Analysis of the Impact of Variable Speed Limits on Environmental Sustainability and Traffic Performance in Urban Networks

Othman, Bassel; De Nunzio, Giovanni; Di Domenico, Domenico; Canudas-de-Wit, Carlos · 2022 · Crossref

DOI: 10.1109/tits.2022.3192129

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Summary

This paper investigates the potential of Variable Speed Limits (VSLs) to enhance both environmental sustainability and traffic performance within urban road networks. While VSLs have been extensively studied for highway applications, their efficacy in urban environments—characterized by signalized intersections and complex traffic dynamics—remains underexplored, particularly regarding explicit ecological optimization. The study addresses this gap by proposing an "eco-VSL" controller designed to reduce pollutant emissions and improve traffic fluidity. The research is motivated by the need to mitigate transportation-related environmental degradation and the increasing availability of connected and automated vehicle technologies that enable dynamic infrastructure control. The methodology employs a hybrid modeling approach combining microscopic and macroscopic simulations. Traffic dynamics are simulated using the microscopic simulator SUMO, governed by the Intelligent Driver Model (IDM) to capture individual vehicle behaviors. For control purposes, a Nonlinear Model Predictive Control (NMPC) framework is implemented. This controller utilizes a macroscopic Cell Transmission Model (CTM), adapted for urban networks with traffic light signals and FIFO intersection policies, to predict traffic evolution. To estimate energy consumption within the control horizon, an Artificial Neural Network (ANN) is calibrated to approximate fuel usage based on macroscopic variables such as density, speed, and traffic light states. The actual environmental impact is evaluated using a detailed microscopic physical model that calculates fuel consumption and NOx emissions for a Euro 4 diesel passenger car based on longitudinal dynamics, including aerodynamic drag, rolling resistance, and engine torque. The study evaluates the eco-VSL controller in a synthetic urban network under various traffic scenarios, comparing its performance against baseline constant speed limits of 30 km/h and 50 km/h. The results indicate that the proposed controller effectively manages transient phases between different congestion levels. Specifically, the eco-VSL strategy accelerates the decongestion process of the network, leading to improved traffic throughput and reduced travel times. Furthermore, the dynamic adjustment of speed limits results in lower fuel consumption and NOx emissions compared to static speed limits, demonstrating a simultaneous improvement in both environmental sustainability and traffic performance. These benefits extend not only to the controlled network but also to its boundary roads. The significance of this work lies in its demonstration that large-scale VSLs can be effectively applied to urban environments with explicit ecological considerations, a domain previously lacking in literature. By integrating adapted macroscopic models for control with high-fidelity microscopic models for evaluation, the study provides a robust framework for eco-driving strategies. The findings suggest that dynamic speed limit control is a viable lever for urban traffic eco-management, offering a pathway to reduce urban air pollution and improve traffic efficiency without requiring changes to vehicle routing or infrastructure geometry. This contributes to the broader field of intelligent transportation systems by validating the use of NMPC with ANN-based energy prediction for sustainable urban mobility.

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• speed management