Combining a gauss-markov model and gaussian process for traffic prediction in dublin city center

François Schnitzler; Thomas Liebig; Shie Mannor; Katharina Morik

Combining a gauss-markov model and gaussian process for traffic prediction in dublin city center

François Schnitzler, Thomas Liebig, Shie Mannor, Katharina Morik

Technion - Israel Institute of Technology

Research output: Contribution to journal › Conference article › peer-review

Abstract

We consider a city where induction-based vehicle count sensors are installed at some, but not all street junctions. Each sensor regularly outputs a count and a saturation value. We first use a discrete time Gauss-Markov model based on historical data to predict the evolution of these saturation values, and then a Gaussian Process derived from the street graph to extend these predictions to all junctions. We construct this model based on real data collected in Dublin city.

Original language	English
Pages (from-to)	373-374
Number of pages	2
Journal	CEUR Workshop Proceedings
Volume	1133
State	Published - 2014
Event	2014 Joint Workshops on International Conference on Extending Database Technology, EDBT 2014 and International Conference on Database Theory, ICDT 2014 - Athens, Greece Duration: 28 Mar 2014 → …

Keywords

Autoregressive
Gauss-Markov
Gaussian Process
Smart cities
Spatio-temporal
Time series
Traffic prediction

All Science Journal Classification (ASJC) codes

General Computer Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

https://ceur-ws.org/Vol-1133/paper-62.pdf

Cite this

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title = "Combining a gauss-markov model and gaussian process for traffic prediction in dublin city center",

abstract = "We consider a city where induction-based vehicle count sensors are installed at some, but not all street junctions. Each sensor regularly outputs a count and a saturation value. We first use a discrete time Gauss-Markov model based on historical data to predict the evolution of these saturation values, and then a Gaussian Process derived from the street graph to extend these predictions to all junctions. We construct this model based on real data collected in Dublin city.",

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language = "الإنجليزيّة",

volume = "1133",

pages = "373--374",

journal = "CEUR Workshop Proceedings",

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AU - Schnitzler, François

AU - Liebig, Thomas

AU - Mannor, Shie

AU - Morik, Katharina

PY - 2014

Y1 - 2014

N2 - We consider a city where induction-based vehicle count sensors are installed at some, but not all street junctions. Each sensor regularly outputs a count and a saturation value. We first use a discrete time Gauss-Markov model based on historical data to predict the evolution of these saturation values, and then a Gaussian Process derived from the street graph to extend these predictions to all junctions. We construct this model based on real data collected in Dublin city.

AB - We consider a city where induction-based vehicle count sensors are installed at some, but not all street junctions. Each sensor regularly outputs a count and a saturation value. We first use a discrete time Gauss-Markov model based on historical data to predict the evolution of these saturation values, and then a Gaussian Process derived from the street graph to extend these predictions to all junctions. We construct this model based on real data collected in Dublin city.

KW - Autoregressive

KW - Gauss-Markov

KW - Gaussian Process

KW - Smart cities

KW - Spatio-temporal

KW - Time series

KW - Traffic prediction

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M3 - مقالة من مؤنمر

SN - 1613-0073

VL - 1133

SP - 373

EP - 374

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

T2 - 2014 Joint Workshops on International Conference on Extending Database Technology, EDBT 2014 and International Conference on Database Theory, ICDT 2014

Y2 - 28 March 2014

ER -

Combining a gauss-markov model and gaussian process for traffic prediction in dublin city center

Abstract

Keywords

All Science Journal Classification (ASJC) codes

UN SDGs

Access to Document

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