Traffic Signal Phase and Timing Estimation with Large-Scale Floating Car Data

2025-07-13

AI Summary

Accurate estimation of Signal Phase and Timing (SPaT) remains challenging in the absence of inter-departmental coordination within transportation agencies. Method: This paper proposes the first scalable, highly robust, fully automated SPaT estimation framework, leveraging large-scale floating-car data. It integrates trajectory clustering, speed-change pattern recognition, adaptive time-series segmentation and denoising, and spatiotemporal contextual modeling to enable multi-period pattern identification and dynamic cycle detection—eliminating reliance on fixed signal cycles or simplified intersection topologies. Contribution/Results: The system processes over 15 million trajectories daily across more than two million traffic signals nationwide. It achieves SPaT estimation errors under 5 seconds for over 75% of signals and has been deployed in a production navigation platform, demonstrating industrial-grade scalability, robustness, and generalizability.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation
• Machine Learning: Time-Series/Data Streams

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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. Our approach estimates signal phases, identifies time-of-day (TOD) periods, and determines the durations of red and green lights. The framework's notable stability and robustness across diverse conditions, regardless of road geometry, is a key feature. Furthermore, we provide a cleaned, de-identified FCD dataset and supporting parameters to facilitate future research. Currently operational within our navigation platform, the system analyses over 15 million FCD records daily, supporting over two million traffic signals in mainland China, with more than 75% of estimations demonstrating less than five seconds of error.

Problem

• Estimating traffic signal phases and timing accurately
• Overcoming limitations of current Floating Car Data methods
• Handling diverse intersection structures and signal changes

Innovation

• Industrial-grade FCD analysis suite for SPaT
• Estimates signal phases and TOD periods
• Robust framework for diverse road conditions