### Abstract

Original language | English |
---|---|

Article number | 1909.12782v1 |

Number of pages | 15 |

Journal | arXiv |

Publication status | Published - 26 Sep 2019 |

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### Cite this

*arXiv*, [1909.12782v1].

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*arXiv*.

**A novel data-driven algorithm for the automated detection of unexpectedly high traffic flow in uncongested traffic states.** / Klaasse, Bo; Timmerman, Rik (Corresponding author); van Ballegooijen, Tessel ; Boon, Marko; Eijkelenboom, Gerard.

Research output: Contribution to journal › Article › Academic

TY - JOUR

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 - 2019/9/26

Y1 - 2019/9/26

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, such days we name high-performance 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; step 2, based on a 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. The algorithm relies on a novel approach to estimate the critical speed; we exploit the roughly linear relation between traffic flow and traffic density in case of no congestion using robust regression as a tool for labelling. In addition, we introduce the notion of high-performance days. 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, such days we name high-performance 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; step 2, based on a 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. The algorithm relies on a novel approach to estimate the critical speed; we exploit the roughly linear relation between traffic flow and traffic density in case of no congestion using robust regression as a tool for labelling. In addition, we introduce the notion of high-performance days. 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

UR - https://arxiv.org/abs/1909.12782

M3 - Article

JO - arXiv

JF - arXiv

M1 - 1909.12782v1

ER -