Examinando por Autor "Blazquez, Carola"
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Ítem An Instance-Specific Parameter Tuning Approach Using Fuzzy Logic for a Post-Processing Topological Map-Matching Algorithm(Institute of Electrical and Electronics Engineers, 2018) Blazquez, Carola; Ries, Jana; Miranda, Pablo Andres; Leon, Roberto JesusMap Matching Algorithms (MMAs) are developed to solve spatial ambiguities that arise in the process of assigning GPS measurements onto a digital roadway network. Scarce systematic parameter tuning approaches exist in the literature for optimizing MMA performance. Thus, a novel framework is proposed for a systematic calibration of the parameters of a post-processing MMA. The calibration approach consists of an Instance-specific Parameter Tuning Strategy (IPTS) that employs Fuzzy Logic principles. The proposed fuzzy IPTS tool determines algorithm-specific parameter values based on instance-specific information a priori to the execution of the MMA. Finally, the proposed IPTS tool is able to adjust to two particular decision maker preferences on algorithm performance, namely solution quality and computational time. © 2009-2012 IEEE.Ítem Differential impacts of ridesharing on alcohol-related crashes by socioeconomic municipalities: rate of technology adoption matters(BioMed Central Ltd, 2021-12) Blazquez, Carola; Laurent, José Guillermo Cedeño; Nazif-Munoz, José IgnacioBackground: An emergent group of studies have examined the extent under which ridesharing may decrease alcohol-related crashes in countries such as United States, United Kingdom, Brazil, and Chile. Virtually all existent studies have assumed that ridesharing is equally distributed across socioeconomic groups, potentially masking differences across them. We contribute to this literature by studying how socioeconomic status at the municipal level impacts Uber’s effect on alcohol-related crashes. Methods: We use data provided by Chile’s Road Safety Commission considering all alcohol-related crashes, and fatal and severe alcohol-related injuries that occurred between January 2013 and September 2013 (before Uber) and January and September 2014 (with Uber) in Santiago. We first apply spatial autocorrelation techniques to examine the level of spatial dependence between the location of alcohol-related crashes with and without Uber. We then apply random-effects meta-analysis to obtain risk ratios of alcohol-related crashes by considering socioeconomic municipality differences before and after the introduction of Uber. Results: In both analyses, we find that the first 9 months of Uber in Santiago is associated with significant rate ratio decreases (RR = 0.71 [95% Confidence Interval (C.I.) 0.56, 0.89]) in high socioeconomic municipalities in all alcohol-related crashes and null (RR = 1.10 [95% C.I. 0.97, 1.23]) increases in low socioeconomic municipalities. No concomitant associations were observed in fatal alcohol-related crashes regardless of the socioeconomic municipality group. Conclusions: One interpretation for the decline in alcohol-related crashes in high socioeconomic municipalities is that Uber may be a substitute form of transport for those individuals who have access to credit cards, and thus, could afford to pay for this service at the time they have consumed alcohol. Slight increases of alcohol-related crashes in low socioeconomic municipalities should be studied further since this could be related to different phenomena such as increases in alcohol sales and consumption, less access to the provision of public transport services in these jurisdictions, or biases in police reports. © 2021, The Author(s).Ítem Global and local spatial autocorrelation of motorcycle crashes in Chile(SciTePress, 2019) Blazquez, Carola; Fuentes, María JoséIn Chile, the usage of motorcycles as a mode of transport is growing in unison with the number of crashes that have arisen in recent years. Spatial statistical methods were used in this study to determine whether motorcycle crashes showed spatial clustering over time from a global and local perspective. The global spatial autocorrelation results indicate that high intensity clusters of collisions at intersections with traffic signals and curved road sections resulting in fatalities persisted during the five-year study period. Locally, recurrent high spatial patterns of motorcycle collisions arose along straight road sections and on sunny days due to the loss of control of the vehicle, or the imprudence of the driver or pedestrian. Communes located in the centre zone of Chile, particularly in the city of Santiago and the surrounding areas, presented a large number of highly clustered crash attributes. The findings of this study may help authorities to target efforts towards policy measures to improve motorcycle safety in Chile. © 2019 by SCITEPRESS - Science and Technology Publications, Lda.Ítem Use of data imputation tools to reconstruct incomplete air quality datasets: A case-study in Temuco, Chile(Atmospheric Environment, 2019-03-01) Quinteros, María Elisa; Luc, Siyao; Blazquez, Carola; Cárdenas-Re, Juan Pablo; Ossaf, Ximena; Delgado-Saboritg, Juana-María; Harrisong, Roy M.; Ruiz-Rudolphl, PabloMissing data from air quality datasets is a common problem, but is much more severe in small cities or localities. This poses a great challenge for environmental epidemiology as high exposures to pollutants worldwide occur in these settings and gaps in datasets hinder health studies that could later inform local and international policies. Here, we propose the use of imputation methods as a tool to reconstruct air quality datasets and have applied this approach to an air quality dataset in Temuco, a mid-size city in Chile as a case-study. We attempted to reconstruct the database comparing five approaches: mean imputation, conditional mean imputation, K-Nearest Neighbor imputation, multiple imputation and Bayesian Principal Component Analysis imputation. As a base for the imputation methods, linear regression models were fitted for PM2.5 against other air quality and meteorological variables. Methods were challenged against validation sets where data was removed artificially. Imputation methods were able to reconstruct the dataset with good performance in terms of completeness, errors, and bias, even when challenged against the validations sets. The performance improved when including covariates from a second monitoring station in Temuco. K-Nearest Neighbor imputation showed slightly better performance than multiple imputation for error (25% vs. 27%) and bias (2.1% vs. 3.9%), but presented lower completeness (70% vs. 100%). In summary, our results show that the imputation methods can be a useful tool in reconstructing air quality datasets in a real-life situation.