Traffic Signaling Prediction Model Using Dual State Data
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Solution Overview
Problem
Current signaling systems for traffic control, particularly light signaling systems, face challenges in accurately predicting switching states and times, which affects vehicle control and traffic regulation, leading to suboptimal prediction quality and potential inefficiencies in energy management and automated driving applications.
Innovation Solution
A method that acquires and combines first and second state data from signaling system controllers and external sources to train a prediction model, enabling the prediction of switching states and times, and outputs these predictions to vehicles for improved traffic management and energy optimization, without requiring extensive infrastructure changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only relevant state data are used for prediction, then prediction quality is improved, but data acquisition complexity increases
Solution Approach 1:
The prediction system segments data acquisition into two distinct channels: a signaling system interface for acquiring first state data (switching states, timing parameters) and a communication interface for acquiring second state data (environmental conditions, traffic patterns). This segmentation allows selective collection of only relevant data types needed for accurate prediction while managing complexity through modular data sources.
Solution Approach 2:
The prediction device is designed with multi-functional interfaces that can acquire multiple types of state data through unified data processing pathways. The signaling system interface and communication interface both feed into the same prediction model, allowing the system to handle diverse data sources (detector data, traffic data, environmental data) through a universal processing framework, reducing overall system complexity.
2Measurement precision
If a comprehensive prediction model is implemented, then prediction accuracy is improved, but computational resources required increase
Solution Approach 1:
The system extracts and utilizes only the essential state data that directly influence switching behavior—specifically first state data from the signaling system controller (switching states, cycle times, detector inputs) and relevant second state data from communication interfaces (traffic patterns, environmental conditions). By extracting only the critical data elements needed for prediction rather than processing all available data, the model achieves good accuracy with reduced computational overhead.
3Ease of manufacture
If existing signaling system infrastructure is used without replacement, then cost is reduced, but integration complexity with prediction system increases
Solution Approach 1:
The prediction device acts as an intermediary layer between the existing signaling system controller and the vehicle or traffic management system. It connects to the signaling system controller through a signaling system interface to acquire state data, processes this data along with additional information from communication interfaces, and generates predictions without requiring modification or replacement of the original signaling system controller. This intermediary approach enables integration with legacy infrastructure while maintaining prediction functionality.
Solution Approach 2:
Instead of replacing the existing signaling system controller, the invention creates a virtual copy or model of the controller's state data by reading and processing its outputs through the signaling system interface. The prediction device reconstructs and analyzes the controller's internal state and switching logic software-based, eliminating the need for expensive hardware replacement while achieving the same predictive functionality.
Data Source
AI summary
A method predicts a switch state and/or a switch time point of a signaling system. The method includes collecting first and second state data, the first and second state data influencing the switch state and/or switch time point. The collection of the first state data includes reading of state data from a signaling system control device of the signaling system by a signaling system interface. The collection of the second state data includes reading in of the state data. A prediction model is provided and configured to make a prediction of the switch time point and/or the switch state of the signaling system based on first and second state data. The switch state and/or the switch time point of the signaling system is predicted via the prediction model using the first and second state data. The predicted switch state and/or switch time point of the signaling system is outputted.