Modelling Occupancy-Queue Relation Using Gaussian Process

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F15%3A00242923" target="_blank" >RIV/68407700:21260/15:00242923 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.14311/NNW.2015.25.002" target="_blank" >http://dx.doi.org/10.14311/NNW.2015.25.002</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.14311/NNW.2015.25.002" target="_blank" >10.14311/NNW.2015.25.002</a>

Result language

angličtina

Original language name

Modelling Occupancy-Queue Relation Using Gaussian Process

Original language description

One of the key indicators of the quality of service for urban transportation control systems is the queue length. Even in unsaturated conditions, longer queues indicate longer travel delays and higher fuel consumption. With the exception of some expensive surveillance equipment, the queue length itself cannot be measured automatically, and manual measurement is both impractical and costly in a long term scenario. Hence, many mathematical models that express the queue length as a function of detector measurements are used in engineering practice, ranging from simple to elaborate ones. The method proposed in this paper makes use of detector time-occupancy, a complementary quantity to vehicle count, provided by most of the traffic detectors at no cost and disregarded by majority of existing approaches for various reasons. Our model is designed as a complement to existing methods. It is based on Gaussian-process model of the occupancy-queue relationship, it can handle data uncertainties, and it provides more information about the quality of the queue length prediction.

Czech name

—

Czech description

—

Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

BB - Applied statistics, operational research

OECD FORD branch

—

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2015

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Neural Network World

ISSN

1210-0552

e-ISSN

—

Volume of the periodical

25

Issue of the periodical within the volume

1

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

18

Pages from-to

35-52

UT code for WoS article

000351252000003

EID of the result in the Scopus database

—