Markov chain model approach for traffic incident length prediction

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F17%3A10238089" target="_blank" >RIV/61989100:27740/17:10238089 - isvavai.cz</a>

Result on the web

<a href="https://dl.acm.org/citation.cfm?doid=3157737.3157750" target="_blank" >https://dl.acm.org/citation.cfm?doid=3157737.3157750</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3157737.3157750" target="_blank" >10.1145/3157737.3157750</a>

Result language

angličtina

Original language name

Markov chain model approach for traffic incident length prediction

Original language description

One of the most challenging parts of traffic modeling is how to model traffic behavior during traffic incidents. One of the possible approaches to this problem is to use historical data to identify typical incidents and use this knowledge to classify future time series. This classification can be utilized for example in prediction of traffic incident duration. This procedure requires solutions to several problems. The first problem is how can historical time series of traffic incidents be clustered, and these clusters can be parametrized. The second problem is how can time series of new ongoing incidents be classified to these existing clusters and how this classification can be utilized in the prediction of their length. The main aim of this article is to propose a solution to these problems. Methods utilized in addressing these problems are called Markov chains, Dynamic Time Warping, Bayesian classification and Monte Carlo simulation. © 2017 Association for Computing Machinery.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

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

Continuities

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

Publication year

2017

Confidentiality

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

Article name in the collection

ACM International Conference Proceeding Series 2017

ISBN

978-1-4503-5376-2

ISSN

—

e-ISSN

neuvedeno

Number of pages

5

Pages from-to

63-67

Publisher name

Association for Computing Machinery

Place of publication

New York

Event location

Čcheng-tu

Event date

Oct 12, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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