Standard

Restriction Set Design for Travel Demand Values in an Urban Road Network. / Raevskaya, Anastasiya; Krylatov, Alexander; Ageev, Petr; Kuznetsova, Darya.

Artificial Intelligence Trends in Systems - Proceedings of 11th Computer Science On-line Conference 2022, Vol 2. ed. / Radek Silhavy. Springer Nature, 2022. p. 110-120 (Lecture Notes in Networks and Systems; Vol. 502 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Raevskaya, A, Krylatov, A, Ageev, P & Kuznetsova, D 2022, Restriction Set Design for Travel Demand Values in an Urban Road Network. in R Silhavy (ed.), Artificial Intelligence Trends in Systems - Proceedings of 11th Computer Science On-line Conference 2022, Vol 2. Lecture Notes in Networks and Systems, vol. 502 LNNS, Springer Nature, pp. 110-120, 11th Computer Science On-line Conference, CSOC 2022, Virtual, Online, 26/04/22. https://doi.org/10.1007/978-3-031-09076-9_10

APA

Raevskaya, A., Krylatov, A., Ageev, P., & Kuznetsova, D. (2022). Restriction Set Design for Travel Demand Values in an Urban Road Network. In R. Silhavy (Ed.), Artificial Intelligence Trends in Systems - Proceedings of 11th Computer Science On-line Conference 2022, Vol 2 (pp. 110-120). (Lecture Notes in Networks and Systems; Vol. 502 LNNS). Springer Nature. https://doi.org/10.1007/978-3-031-09076-9_10

Vancouver

Raevskaya A, Krylatov A, Ageev P, Kuznetsova D. Restriction Set Design for Travel Demand Values in an Urban Road Network. In Silhavy R, editor, Artificial Intelligence Trends in Systems - Proceedings of 11th Computer Science On-line Conference 2022, Vol 2. Springer Nature. 2022. p. 110-120. (Lecture Notes in Networks and Systems). https://doi.org/10.1007/978-3-031-09076-9_10

Author

Raevskaya, Anastasiya ; Krylatov, Alexander ; Ageev, Petr ; Kuznetsova, Darya. / Restriction Set Design for Travel Demand Values in an Urban Road Network. Artificial Intelligence Trends in Systems - Proceedings of 11th Computer Science On-line Conference 2022, Vol 2. editor / Radek Silhavy. Springer Nature, 2022. pp. 110-120 (Lecture Notes in Networks and Systems).

BibTeX

@inproceedings{2f8776c5bc1849b182785e529cd4a690,
title = "Restriction Set Design for Travel Demand Values in an Urban Road Network",
abstract = "Efficient traffic management in a modern urban road network seems impossible today without the support of artificial intelligence systems that use accurate travel demand data for predicting traffic congestions. However, despite researchers being equipped with different approaches and techniques to cope with travel demand estimation, there is still a gap between up-to-date accuracy requirements and available methods. The present paper is devoted to this urgent problem and investigates evolutionary strategies for the travel demand search task, formulated as an inverse traffic assignment problem. We develop polynomial regression models to estimate overall demand by observed congestions. The overall demand value allows one to restrict the set of feasible travel demand matrices. Eventually, we offer the travel demand estimation problem minimizing the deviation of both congestion and time on the simplex.",
keywords = "Bilevel programming, Evolutionary optimization, Regression",
author = "Anastasiya Raevskaya and Alexander Krylatov and Petr Ageev and Darya Kuznetsova",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 11th Computer Science On-line Conference, CSOC 2022 ; Conference date: 26-04-2022 Through 26-04-2022",
year = "2022",
doi = "10.1007/978-3-031-09076-9_10",
language = "English",
isbn = "9783031090752",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Nature",
pages = "110--120",
editor = "Radek Silhavy",
booktitle = "Artificial Intelligence Trends in Systems - Proceedings of 11th Computer Science On-line Conference 2022, Vol 2",
address = "Germany",

}

RIS

TY - GEN

T1 - Restriction Set Design for Travel Demand Values in an Urban Road Network

AU - Raevskaya, Anastasiya

AU - Krylatov, Alexander

AU - Ageev, Petr

AU - Kuznetsova, Darya

N1 - Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2022

Y1 - 2022

N2 - Efficient traffic management in a modern urban road network seems impossible today without the support of artificial intelligence systems that use accurate travel demand data for predicting traffic congestions. However, despite researchers being equipped with different approaches and techniques to cope with travel demand estimation, there is still a gap between up-to-date accuracy requirements and available methods. The present paper is devoted to this urgent problem and investigates evolutionary strategies for the travel demand search task, formulated as an inverse traffic assignment problem. We develop polynomial regression models to estimate overall demand by observed congestions. The overall demand value allows one to restrict the set of feasible travel demand matrices. Eventually, we offer the travel demand estimation problem minimizing the deviation of both congestion and time on the simplex.

AB - Efficient traffic management in a modern urban road network seems impossible today without the support of artificial intelligence systems that use accurate travel demand data for predicting traffic congestions. However, despite researchers being equipped with different approaches and techniques to cope with travel demand estimation, there is still a gap between up-to-date accuracy requirements and available methods. The present paper is devoted to this urgent problem and investigates evolutionary strategies for the travel demand search task, formulated as an inverse traffic assignment problem. We develop polynomial regression models to estimate overall demand by observed congestions. The overall demand value allows one to restrict the set of feasible travel demand matrices. Eventually, we offer the travel demand estimation problem minimizing the deviation of both congestion and time on the simplex.

KW - Bilevel programming

KW - Evolutionary optimization

KW - Regression

UR - http://www.scopus.com/inward/record.url?scp=85135007801&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/be088840-5fef-3af0-b2c9-79ca3a76d073/

U2 - 10.1007/978-3-031-09076-9_10

DO - 10.1007/978-3-031-09076-9_10

M3 - Conference contribution

AN - SCOPUS:85135007801

SN - 9783031090752

T3 - Lecture Notes in Networks and Systems

SP - 110

EP - 120

BT - Artificial Intelligence Trends in Systems - Proceedings of 11th Computer Science On-line Conference 2022, Vol 2

A2 - Silhavy, Radek

PB - Springer Nature

T2 - 11th Computer Science On-line Conference, CSOC 2022

Y2 - 26 April 2022 through 26 April 2022

ER -

ID: 97924573