TY - GEN
T1 - A Machine-Learning Approach to Queue Length Estimation Using Tagged Customers Emission
AU - Efrosinin, Dmitry
AU - Vishnevskiy, Vladimir
AU - Stepanova, Natalia
PY - 2024
Y1 - 2024
N2 - In this paper, we consider the problem of the queue length estimation if only some small number of a so-called tagged customers is observable. The problem is treated in terms of the queueing of vehicles behind a traffic light. A supervised machine learning, particularly an artificial neural network, is used to construct non-linear relationships between the feature and the target. For data generation we simulate an appropriate queueing system. We used an auxiliary Fourier series correction factor by training the neural network. As a result, the quality of the queue length estimation expressed in form of the empirical distribution function of an absolute error was considerably improved.
AB - In this paper, we consider the problem of the queue length estimation if only some small number of a so-called tagged customers is observable. The problem is treated in terms of the queueing of vehicles behind a traffic light. A supervised machine learning, particularly an artificial neural network, is used to construct non-linear relationships between the feature and the target. For data generation we simulate an appropriate queueing system. We used an auxiliary Fourier series correction factor by training the neural network. As a result, the quality of the queue length estimation expressed in form of the empirical distribution function of an absolute error was considerably improved.
UR - https://www.scopus.com/pages/publications/85189503425
U2 - 10.1007/978-3-031-50482-2_21
DO - 10.1007/978-3-031-50482-2_21
M3 - Conference proceedings
SN - 978-3-031-50481-5
VL - 14123
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 265
EP - 276
BT - Distributed Computer and Communication Networks: Control, Computation, Communications. 26th International Conference, DCCN 2023, Moscow, Russia, September 25–29, 2023, Revised Selected Papers
A2 - Vishnevskiy, Vladimir M.
A2 - Kozyrev, Dmitry V.
A2 - Samouylov, Konstantin E.
A2 - Kozyrev, Dmitry V.
PB - Springer
ER -