Decision Support In Dynamic Traffic Management: Real-time Scenario Evaluation.pdf

Real-Time Scenario Evaluation

This technical report by Delft University of Technology's Center for Systems and Control presents BSES (Boss Scenario Evaluation System), a real-time decision support tool for dynamic traffic management (DTM).

Problem Statement:

The report highlights the challenges facing Regional Traffic Management Centers (RTMCs) in efficiently and safely managing traffic flows on motorways and urban networks. While RTMC operators rely on current measurement data, predicting the impact of control scenarios – especially under non-recurrent conditions like incidents or events – remains difficult.

Solution: BSES (Fuzzy Multi-Agent Case-Based Reasoning)

BSES aims to bridge this gap by providing conditional predictions about future traffic states based on:

Case Base: A repository of synthetic (simulated) scenarios showcasing the effects of control measures under various conditions.
Fuzzy Logic: Used to determine the similarity between current network conditions and cases in the base, enabling nuanced comparisons.
Agent-Based Approach: Predicts impacts on smaller subnetworks before aggregating these predictions for the entire network.

Key Features:

Case-Based: Leverages examples (cases) of control scenarios' effects under different circumstances, both real and synthetic.
Fuzzy Logic: Enables similarity measurements between current conditions and cases in the base, allowing for nuanced predictions.
Agent-Based: Analyzes smaller subnetworks individually before combining results for a comprehensive network view.
Real-Time Predictions: Designed to provide timely support to RTMC operators by evaluating control scenarios instantaneously.

Test Results & Validation:

The report demonstrates BSES's capabilities through synthetic data testing, showcasing its ability to:

Accurately predict various traffic metrics (travel times, vehicle stop times, average speeds, fuel consumption) under different conditions.
* Compare favorably with predictions from a detailed simulation model used to build the case base, validating the system's generalizability.

Future Directions:

The authors envision incorporating real traffic data and refining the system through integration with more sophisticated traffic simulation models for enhanced accuracy and applicability.