Multiobjective Model Predictive Control Based on Urban and Emission Macroscopic Fundamental Diagrams

Tesone, Alessio; Tettamanti, Tamás; Varga, Balázs; Bifulco, Gennaro Nicola; Pariota, Luigi · 2024 · OpenAlex-citations

DOI: 10.1109/access.2024.3387664

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Summary

This paper addresses the challenge of managing urban traffic congestion while simultaneously reducing vehicle emissions, a critical issue given the environmental and economic impacts of motorization. The authors propose a Multiobjective Model Predictive Control (M-MPC) framework that integrates traditional Macroscopic Fundamental Diagrams (MFDs) with emerging Emission Macroscopic Fundamental Diagrams (e-MFDs). While existing MFD-based control strategies often focus solely on maximizing network throughput or minimizing travel time, this work aims to balance these mobility goals with the minimization of CO2 emissions. The motivation stems from the recognition that maximizing throughput can inadvertently increase emissions, and that previous studies often relied on simplified, toy-network simulations or single-objective controllers that failed to account for the heterogeneous nature of urban traffic and its environmental footprint. The methodology involves dividing the urban network into homogeneous regions, each characterized by its own calibrated MFD and e-MFD curves. The M-MPC operates as a high-level controller that optimizes route guidance by determining optimal flow split ratios between regions to balance congestion and emission levels. This approach utilizes a weighted cost function within the MPC framework, where weights are dynamically adjusted based on real-time network conditions. The control strategy is validated using a realistic, large-scale microscopic traffic simulation of the entire city of Luxembourg, implemented in the SUMO simulator. This represents a significant departure from prior literature, which typically tested controllers on partial networks or synthetic data. The simulation incorporates real-world demand and supply data, ensuring that the distinct topological characteristics of different zones are accurately reflected in the calibrated MFD and e-MFD models. The study demonstrates that the proposed M-MPC effectively exploits road network capacity while efficiently reducing traffic-induced emissions. By benchmarking the multiobjective approach against simpler strategies—including a standard Dijkstra routing algorithm, a mono-objective MPC for throughput maximization, and a mono-objective MPC for emission minimization—the authors show that the integrated approach offers superior performance in balancing conflicting objectives. The results indicate that the controller can steer the network away from high-emission congested states without significantly compromising throughput. The use of region-specific e-MFD curves allows for a more accurate representation of the relationship between aggregate traffic dynamics and emissions, particularly in distinguishing between free-flow, saturation, and congested regimes. The significance of this work lies in its contribution to the field of network-level traffic control by providing a robust, scalable framework that addresses both mobility and environmental sustainability. The validation on a full-city scale using realistic data underscores the practical applicability of MFD-based control schemes. Furthermore, the introduction of a dynamic weighting mechanism for multiobjective optimization offers a pathway for harmonizing divergent urban management goals. This research highlights the potential of Intelligent Transportation Systems, such as Variable Message Signs, to implement such control strategies, thereby improving the overall efficiency and environmental performance of urban road networks.

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• traffic density