Variable Speed Limits Control in an Urban Road Network to Reduce Environmental Impact of Traffic

Othman, Bassel; De Nunzio, Giovanni; Di Domenico, Domenico; Canudas-de-Wit, Carlos · 2020 · Crossref

DOI: 10.23919/acc45564.2020.9147617

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Summary

This paper addresses the challenge of improving traffic sustainability and efficiency in urban road networks by implementing Variable Speed Limits (VSL). While VSL systems are common for managing congestion and safety, they often neglect environmental impacts. The authors propose a Nonlinear Model Predictive Control (NMPC) framework that optimizes speed limits to simultaneously reduce energy consumption and pollutant emissions while maintaining traffic fluidity. The study specifically targets the gap in literature regarding large-scale VSL strategies based on macroscopic traffic models that explicitly account for environmental factors in urban settings. The methodology utilizes a first-order macroscopic traffic flow model, specifically the Cell Transmission Model (CTM), extended to handle urban networks with signalized intersections and First-In, First-Out (FIFO) policies. A key innovation is the estimation of vehicle acceleration—both temporal (within cells) and spatial (between cells)—to accurately calculate energy consumption and NOx emissions using the VT-macro approach. The NMPC controller optimizes speed limits every 5 minutes over a 30-minute prediction horizon, with speed limits bounded between 20 km/h and 50 km/h. The objective function balances ecological metrics (fuel consumption, NOx) and traffic performance metrics (total distance traveled), weighted equally. Simulations were conducted on a synthetic 40-road grid network with 8 entering and 8 exiting roads, subjected to a constant demand of 9600 vehicles per hour. Simulation results compare the proposed VSL control against baseline scenarios with constant speed limits of 30 km/h and 50 km/h. The findings demonstrate that the NMPC controller effectively reduces both total fuel consumption and NOx emissions compared to the uncontrolled reference cases. Crucially, the controller achieves these environmental benefits without increasing the total time spent by vehicles queuing at the network boundaries, thereby avoiding delays for users entering the system. In congested conditions, the approach successfully lowers energy consumption and travel time within the network. The study confirms that optimizing speed limits dynamically allows for a trade-off that improves ecological outcomes without sacrificing traffic throughput or user comfort at network entry points. The significance of this work lies in its demonstration that macroscopic models can be effectively used for eco-management in complex urban environments. By integrating acceleration-based emission models into a predictive control framework, the paper provides a viable strategy for reducing the environmental footprint of urban traffic. The results suggest that VSL can serve as a dual-purpose tool for congestion mitigation and pollution reduction, offering a practical solution for smart city infrastructure that prioritizes sustainability without compromising operational efficiency.

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• speed management