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Rottenstreich, Ori (Google Research), Buchnik, Eliav (Google Research), Ferster, Shai (Google Research), Kalvari, Tom (Google Research), Tsibulsky, Ron (Google Research), Veikherman, Danny (Google Research), Zagoury, Avishai (Google Research), Haddad, Jack (Google Research), Emanuel, Dotan (Google Research), Hassidim, Avinatan (Google Research)

Day-Of-The-Week Awareness in Time of Day Breakpoints for Traffic Light Plans

Scheduled for presentation during the Video Session "On-Demand Video Presentations" (VP-VP), Saturday, November 22, 2025, 08:00−18:00, On-Demand Platform

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on April 2, 2026

Keywords AI, Machine Learning for Dynamic Traffic Signal Control and Optimization

Abstract

Time-of-day breakpoints (TODs) refer to the times over the day in which the plan of a traffic light is changed. Traditionally, TODs are selected jointly for all weekdays (Monday-Friday), typically with additional TODs dedicated to weekends. In this paper, we present an alternative approach motivated by traffic characteristics that can differ among the weekdays Monday-Friday and consider TODs which are day-of-the-week aware. The traffic-aware approach studies similarities among days and computes TODs that can be shared among days with similar characteristics but can also have other forms for weekdays with unique characteristics. Based on traffic properties derived from anonymized trajectories, we apply the new methodology to compute time-of-day breakpoints that are day-of-the-week aware in the city of Rio de Janeiro, Brazil and estimate the impact of the new methodology.

 

 

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