# Time-to-Green predictions for fully-actuated signal control systems with supervised learning

arXiv: https://arxiv.org/abs/2208.11344 · HTML: https://mlsanity.com/paper/2208.11344/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 74 references that are in the map.
- First posted on arXiv 24 Aug 2022.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand](https://mlsanity.com/paper/2202.07147/) (2022)
- [Driver Action Prediction Using Deep (Bidirectional) Recurrent Neural Network](https://mlsanity.com/paper/1706.02257/) (2017)
- [Critical ride comfort detection for automated vehicles](https://mlsanity.com/paper/2107.14631/) (2021)
- [Estimating Phase Duration for SPaT Messages](https://mlsanity.com/paper/1710.05394/) (2017)
- [An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation](https://mlsanity.com/paper/2108.07698/) (2021)

## Built on by (most cited first, 3 of 3)

- [Mixture-of-Personas Language Models for Population Simulation](https://mlsanity.com/paper/2504.05019/) (2025)
- [Privacy-Preserving Data Fusion for Traffic State Estimation: A Vertical Federated Learning Approach](https://mlsanity.com/paper/2401.11836/) (2024)
- [HONEST-CAV: Hierarchical Optimization of Network Signals and Trajectories for Connected and Automated Vehicles with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2602.18740/) (2026)

## Abstract (arXiv)

Recently, efforts have been made to standardize signal phase and timing (SPaT) messages. These messages contain signal phase timings of all signalized intersection approaches. This information can thus be used for efficient motion planning, resulting in more homogeneous traffic flows and uniform speed profiles. Despite efforts to provide robust predictions for semi-actuated signal control systems, predicting signal phase timings for fully-actuated controls remains challenging. This paper proposes a time series prediction framework using aggregated traffic signal and loop detector data. We utilize state-of-the-art machine learning models to predict future signal phases' duration. The performance of a Linear Regression (LR), a Random Forest (RF), and a Long-Short-Term-Memory (LSTM) neural network are assessed against a naive baseline model. Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that machine learning models outperform conventional prediction methods. Furthermore, tree-based decision models such as the RF perform best with an accuracy that meets requirements for practical applications.