Enhancing Model-Based Anticipatory Traffic Signal Control with Metamodeling and Adaptive Optimization

Wei Huang, Yang Hu and Xuanyu Zhang
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Wei Huang: School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518063, China
Yang Hu: School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518063, China
Xuanyu Zhang: School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518063, China

Mathematics, 2022, vol. 10, issue 15, 1-18

Abstract: Traffic signal control is one effective way to alleviate traffic congestion. Anticipatory traffic signal control determines signal settings from a network planning perspective, which takes into account the influence of travelers’ route choice response and triggers better equilibrium flow patterns for better network performance. For the route choice response, it is usually predicted by a response function known as traffic assignment model. However, the response behavior can never be precisely modeled, leading to a mismatch between the modeled and real traffic flow patterns. This model-reality mismatch generally contributes to suboptimal control performance and hence brings unexpected congestion in real-life traffic operations. This study aims to address the model-reality mismatch and proposes an effective anticipatory traffic control for real operations. A metamodel is introduced that serves as a surrogate of the unknown structural model bias. Then an iterative optimizing control scheme is applied to correct the model bias by learning from observations. By integrating the model-based control design with data-driven learning techniques, the metamodeling framework is able to enhance the control performance. Moreover, the analytical model bias formulation allows theoretical investigation of the model approximation error. To further improve the control performance, a joint traffic model parameter estimation is developed, hence achieving a better model calibration jointly with the model bias correction. The proposed control method is examined on a test network. Numerical examples confirm the effectiveness of the proposed method in improving control performance despite the model-reality mismatch. Comparison results show that the proposed method outperforms the traditional model-based control method and an improvement of 14.8% in total travel time is achieved in the example network.

Keywords: traffic signal control; network design; equilibrium flow; model bias; metamodeling; iterative learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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