Data-Driven Approach for Modeling the Mixed Traffic Conditions Using Supervised Machine Learning

Narayana Raju; shriniwas arkatkar; gaurang joshi; Constantinos Antoniou

doi:10.1007/978-981-16-6936-1_1

Data-Driven Approach for Modeling the Mixed Traffic Conditions Using Supervised Machine Learning

Narayana Raju, shriniwas arkatkar, gaurang joshi, Constantinos Antoniou

Transport and Planning

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

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Abstract

The article describes modeling vehicular movements using supervised machine learning algorithms with trajectory data from heterogeneous non-lane-based traffic conditions. The trajectory data on the mid-block road section of around 540 m is used in the study. Supervised machine learning algorithms are employed to model the vehicular positions. A set of parameters were identified for modeling the longitudinal and lateral positions. With the set of parameters, the algorithm’s potentiality for mimicking vehicular positions is evaluated. It was identified that supervised machine learning algorithms would model the vehicles’ positions with accuracy in the range of 20–60 mean absolute percentage error. The k-NN algorithm was marginally edging past all algorithms and acted as a promising candidate for modeling vehicular positions.

Original language	English
Title of host publication	Intelligent Infrastructure in Transportation and Management
Subtitle of host publication	Proceedings of i-TRAM 2021
Editors	Jiten Shah, Shriniwas S. Arkatkar, Pravin Jadhav
Publisher	Springer
Chapter	1
Pages	3-12
Number of pages	10
ISBN (Electronic)	978-981-16-6937-8
ISBN (Print)	978-981-16-6935-4
DOIs	https://doi.org/10.1007/978-981-16-6936-1_1
Publication status	Published - 2022

Publication series

Name	Studies in Infrastructure and Control
ISSN (Print)	2730-6453
ISSN (Electronic)	2730-6461

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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10.1007/978-981-16-6936-1_1

Raju2022_Chapter_Data-DrivenApproachForModelingFinal published version, 496 KB

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Raju, N., arkatkar, S., joshi, G., & Antoniou, C. (2022). Data-Driven Approach for Modeling the Mixed Traffic Conditions Using Supervised Machine Learning. In J. Shah, S. S. Arkatkar, & P. Jadhav (Eds.), Intelligent Infrastructure in Transportation and Management: Proceedings of i-TRAM 2021 (pp. 3-12). ( Studies in Infrastructure and Control ). Springer. https://doi.org/10.1007/978-981-16-6936-1_1

Raju, Narayana ; arkatkar, shriniwas ; joshi, gaurang et al. / Data-Driven Approach for Modeling the Mixed Traffic Conditions Using Supervised Machine Learning. Intelligent Infrastructure in Transportation and Management: Proceedings of i-TRAM 2021. editor / Jiten Shah ; Shriniwas S. Arkatkar ; Pravin Jadhav. Springer, 2022. pp. 3-12 ( Studies in Infrastructure and Control ).

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abstract = "The article describes modeling vehicular movements using supervised machine learning algorithms with trajectory data from heterogeneous non-lane-based traffic conditions. The trajectory data on the mid-block road section of around 540 m is used in the study. Supervised machine learning algorithms are employed to model the vehicular positions. A set of parameters were identified for modeling the longitudinal and lateral positions. With the set of parameters, the algorithm{\textquoteright}s potentiality for mimicking vehicular positions is evaluated. It was identified that supervised machine learning algorithms would model the vehicles{\textquoteright} positions with accuracy in the range of 20–60 mean absolute percentage error. The k-NN algorithm was marginally edging past all algorithms and acted as a promising candidate for modeling vehicular positions.",

author = "Narayana Raju and shriniwas arkatkar and gaurang joshi and Constantinos Antoniou",

note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. ",

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Raju, N, arkatkar, S, joshi, G & Antoniou, C 2022, Data-Driven Approach for Modeling the Mixed Traffic Conditions Using Supervised Machine Learning. in J Shah, SS Arkatkar & P Jadhav (eds), Intelligent Infrastructure in Transportation and Management: Proceedings of i-TRAM 2021. Studies in Infrastructure and Control , Springer, pp. 3-12. https://doi.org/10.1007/978-981-16-6936-1_1

Data-Driven Approach for Modeling the Mixed Traffic Conditions Using Supervised Machine Learning. / Raju, Narayana; arkatkar, shriniwas; joshi, gaurang et al.
Intelligent Infrastructure in Transportation and Management: Proceedings of i-TRAM 2021. ed. / Jiten Shah; Shriniwas S. Arkatkar; Pravin Jadhav. Springer, 2022. p. 3-12 ( Studies in Infrastructure and Control ).

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

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N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2022

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AB - The article describes modeling vehicular movements using supervised machine learning algorithms with trajectory data from heterogeneous non-lane-based traffic conditions. The trajectory data on the mid-block road section of around 540 m is used in the study. Supervised machine learning algorithms are employed to model the vehicular positions. A set of parameters were identified for modeling the longitudinal and lateral positions. With the set of parameters, the algorithm’s potentiality for mimicking vehicular positions is evaluated. It was identified that supervised machine learning algorithms would model the vehicles’ positions with accuracy in the range of 20–60 mean absolute percentage error. The k-NN algorithm was marginally edging past all algorithms and acted as a promising candidate for modeling vehicular positions.

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Raju N, arkatkar S, joshi G, Antoniou C. Data-Driven Approach for Modeling the Mixed Traffic Conditions Using Supervised Machine Learning. In Shah J, Arkatkar SS, Jadhav P, editors, Intelligent Infrastructure in Transportation and Management: Proceedings of i-TRAM 2021. Springer. 2022. p. 3-12. ( Studies in Infrastructure and Control ). doi: 10.1007/978-981-16-6936-1_1

Data-Driven Approach for Modeling the Mixed Traffic Conditions Using Supervised Machine Learning

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