Congestión urbana en América Latina y el Caribe: Características, costos y mitigación

Abstract

Este documento presenta el primer análisis exhaustivo para la región acerca las características y costos de la congestión urbana en América Latina y el Caribe, con resultados para las áreas metropolitanas de Bogotá (Colombia), Buenos Aires (Argentina), Ciudad de México (México), Lima (Perú), Montevideo (Uruguay), Río de Janeiro (Brasil), San Salvador (El Salvador), Santiago (Chile), Santo Domingo (República Dominicana) y Sao Paulo (Brasil). Utiliza big data y ciencia de datos para elucidar la dinámica de la congestión en cada ciudad y sus costos directos e indirectos para la sociedad. Con el fin de mitigar estos impactos, el documento reúne cinco grupos de medidas de política pública: (i) instrumentos de gestión de tráfico; (ii) políticas que restringen el uso del vehículo particular; (iii) políticas que promueven el uso del transporte público, el transporte activo y el transporte compartido; (iv) planificación integrada de la movilidad y el uso del suelo; y (v) políticas para la gestión de la logística urbana. Concluye con recomendaciones sobre las medidas más adecuadas y la secuenciación de las mismas para reducir la congestión en las grandes ciudades y las megaciudades de la región.

Using big data to estimate the impact of cruise activity on congestion in port cities

“…In the case of Buenos Aires, an exclusive highway for heavy traffic and long-distance buses was inaugurated in 2019, with the aim of easing congestion in a neuralgic area of the city that attracts business offices, the port-both cargo and passenger terminals-and the long-distance bus passenger terminal of Retiro. By separating light and heavy traffic, speed was significantly improved in the area (Calatayud et al 2021). The government is currently evaluating allowing tourist buses to use the highway during cruise peak season to further reduce urban congestion.…”

“…We applied the methodology developed by Calatayud et al (2021) to estimate congestion, based on the data provided by Waze. Here, congestion was defined as "the impedance vehicles impose on each other, due to the speed-flow relationship, in conditions where the use of a transport system approaches its capacity" (Goodwin 2004).…”

“…The information regarding road status was updated every 2 min. Given that Waze does not provide data on the number of vehicles at the jam, this difficulty was overcome following Calatayud et al (2021). First, the road network was built using a neural network model, allowing the number of lanes on each road to be calculated based on the information provided by the app, and some socioeconomic figures, such as gross domestic product (GDP) and population.…”

Understanding the Effect of Traffic Congestion on Accidents Using Big Data

“…In the United States, such costs amounted to as much as US $160 billion in 2015 in wasted time and fuel [4] and it was expected to grow steadily to US $186.2 billion by 2030 [2]. Recent studies focusing on developing countries showed that in 2019, direct congestion costs in Sao Paulo were equivalent to the annual amount that the city invested in healthcare, and twice the annual amount that Buenos Aires and Mexico City invested in education [5]. Mitigating congestion is therefore among the main goals of transportation policy [6].…”

“…Following Goodwin [34], we defined congestion as the impediment that vehicles impose on each other, due to the speed-flow relationship, in conditions where the use of the transport system approaches its capacity limit. We estimated aggregate delay for the selected cities according to the methodology developed by Calatayud et al [5], who used a two-phased methodology that included a neural network model to build the road network in each city; and a model to calculate delay based on the characteristics of Waze data.…”