
abstract = "The paper addresses the classical network tomography problem of inferring local traffic given origin-destination observations. Focusing on large complex public transportation systems, we build a scalable model that exploits input-output information to estimate the unobserved link/station loads and the users' path preferences. Based on the reconstruction of the users' travel time distribution, the model is flexible enough to capture possible different path-choice strategies and correlations between users travelling on similar paths at similar times. The corresponding likelihood function is intractable for medium or large-scale networks and we propose two distinct strategies, namely the exact maximum-likelihood inference of an approximate but tractable model and the variational inference of the original intractable model. As an application of our approach, we consider the emblematic case of the London underground network, where a tap-in/tap-out system tracks the starting/exit time and location of all journeys in a day. A set of synthetic simulations and real data provided by Transport For London are used to validate and test the model on the predictions of observable and unobservable quantities.",
author = "Nicol{\`o} Colombo and Ricardo Silva and Soong Kang",
year = "2017",
month = dec,
language = "English",
volume = "2017-December",
pages = "3063--3074",
journal = "ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS",
issn = "1049-5258",
note = "31st Annual Conference on Neural Information Processing Systems, NIPS 2017 ; Conference date: 04-12-2017 Through 09-12-2017",

}

RIS

TY - JOUR

T1 - Tomography of the London Underground

T2 - 31st Annual Conference on Neural Information Processing Systems, NIPS 2017

AU - Colombo, Nicolò

AU - Silva, Ricardo

AU - Kang, Soong

PY - 2017/12

Y1 - 2017/12

N2 - The paper addresses the classical network tomography problem of inferring local traffic given origin-destination observations. Focusing on large complex public transportation systems, we build a scalable model that exploits input-output information to estimate the unobserved link/station loads and the users' path preferences. Based on the reconstruction of the users' travel time distribution, the model is flexible enough to capture possible different path-choice strategies and correlations between users travelling on similar paths at similar times. The corresponding likelihood function is intractable for medium or large-scale networks and we propose two distinct strategies, namely the exact maximum-likelihood inference of an approximate but tractable model and the variational inference of the original intractable model. As an application of our approach, we consider the emblematic case of the London underground network, where a tap-in/tap-out system tracks the starting/exit time and location of all journeys in a day. A set of synthetic simulations and real data provided by Transport For London are used to validate and test the model on the predictions of observable and unobservable quantities.

AB - The paper addresses the classical network tomography problem of inferring local traffic given origin-destination observations. Focusing on large complex public transportation systems, we build a scalable model that exploits input-output information to estimate the unobserved link/station loads and the users' path preferences. Based on the reconstruction of the users' travel time distribution, the model is flexible enough to capture possible different path-choice strategies and correlations between users travelling on similar paths at similar times. The corresponding likelihood function is intractable for medium or large-scale networks and we propose two distinct strategies, namely the exact maximum-likelihood inference of an approximate but tractable model and the variational inference of the original intractable model. As an application of our approach, we consider the emblematic case of the London underground network, where a tap-in/tap-out system tracks the starting/exit time and location of all journeys in a day. A set of synthetic simulations and real data provided by Transport For London are used to validate and test the model on the predictions of observable and unobservable quantities.

UR - http://www.scopus.com/inward/record.url?scp=85046996607&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85046996607

VL - 2017-December

SP - 3063

EP - 3074

JO - ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS

JF - ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS

SN - 1049-5258

Y2 - 4 December 2017 through 9 December 2017

ER -