Dundar,S.Berktas,E.S.Hoke,M.C.İnşaat Mühendisliği / Civil Engineering2024-05-252024-05-2520220978-166548894-510.1109/ASYU56188.2022.99255422-s2.0-85142681246https://doi.org/10.1109/ASYU56188.2022.9925542https://hdl.handle.net/20.500.14517/2587E-scooters have become a popular transportation alternative, especially for short distances. Compared to motor vehicles, e-scooters are operated at lower speeds on urban highways, therefore; changing traffic flow characteristics and reducing road traffic safety. Within the scope of this study, the effects of different demand levels of e-scooters on Istanbul Bagdat Avenue traffic were examined and modeled using artificial neural networks. Trained artificial neural network can estimate the hourly traffic volume and average speed values with a R2 value of 0.9699 and 0.9853, respectively. The effects of e-scooters on traffic can also be examined for different traffic conditions with the trained network structure. © 2022 IEEE.eninfo:eu-repo/semantics/closedAccessartificial neural networkse-scootermicromobilityPTV VissimConference Object