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Risk appetite-enhanced game-theoretic approach for modelling mandatory lane-changing behaviour in freeway merging
Bingtong Wang1,2, Shunchao Wang3, Gaili He2,4
1School of Transportation, Southeast University, Nanjing, China.
Plos One
|August 7, 2026
Summary
This study models mandatory lane changes using game theory and prospect theory to predict driver behavior. Incorporating loss aversion significantly improves the accuracy of traffic safety models.
Area of Science:
- Traffic Safety
- Behavioral Economics
- Game Theory
Background:
- Mandatory lane changes pose traffic safety risks.
- Driver risk appetite is crucial for modeling merging behavior.
- Game theory is a common approach for analyzing lane-changing interactions.
Purpose of the Study:
- Develop an empirically accurate model for mandatory lane changes.
- Incorporate drivers' risk appetite into merging behavior models.
- Utilize prospect theory to enhance decision-making models.
Main Methods:
- A non-cooperative game-theoretical decision-making model with two players (mainline and on-ramp vehicles).
- Payoff functions based on prospect theory, including an S-shaped value function and a loss-aversion weighting function.
- Bilevel optimization for parameter estimation, combining nonlinear programming and Nash equilibrium analysis.
- Calibration and validation using vehicle trajectory data from US-101 and I-80 datasets.
Main Results:
- The proposed model achieved high detection rates (92.14% on US-101, 92.34% on I-80).
- Validation errors were reported using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).
- Prospect theory integration enhanced behavioral interpretability and predictive performance compared to existing models.
Conclusions:
- The developed model accurately predicts driver behavior in mandatory lane changes.
- Incorporating loss aversion via prospect theory is key to improving traffic safety models.
- The model offers a robust framework for understanding and mitigating risks in freeway merging scenarios.
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