Ecological traffic management: A review of the modeling and control strategies for improving environmental sustainability of road transportation

Othman, Bassel; De Nunzio, Giovanni; Di Domenico, Domenico; de Wit, Carlos Canudas · 2019 · OpenAlex-citations

DOI: 10.1016/j.arcontrol.2019.09.003

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Summary

This review paper addresses the growing environmental impact of road transportation, which accounts for a significant portion of global liquid fuel consumption and carbon dioxide emissions. The authors highlight that while technological advancements like connected and automated vehicles promise efficiency gains, their system-wide impacts remain uncertain due to complex non-linear dependencies and potential side effects, such as increased congestion or travel distances. Consequently, the paper aims to survey existing modeling approaches and control strategies that explicitly target energy consumption and pollutant emissions, rather than relying solely on congestion mitigation as a proxy for environmental performance. The study categorizes research into two primary levels: single-vehicle and traffic flow. For single vehicles, the authors review microscopic emission and energy consumption models, distinguishing between data-driven and physical approaches. Data-driven methods include look-up tables derived from chassis dynamometer tests and regression models (such as VT-micro and POLY) that estimate emissions based on instantaneous speed, acceleration, and road grade. Physical models are further divided into deterministic methods, which calculate power demand using vehicle longitudinal dynamics and engine efficiency parameters (e.g., CMEM), and probabilistic methods that estimate emissions based on average traffic conditions when specific vehicle dynamics are unknown. The review also covers traffic-level modeling, focusing on fluid-dynamics models and macroscopic approaches that describe vehicular flow kinematics to estimate aggregate energy use. The paper provides a comprehensive overview of control strategies designed to improve environmental sustainability. At the vehicle level, strategies include eco-driving, which optimizes individual speed profiles, and eco-routing, which selects routes to minimize emissions. At the traffic level, the review examines control techniques such as traffic light optimization, speed limit adjustments, dynamic routing, and the integration of automated vehicles. The authors analyze how these strategies interact with the previously discussed models to achieve energy efficiency. The significance of this work lies in its identification of research gaps and the clarification of the relationship between traffic congestion and environmental impact. By systematically reviewing modeling and control techniques, the paper provides a structured framework for future research in eco-traffic management. It emphasizes the need for models that accurately capture transient operations and real-world driving conditions, as well as control strategies that directly optimize environmental criteria. This synthesis supports the development of more effective policies and technologies for reducing the carbon footprint of road transportation, particularly in urban networks.

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• Theoretical Contribution: computational model