Spatio-Temporal Modeling of Traffic Signals | #sciencefather #scientistawards #DeepLearning #SpatioTemporalData #TrafficPrediction #SmartTrafficControl #UrbanMobility

Spatio-Temporal Deep Learning for Predicting Signal Plan Execution in Urban Traffic Networks 

1. Introduction to Intelligent Traffic Signal Control

Modern cities face increasing traffic congestion due to urbanization and limited road infrastructure. Traditional traffic signal systems, often manually configured or based on fixed time cycles, struggle to adapt to real-time conditions. To address these limitations, this research explores the use of deep learning techniques—specifically Spatio-Temporal Graph Convolutional Networks (STGCNs)—to develop a responsive, automated traffic signal management system that adjusts signal timings dynamically based on real-time traffic flow.


2. Role of Graph Neural Networks in Traffic Modeling

Urban road networks can be naturally represented as graphs, where intersections are nodes and roads are edges. Graph Neural Networks (GNNs), particularly Graph Convolutional Networks (GCNs), are powerful tools for analyzing such graph-structured data. GCNs aggregate features from neighboring nodes to learn complex spatial relationships. When combined with time-series models like LSTMs or Temporal Convolutional Networks, GCNs can capture both spatial and temporal dynamics, forming the basis of STGCNs—an ideal model for real-time traffic prediction.

3. Data Collection and Feature Representation

The study uses Bluetooth (BT) detectors deployed across the Tel Aviv urban network to collect traffic-related data such as vehicle speed, travel time, flow, and density. Signal plan (SP) data—like green time duration and signal plan IDs—is extracted from traffic control center databases. These multivariate features are transformed into spatio-temporal graphs, where each node represents a road section, and the evolving data over time forms the input for model training.

4. Model Architecture and Prediction Objectives

Two STGCN-based models are proposed. The first model focuses on predicting traffic speed using historical data. The second, enhanced model predicts green signal durations at intersections by incorporating traffic states, SP IDs (treated as command inputs), and external factors like time-of-day and day-of-week. Categorical features, such as signal plan IDs, are encoded using embedding layers, while temporal features are directly included to improve the model’s ability to adapt to daily traffic patterns.

5. Experimentation and Evaluation

Experiments are conducted on both the entire Tel Aviv network and a specific arterial road (Even Gvirol). Results show that including both explicit (SP ID) and implicit (traffic speed, density) features significantly enhances prediction accuracy. Additionally, fixed-cycle time modeling improves temporal consistency. However, excessive feature combinations (e.g., Bluetooth-derived features) sometimes lead to overfitting or degraded performance, underscoring the importance of feature selection.

6. Benefits and Future Applications

The proposed models pave the way for smarter traffic signal systems that can interpret operator commands and execute appropriate actions, such as adjusting green light durations based on current conditions. This automation could eventually support voice- or text-based traffic control interfaces, enabling real-time, human-in-the-loop traffic management. The approach also supports scalability, making it applicable to various urban environments beyond Tel Aviv.

7. Conclusion

This research demonstrates the potential of combining graph-based deep learning with real-time sensor data for intelligent traffic signal control. By integrating command signals and traffic states into a unified STGCN model, the system can predict and execute optimal signal timings, improving traffic flow, reducing congestion, and setting the foundation for fully automated urban traffic management systems.

#DeepLearning #GraphNeuralNetworks #SpatioTemporalData #STGCN #TrafficPrediction #SmartTrafficControl #UrbanMobility #TrafficSignalOptimization #IntelligentTransportSystems #AIInTransportation

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