A novel data-driven algorithm for the automated detection of unexpectedly high traffic flow in uncongested traffic states

Bo Klaasse, Rik Timmerman, Tessel van Ballegooijen, Marko Boon, Gerard Eijkelenboom

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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We present an algorithm to identify days that exhibit the seemingly paradoxical behaviour of high traffic flow and, simultaneously, a striking absence of traffic jams. We introduce the notion of high-performance days to refer to these days. The developed algorithm consists of three steps: step 1, based on the fundamental diagram (i.e. an empirical relation between the traffic flow and traffic density), we estimate the critical speed by using robust regression as a tool for labelling congested and uncongested data points; step 2, based on this labelling of the data, the breakdown probability can be estimated (i.e. the probability that the average speed drops below the critical speed); step 3, we identify unperturbed moments (i.e. moments when a breakdown is expected, but does not occur) and subsequently identify the high-performance days based on the number of unperturbed moments. Identifying high-performance days could be a building block in the quest for traffic jam reduction; using more detailed data one might be able to identify specific characteristics of high-performance days. The algorithm is applied to a case study featuring the highly congested A15 motorway in the Netherlands.
Originele taal-2Engels
TitelComputer performance engineering
Subtitel16th European Workshop, EPEW 2019, Milan, Italy, November 28–29, 2019, Revised Selected Papers
RedacteurenMarco Gribaudo, Mauro Iacono, Tuan Phung-Duc, Rostislav Razumchik
Plaats van productieBerlin
Aantal pagina's19
ISBN van elektronische versie978-3-030-44411-2
ISBN van geprinte versie978-3-030-44410-5
StatusGepubliceerd - 2020

Publicatie series

NaamLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12039 LNCS
ISSN van geprinte versie0302-9743
ISSN van elektronische versie1611-3349


  • physics.soc-ph

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