Archivo de la etiqueta: Bogotá

El Gobierno Petro tendrá que girar $28,5 billones al FEPC

El Gobierno Nacional, a través del Ministerio de Hacienda en el Marco Fiscal de Mediano Plazo, reveló cuál será el desembolso de los próximos años para cubrir el déficit que tiene el Fondo de Estabilización de Precios de los Combustibles (Fepc).

Al cierre de este año, se espera un déficit del Fepc entre $31 y $34 billones, cifra que representa 2,7% del PIB, según datos del Comité Autónomo de la Regla Fiscal (Carf). De esta manera, el nuevo Gobierno tendría que girar $28,5 billones al Fepc para pagar sus deudas y cubrir el déficit entre los años 2023 y 2025.

“No es suficiente la subida de los precios. Se requieren muchos meses o años de subidas para poder cerrar la brecha entre el precio doméstico y el precio internacional” @SergioCabrales experto de @Uniandes

https://www.larepublica.co/economia/el-nuevo-gobierno-tendra-que-girar-28-5-billones-al-fepc-para-mitigar-el-deficit-3388623

El precio de la gasolina y el diésel se mantendría igual para el quinto mes de este año

La expectativa sobre cuáles podrían ser las variaciones en los precios de los combustibles en el país sigue latente para este mes de mayo, que ya completa cinco días de haber iniciado sin anuncios de variaciones por parte del Ministerio de Minas y Energía.

“Aunque los precios deberían estar más altos basados en los precios internacionales, un aumento significativo en los combustibles tendría un efecto en la inflación (8,53%) que no es lo deseable en estos momentos, por lo que no se esperan aumentos en los precios”, resaltó Sergio Cabrales, experto de la Universidad de Los Andes

https://www.larepublica.co/economia/el-precio-de-la-gasolina-y-el-diesel-se-mantendria-igual-para-el-quinto-mes-del-ano-3357087

Level of traffic stress-based classification: A clustering approach for Bogotá, Colombia

The Level of Traffic Stress (LTS) is an indicator that quantifies the stress experienced by a cyclist on the segments of a road network. We propose an LTS-based classification with two components: a clustering component and an interpretative component. Our methodology is comprised of four steps: (i) compilation of a set of variables for road segments, (ii) generation of clusters of segments within a subset of the road network, (iii) classification of all segments of the road network into these clusters using a predictive model, and (iv) assignment of an LTS category to each cluster. At the core of the methodology, we couple a classifier (unsupervised clustering algorithm) with a predictive model (multinomial logistic regression) to make our approach scalable to massive data sets. Our methodology is a useful tool for policy-making, as it identifies suitable areas for interventions; and can estimate their impact on the LTS classification, according to probable changes to the input variables (e.g., traffic density). We applied our methodology on the road network of Bogotá, Colombia, a city with a history of implementing innovative policies to promote biking. To classify road segments, we combined government data with open-access repositories using geographic information systems (GIS). Comparing our LTS classification with city reports, we found that the number of bicyclists’ fatal and non-fatal collisions per kilometer is positively correlated with higher LTS. Finally, to support policy making, we developed a web-enabled dashboard to visualize and analyze the LTS classification and its underlying variables.

Bicycle safety in Bogotá: A seven-year analysis of bicyclists’ collisions and fatalities

Road safety research in low- and middle-income countries is limited, even though ninety percent of global road traffic fatalities are concentrated in these locations. In Colombia, road traffic injuries are the second leading source of mortality by external causes and constitute a significant public health concern in the city of Bogotá. Bogotá is among the top 10 most bike-friendly cities in the world. However, bicyclists are one of the most vulnerable road-users in the city. Therefore, assessing the pattern of mortality and understanding the variables affecting the outcome of bicyclists’ collisions in Bogotá is crucial to guide policies aimed at improving safety conditions. This study aims to determine the spatiotemporal trends in fatal and nonfatal collision rates and to identify the individual and contextual factors associated with fatal outcomes. We use confidence intervals, geo-statistics, and generalized additive mixed models (GAMM) corrected for spatial correlation.