@inproceedings{8e3f243aba384e31ae5dc3170d26dd27,
title = "Systematic Data Driven Detection of Unintentional Transitions in Traffic Light Plans",
abstract = "Traffic light plans determine the time allocated to each movement within an intersection. The plan has a high impact on vehicle travel performance, such as on the average delay time or the probability of stopping at the intersection. Traffic engineers of a city control its traffic lights and can make changes in their plans to improve traffic performance. As it is not always easy to predict the impact of such transitions, they can also be detrimental. We present an experimental study of real transitions in traffic plans in 10 cities with a total of over 9900 intersections within a time period of over 40 days. We focus on changes in the cycle time of plans that have a major influence on performance metrics such as delay. We compare the overall impact of such transitions and dive into several of them through a careful analysis. Interestingly, we indicate that many of the changes result in higher delay. To the best of our knowledge, our study is one of the largest experimental studies of traffic conditions in recent years.",
author = "Ori Rottenstreich and Dan Karliner and Eliav Buchnik and Shai Ferster and Tom Kalvari and Omer Litov and Nitzan Tur and Danny Veikherman and Avishai Zagoury and Jack Haddad and Dotan Emanuel and Avinatan Hassidim",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024 ; Conference date: 24-09-2024 Through 27-09-2024",
year = "2024",
month = jan,
day = "1",
doi = "10.1109/ITSC58415.2024.10919903",
language = "الإنجليزيّة",
series = "IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC",
pages = "3554--3560",
booktitle = "2024 IEEE 27th International Conference on Intelligent Transportation Systems, ITSC 2024",
}