TY - GEN

T1 - A novel data-driven algorithm for the automated detection of unexpectedly high traffic flow in uncongested traffic states

AU - Klaasse, Bo

AU - Timmerman, Rik

AU - van Ballegooijen, Tessel

AU - Boon, Marko

AU - Eijkelenboom, Gerard

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - physics.soc-ph

KW - Congestion

KW - Data-driven algorithm

KW - Detector data

KW - Fundamental diagram

KW - High-performance days

KW - Traffic breakdown

UR - http://www.scopus.com/inward/record.url?scp=85085057441&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-44411-2_5

DO - 10.1007/978-3-030-44411-2_5

M3 - Conference contribution

SN - 978-3-030-44410-5

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 65

EP - 83

BT - Computer performance engineering

A2 - Gribaudo, Marco

A2 - Iacono, Mauro

A2 - Phung-Duc, Tuan

A2 - Razumchik, Rostislav

PB - Springer

CY - Berlin

ER -