arXiv subjects

Cinzia Cirillo

Publications and source records attributed to Cinzia Cirillo.

3 recordsLinked to original sources

arXiv2026

Fare-Free Bus Service and CO2 Reductions: Evidence from a Natural Experiment

We devise a difference-in-difference study design to assess the impact of fare-free bus service in Alexandria, located in the Washington, DC metro area. Our surveys show modest to no effect, with at most 6% more residents in Alexandria increasing their bus usage compared to control locations. We find no effect on ground-level ozone or road crashes, suggesting little to no impact on road traffic. One-third of respondents in control locations indicated they would use buses more frequently if fare-free service were available in their areas. Based on the respondent-reported reductions in car miles, the program led to a reduction of 0.294 to 0.494 tons of CO2 per year, or 5% to 9% of the average annual emissions from a US car, at a cost of $70-$120 per ton of CO2. We predict a CO2 reduction of 0.454 tons per year, equivalent to 8% of the average US car's annual emissions if the fare-free bus covered all of the study areas.

econ.EM↗

arXiv2024

Integrating En Route and Home Proximity in EV Charging Accessibility: A Spatial Analysis in the Washington Metropolitan Area

This study evaluates the accessibility of public EV charging stations in the Washington metropolitan area using a comprehensive measure that accounts for both destination-based and en route charging opportunities. By incorporating the full spectrum of daily travel patterns into the accessibility evaluation, our methodology offers a more realistic measure of charging opportunities than destination-based methods that prioritize proximity to residential locations. Results from spatial autocorrelation analysis indicate that conventional accessibility assessments often overestimate the availability of infrastructure in central urban areas and underestimate it in peripheral commuting zones, potentially leading to misallocated resources. By highlighting significant clusters of high-access and low-access areas, our approach identifies spatial inequalities in infrastructure distribution and provides insights into areas requiring targeted interventions. This study underscores the importance of incorporating daily mobility patterns into urban planning to ensure equitable access to EV charging infrastructure and suggests a framework that other regions could adopt to enhance sustainable transportation networks and support equitable urban development.

physics.soc-ph↗

arXiv2024

Copula-based transferable models for synthetic population generation

Population synthesis involves generating synthetic yet realistic representations of a target population of micro-agents for behavioral modeling and simulation. Traditional methods, often reliant on target population samples, such as census data or travel surveys, face limitations due to high costs and small sample sizes, particularly at smaller geographical scales. We propose a novel framework based on copulas to generate synthetic data for target populations where only empirical marginal distributions are known. This method utilizes samples from different populations with similar marginal dependencies, introduces a spatial component into population synthesis, and considers various information sources for more realistic generators. Concretely, the process involves normalizing the data and treating it as realizations of a given copula, and then training a generative model before incorporating the information on the marginals of the target population. Utilizing American Community Survey data, we assess our framework's performance through standardized root mean squared error (SRMSE) and so-called sampled zeros. We focus on its capacity to transfer a model learned from one population to another. Our experiments include transfer tests between regions at the same geographical level as well as to lower geographical levels, hence evaluating the framework's adaptability in varied spatial contexts. We compare Bayesian Networks, Variational Autoencoders, and Generative Adversarial Networks, both individually and combined with our copula framework. Results show that the copula enhances machine learning methods in matching the marginals of the reference data. Furthermore, it consistently surpasses Iterative Proportional Fitting in terms of SRMSE in the transferability experiments, while introducing unique observations not found in the original training sample.

stat.ML↗