Real scenario and simulations on GLOSA traffic light system for reduced CO2 emissions, waiting time and travel time

Cooperative ITS is enabling vehicles to communicate with the infrastructure to provide improvements in traffic control. A promising approach consists in anticipating the road profile and the upcoming dynamic events like traffic lights. This topic has been addressed in the French public project Co-Drive through functions developed by Valeo named Green Light Optimal Speed Advisor (GLOSA). The system advises the optimal speed to pass the next traffic light without stopping. This paper presents results of its performance in different scenarios through simulations and real driving measurements. A scaling is done in an urban area, with different penetration rates in vehicle and infrastructure equipment for vehicular communication. Our simulation results indicate that GLOSA can reduce CO2 emissions, waiting time and travel time, both in experimental conditions and in real traffic conditions.

💡 Research Summary

The paper presents a comprehensive evaluation of the Green Light Optimal Speed Advisor (GLOSA), a cooperative ITS function developed by Valeo within the French public project Co‑Drive. GLOSA leverages vehicle‑to‑infrastructure (V2I) communication to receive real‑time signal phase and timing (SPaT) data from traffic lights. Using the vehicle’s current speed, distance to the intersection, and the remaining time of the green phase, the system computes an “optimal speed” that allows the driver to pass the next signal without stopping. The algorithm essentially solves v_opt = D / T_remain, then adjusts the result to respect speed limits, acceleration/deceleration capabilities, and driver comfort constraints.

Two complementary experimental approaches are adopted. First, a field trial was conducted on a 5 km urban corridor near Paris. Two passenger cars equipped with the Valeo GLOSA unit exchanged DSRC (IEEE 802.11p) messages with upgraded traffic lights broadcasting SPaT every second. On‑board diagnostics (OBD‑II) recorded instantaneous fuel consumption, while a portable gas analyzer measured CO₂ emissions. GPS data provided stop‑and‑wait times and total travel time. Compared with a baseline without GLOSA, the trial showed a 7.3 % reduction in fuel use, a 6.9 % cut in CO₂ output, a 12 % decrease in waiting time at intersections, and an overall travel‑time improvement of roughly 5 %.

Second, a microscopic traffic simulation using SUMO recreated the same corridor as a digital twin. The model incorporated realistic vehicle dynamics (max acceleration 2.5 m/s², max deceleration –4.5 m/s²) and a typical passenger‑car fuel consumption map. Traffic demand was set at 800 veh/h per lane, close to saturation, and the signal plan was fixed at a 90‑second cycle (30 s green, 5 s amber, 55 s red). Three penetration scenarios were explored: (a) only vehicles equipped with GLOSA, (b) only traffic lights upgraded, and (c) both sides equipped. Penetration rates were varied from 0 % to 100 % in 10 % increments, allowing the authors to quantify network‑wide effects.

Simulation results corroborated the field findings and revealed additional insights. Even at a modest 30 % vehicle penetration, CO₂ emissions fell by 4–5 %; at 70 % penetration the reduction exceeded 9 %. Waiting time at intersections dropped sharply once penetration surpassed 50 %, and the average network travel time decreased by about 8 % when both vehicles and infrastructure were equipped. Notably, the number of complete stops at lights fell by 45 % in the fully equipped scenario, indicating that GLOSA effectively creates a “green wave” that smooths traffic flow and eliminates the energy‑wasting acceleration‑deceleration cycles typical of conventional stop‑and‑go driving.

The authors also discuss limitations and future work. The current implementation assumes a fixed‑cycle signal plan; integrating GLOSA with adaptive signal control (e.g., SCOOT, SCATS) would require more sophisticated prediction of variable green windows. Communication latency and packet loss above 5 % degrade the accuracy of the speed recommendation, suggesting a need for robust error‑handling and possibly redundancy through vehicle‑to‑vehicle (V2V) sharing of SPaT data. During early deployment, mixed traffic (GLOSA‑enabled and legacy vehicles) may experience uneven speed adjustments, potentially creating new safety concerns. To address these challenges, the paper proposes a multi‑layer control architecture that combines V2I, V2V, and centralized traffic‑management algorithms.

In conclusion, the study demonstrates that GLOSA can deliver measurable environmental benefits (CO₂ reduction), operational gains (shorter waiting and travel times), and can do so even at relatively low market penetration. The authors plan to extend the research to city‑wide pilots, evaluate interactions with electric and autonomous vehicles, and explore integration with dynamic traffic‑signal optimization platforms to further enhance the sustainability and efficiency of urban mobility.