Model and Computation of traffic Resilience

Leon Rothkrantz

doi:10.1145/3606305.3606314

Model and Computation of traffic Resilience

^*Corresponding author for this work

Interactive Intelligence

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

1 Citation (Scopus)

35 Downloads (Pure)

Abstract

At many times we observed disturbances of traffic flow on highways. This may be caused by traffic accidents, bad weather conditions, road maintenance or rush hours. The Road Traffic Management takes many rules and regulation, to make road sections more robust to disturbances and improve the recovery from disturbances in traffic flow (traffic resilience). To study the effect and impact of these rules and regulations assessment models of traffic resilience was needed. In this paper, we designed and tested such an assessment model. The model is inspired by the well-known Resilience Triangle. The assessment of traffic resilience was based on measurements of the speed of traffic flow on highways. Neural Networks were used to model and smooth recorded speed data. The Traffic Resilience model has been tested on real life data.

Original language	English
Title of host publication	Computer Systems and Technologies - 24th International Conference, CompSysTech 2023
Subtitle of host publication	Proceedings
Editors	Tzvetomir Vassilev, Roumen Trifonov
Publisher	Association for Computing Machinery (ACM)
Pages	74-78
Number of pages	5
ISBN (Electronic)	9798400700477
DOIs	https://doi.org/10.1145/3606305.3606314
Publication status	Published - 2023
Event	24th International Conference on Computer Systems and Technologies, CompSysTech 2023 - Ruse, Bulgaria Duration: 16 Jun 2023 → 17 Jun 2023

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	24th International Conference on Computer Systems and Technologies, CompSysTech 2023
Country/Territory	Bulgaria
City	Ruse
Period	16/06/23 → 17/06/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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10.1145/3606305.3606314

3606305.3606314Final published version, 1.39 MB

Cite this

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title = "Model and Computation of traffic Resilience",

abstract = "At many times we observed disturbances of traffic flow on highways. This may be caused by traffic accidents, bad weather conditions, road maintenance or rush hours. The Road Traffic Management takes many rules and regulation, to make road sections more robust to disturbances and improve the recovery from disturbances in traffic flow (traffic resilience). To study the effect and impact of these rules and regulations assessment models of traffic resilience was needed. In this paper, we designed and tested such an assessment model. The model is inspired by the well-known Resilience Triangle. The assessment of traffic resilience was based on measurements of the speed of traffic flow on highways. Neural Networks were used to model and smooth recorded speed data. The Traffic Resilience model has been tested on real life data.",

author = "Leon Rothkrantz",

year = "2023",

doi = "10.1145/3606305.3606314",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery (ACM)",

pages = "74--78",

editor = "Tzvetomir Vassilev and Roumen Trifonov",

booktitle = "Computer Systems and Technologies - 24th International Conference, CompSysTech 2023",

address = "United States",

note = "24th International Conference on Computer Systems and Technologies, CompSysTech 2023 ; Conference date: 16-06-2023 Through 17-06-2023",

}

Rothkrantz, L 2023, Model and Computation of traffic Resilience. in T Vassilev & R Trifonov (eds), Computer Systems and Technologies - 24th International Conference, CompSysTech 2023: Proceedings. ACM International Conference Proceeding Series, Association for Computing Machinery (ACM), pp. 74-78, 24th International Conference on Computer Systems and Technologies, CompSysTech 2023, Ruse, Bulgaria, 16/06/23. https://doi.org/10.1145/3606305.3606314

Model and Computation of traffic Resilience. / Rothkrantz, Leon.
Computer Systems and Technologies - 24th International Conference, CompSysTech 2023: Proceedings. ed. / Tzvetomir Vassilev; Roumen Trifonov. Association for Computing Machinery (ACM), 2023. p. 74-78 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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T1 - Model and Computation of traffic Resilience

AU - Rothkrantz, Leon

PY - 2023

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N2 - At many times we observed disturbances of traffic flow on highways. This may be caused by traffic accidents, bad weather conditions, road maintenance or rush hours. The Road Traffic Management takes many rules and regulation, to make road sections more robust to disturbances and improve the recovery from disturbances in traffic flow (traffic resilience). To study the effect and impact of these rules and regulations assessment models of traffic resilience was needed. In this paper, we designed and tested such an assessment model. The model is inspired by the well-known Resilience Triangle. The assessment of traffic resilience was based on measurements of the speed of traffic flow on highways. Neural Networks were used to model and smooth recorded speed data. The Traffic Resilience model has been tested on real life data.

AB - At many times we observed disturbances of traffic flow on highways. This may be caused by traffic accidents, bad weather conditions, road maintenance or rush hours. The Road Traffic Management takes many rules and regulation, to make road sections more robust to disturbances and improve the recovery from disturbances in traffic flow (traffic resilience). To study the effect and impact of these rules and regulations assessment models of traffic resilience was needed. In this paper, we designed and tested such an assessment model. The model is inspired by the well-known Resilience Triangle. The assessment of traffic resilience was based on measurements of the speed of traffic flow on highways. Neural Networks were used to model and smooth recorded speed data. The Traffic Resilience model has been tested on real life data.

UR - https://www.scopus.com/pages/publications/85179881837

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DO - 10.1145/3606305.3606314

M3 - Conference contribution

AN - SCOPUS:85179881837

T3 - ACM International Conference Proceeding Series

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BT - Computer Systems and Technologies - 24th International Conference, CompSysTech 2023

A2 - Vassilev, Tzvetomir

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T2 - 24th International Conference on Computer Systems and Technologies, CompSysTech 2023

Y2 - 16 June 2023 through 17 June 2023

ER -

Model and Computation of traffic Resilience

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UN SDGs

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