EQ-ALINEA – Equitable Ramp Metering for Sustainable Metropolitan Highways

The 64th IEEE Conference on Decision and Control (CDC 2025), Rio de Janeiro, Brazil, December 09-12, 2025

Urban transportation networks increasingly suffer from congestion. Negative externalities resulting from noise and pollution, affect public health, quality of life, and the economy. The major traffic bottlenecks in cities are conflicts at intersections, leading to this pressing issue. Intelligent transportation systems leverage sensors to optimize traffic flows, mainly by control of traffic lights. Green-Pressure is an extension of the Max-Pressure algorithm, that leverages vehicle category information from loop-detectors for a weighted queue-length approach, to reduce emissions at signalized intersections. A multi-modal, case study of a real-world artery network with seven intersections, and 96 traffic signals, demonstrates the feasibility of the proposed method using a calibrated microsimulation model. Interestingly, the differentiation of vehicle categories at traffic lights not only enables reductions in emissions up to $9%$ but also improves traffic efficiency significantly ($5%$ reduction of total travel time) when compared with the (unweighted) Max-Pressure controller. This is achieved by systematic prioritization of transporters, trucks, and buses, at the cost of slightly larger delays for passenger cars and motorcycles. Ultimately, the proposed method has the potential to achieve more sustainable road traffic leveraging existing sensor infrastructure. Highway congestion leads to urban traffic diversion, increased emissions, and extended travel times. Even though ramp metering systems effectively reduce congestion, they often do face public opposition and lack acceptance due to inequitable delay distribution and ramp access among users. EQ-ALINEA, an extension to the ALINEA algorithm, balances both the fairness and efficiency aspects of ramp metering. EQ-ALINEA implements Utilitarian (total travel time), Rawlsian (maximum waiting times at on-ramps), and Egalitarian (dispersion of delays) fairness. Three boundary conditions prevent queue spill-backs, unacceptably long maximum waiting times, and ensure sufficient time for ramp dequeueing. A microsimulation-based case study on Barcelona’s metropolitan highway ring-road Ronda de Dalt showcases how EQ-ALINEA can effectively improve efficiency and fairness of highway traffic. The results show that more equitable transportation does not have to come at the cost of losses in efficiency or environmental impact. Besides democratizing the delay distribution over the user population (50% smaller Gini-coefficient) and significantly reducing maximum waiting times (by 40%), EQ-ALINEA redistributes highway accessibility to create more equal opportunities for all ramp users. Ultimately, the contribution of this work is to gain public acceptance for ramp metering by integrating fairness into the traffic control strategy.