Traffic speed prediction using ensemble kalman filter and differential evolution

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F19%3A10244139" target="_blank" >RIV/61989100:27740/19:10244139 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.matec-conferences.org/articles/matecconf/abs/2019/08/matecconf_ictle2019_02001/matecconf_ictle2019_02001.html" target="_blank" >https://www.matec-conferences.org/articles/matecconf/abs/2019/08/matecconf_ictle2019_02001/matecconf_ictle2019_02001.html</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1051/matecconf/201925902001" target="_blank" >10.1051/matecconf/201925902001</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Traffic speed prediction using ensemble kalman filter and differential evolution

Popis výsledku v původním jazyce

Importance of traffic state prediction steadily increases with growing volume of traffic. Ability to predict traffic speed in short to medium horizon (i.e. up to one hour) is one of the main tasks of every newly developed Intelligent Transportation System. There are two possible approaches to this prediction. The first is to utilize physical properties of the traffic flow to construct an exact or approximate numerical model. This approach is, however, almost impossible to implement on a larger scale given the difficulty to obtain enough traffic data to describe the starting and boundary conditions of the model. The other option is to use historical traffic data and relate information and patterns they contain to the current traffic state by application of some form of statistical or machine learning approach. We propose to use combination of Ensemble Kalman filter and Cell Transmission Model for this task. These models combine properties of physical model with ability to incorporate uncertainty of the traffic data.

Název v anglickém jazyce

Traffic speed prediction using ensemble kalman filter and differential evolution

Popis výsledku anglicky

Importance of traffic state prediction steadily increases with growing volume of traffic. Ability to predict traffic speed in short to medium horizon (i.e. up to one hour) is one of the main tasks of every newly developed Intelligent Transportation System. There are two possible approaches to this prediction. The first is to utilize physical properties of the traffic flow to construct an exact or approximate numerical model. This approach is, however, almost impossible to implement on a larger scale given the difficulty to obtain enough traffic data to describe the starting and boundary conditions of the model. The other option is to use historical traffic data and relate information and patterns they contain to the current traffic state by application of some form of statistical or machine learning approach. We propose to use combination of Ensemble Kalman filter and Cell Transmission Model for this task. These models combine properties of physical model with ability to incorporate uncertainty of the traffic data.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

MATEC Web of Conferences. Volume 259

ISBN

—

ISSN

2261-236X

e-ISSN

—

Počet stran výsledku

7

Strana od-do

7

Název nakladatele

EDP Sciences

Místo vydání

Paříž

Místo konání akce

Bangkok

Datum konání akce

3. 8. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000471300400005