Rottenstreich, Ori (Google Research), Karliner, Dan (Google Research), Buchnik, Eliav (Google Research), Ferster, Shai (Google Research), Kalvari, Tom (Google Research), Litov, Omer (Google Research), Tur, Nitzan (Google Research), Veikherman, Danny (Google Research), Zagoury, Avishai (Google Research), Haddad, Jack (Google Research), Emanuel, Dotan (Google Research), Hassidim, Avinatan (Google Research)

Systematic Data Driven Detection of Unintentional Transitions in Traffic Light Plans

Scheduled for presentation during the Poster Session "Data Mining and Data Analysis" (FrAT14), Friday, September 27, 2024, 10:30−12:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 3, 2024

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.