STOchastic Traffic NEtworks (STONE)
Forskningsprojekt , 2020 – 2022

Understanding efficiency and behavior aspects in partially automated (vehicular) technology in large-scale (traffic) context is an unsolved problem nowadays. Our main goal is to develop learning methods for uncertain traffic networks. We will rely on interdisciplinary approaches between i) mathematical sciences (stochastic Partial Differential Equations sPDE) ii) traffic flow theory (hyperbolic conservation vehicular laws, network efficiency) and iii) traffic safety analysis (driver behavior, collision probability) glued by probabilistic machine leaning concept. We propose to to learn the 1) parameter variation and 2) solution of sPDE network models. Whilst learning parameters will deliver a probabilistic picture of partially automated traffic networks in terms of congestions, driver behavior, network capacity; learning the solutions of such models enables us to short term predict traffic network behavior.

Deltagare

Annika Lang (kontakt)

Docent vid Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Pinar Boyraz Baykas

Docent vid Chalmers, Mekanik och maritima vetenskaper, Fordonssäkerhet, Olycksanalys och prevention

Balázs Adam Kulcsár

Biträdande professor vid Chalmers, Elektroteknik, System- och reglerteknik, Reglerteknik

Finansiering

Chalmers AI-forskningscentrum (CHAIR)

(Finansieringsperiod saknas)

Chalmers

Finansierar Chalmers deltagande under 2020–2022

Relaterade styrkeområden och infrastruktur

Transport

Styrkeområden

Mer information

Senast uppdaterat

2020-01-17