Traffic Signal Phase and Timing Estimation with Large-Scale Floating Car Data
Mingcheng Liao, Zebang Feng, Miao Fan, Shengtong Xu, Haoyi Xiong

TL;DR
This paper presents a robust, large-scale FCD-based framework for accurate traffic signal phase and timing estimation, overcoming limitations of previous methods and supporting extensive real-world deployment in China.
Contribution
We develop an industrial-grade FCD analysis suite that accurately estimates SPaT across diverse intersection types, providing a comprehensive, scalable solution with a large, publicly available dataset.
Findings
Supports over 15 million FCD records daily
Achieves less than 5 seconds error in over 75% of estimations
Operates reliably across diverse road geometries
Abstract
Effective modern transportation systems depend critically on accurate Signal Phase and Timing (SPaT) estimation. However, acquiring ground-truth SPaT information faces significant hurdles due to communication challenges with transportation departments and signal installers. As a result, Floating Car Data (FCD) has become the primary source for large-scale SPaT analyses. Current FCD approaches often simplify the problem by assuming fixed schedules and basic intersection designs for specific times and locations. These methods fail to account for periodic signal changes, diverse intersection structures, and the inherent limitations of real-world data, thus lacking a comprehensive framework that is universally applicable. Addressing this limitation, we propose an industrial-grade FCD analysis suite that manages the entire process, from initial data preprocessing to final SPaT estimation.…
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