Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › Рецензирование
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. ред. / Radek Silhavy. Springer Nature, 2022. стр. 110-120 (Lecture Notes in Networks and Systems; Том 502 LNNS).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › Рецензирование
}
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