Understanding how to reduce road transport emissions : modelling the impact of eco-driving

Castro, Alvaro García · 2017 · Crossref

DOI: 10.20868/upm.thesis.40634

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Summary

This doctoral thesis investigates the impact of eco-driving on road transport emissions, addressing the broader challenge of reducing greenhouse gases and pollutants in metropolitan areas. While eco-driving is known to yield individual fuel savings, there is limited understanding of its systemic effects on traffic flow and surrounding vehicles, particularly regarding varying penetration rates. The study aims to quantify how Information and Communication Technology (ICT) measures, specifically eco-driving, influence vehicle dynamics, congestion, and aggregate emissions. The research employs a two-phase methodology centered on the Madrid metropolitan area. First, a data collection campaign using floating vehicles evaluated four ICT measures: section speed control, variable speed limits, cruise control, and eco-driving. This empirical data informed the calibration of a traffic micro-simulation tool. Second, the study focused on eco-driving by constructing four urban road models: urban motorway, urban arterial, urban collector, and local street. Both base-case scenarios and eco-driving parameters were calibrated using the collected floating vehicle data. The researchers simulated 72 scenarios, varying road type, traffic demand, and the percentage of eco-drivers. Emissions of CO2 and NOx were estimated using a microscopic emission model linked to the simulation outputs. The results reveal a counterintuitive finding: in scenarios with high traffic demand and a high percentage of eco-drivers, total emissions increase. This occurs because eco-driving behaviors, characterized by smoother acceleration and deceleration and larger headways, reduce traffic throughput and increase congestion. The resulting delays lead to higher global emissions despite individual efficiency gains. The study further analyzed fuel consumption as a function of delay, identifying that congestion indicators significantly explain fuel consumption variations. Factor analysis and multiple regression models were used to identify key explanatory variables for fuel consumption, confirming the strong link between delay rates and energy use. The significance of this work lies in challenging the assumption that widespread eco-driving adoption automatically reduces urban emissions. It highlights the complex interaction between individual driving behavior and network-level traffic dynamics. The findings suggest that while eco-driving benefits individual drivers, it may exacerbate congestion and emissions at the network level under high-demand conditions. This implies that traffic management strategies must account for these systemic effects, potentially combining eco-driving with other demand management or infrastructure improvements to achieve net emission reductions. The thesis provides a robust framework for evaluating ICT-based traffic measures, offering insights for policymakers and urban planners aiming to mitigate climate change and air quality issues through transportation interventions.

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• eco driving