Exploring the impact of automated vehicles lane-changing behavior on urban network efficiency

Pelizza, Alberto; Orsini, Federico; Yilmaz-Niewerth, Sefa; Rossi, Riccardo; Friedrich, Bernhard · 2023 · OpenAlex-citations

DOI: 10.1109/mt-its56129.2023.10241492

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Summary

This study investigates the isolated impact of automated vehicle (AV) lane-changing behavior on urban network efficiency, addressing a gap in existing literature that typically conflates longitudinal and lateral control effects. The authors hypothesize that AVs exhibit more prudent lane-changing behaviors than conventional vehicles (CVs), potentially reducing network efficiency. To isolate this effect, the study models AVs with automated lateral control but retains conventional longitudinal control characteristics. The research employs the SUMO microsimulation software to model an urban road network in the Vahrenwald-List neighborhood of Hannover, Germany. The network comprises 117.45 km of roads and 84 intersections. AV lane-changing parameters were calibrated using real-world data from Mintsis et al., specifically adjusting the `lcAssertive` parameter to reflect more conservative gap acceptance, alongside adjustments to `lcStrategic`, `lcSpeedGain`, and `lcKeepRight`. The study analyzed eleven scenarios, increasing AV penetration rates from 0% to 100% in 10% increments. Simulations utilized both real-world traffic demand profiles and artificially inflated profiles to assess performance under normal and congested conditions. Results indicate that as AV penetration increases, the total number of lane-changing maneuvers decreases significantly, with AVs performing proportionally fewer maneuvers than CVs due to their precautionary behavior. This shift leads to a statistically significant, albeit modest, decline in system performance. In the 100% AV scenario, mean travel times increased by 7.35%, and average speeds decreased by 6.17% compared to the baseline. Furthermore, macroscopic fundamental diagram analysis revealed a 3.65% reduction in maximum network flow capacity at the critical point. These findings contrast with previous studies that reported efficiency gains, suggesting that those improvements were likely driven by automated longitudinal control rather than lateral behavior. The study concludes that automated lane-changing behavior, characterized by larger gap acceptance and reduced maneuver frequency, negatively impacts urban traffic efficiency. The authors argue that while the efficiency loss is modest, it highlights the need for further development and testing of automated lane-changing algorithms. Future research should explore whether potential safety or emission benefits could offset these efficiency losses and should validate these findings across different network contexts and simulation platforms.

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