Assessing Neighborhood-scale Traffic from Crowd-sensed Traffic Data: Findings from an Environmental Justice Community in New York City

Beyond traffic jam alleviation: evaluating the health and health equity impacts of New York City's congestion pricing plan

New York City (NYC) is slated to be the first jurisdiction in the USA to implement a cordon-based congestion tax, which will be levied on vehicles entering its Central Business District. Several cities around the world, for example, London and Stockholm, have had similar cordon-based pricing programmes, defined as road pricing that charges drivers a fee for entering a specified area (typically a congested urban centre). In addition to reducing congestion and creating revenue, projections suggest the NYC congestion pricing plan may yield meaningful traffic-related air quality improvements that could result in health benefits. NYC is a large city with high air pollution and substantial racial/ethnic and socioeconomic health inequities. The distinct geography and meteorological conditions of the city also suggest that the policy's impact on air quality may extend beyond the NYC metropolitan area. As such, the potential breadth, directionality and magnitude of health impacts on communities who might be heavily affected by the nation's first congestion pricing plan should be empirically investigated. We briefly review evaluation studies of other cordon-based congestion pricing policies and argue that implementation of this policy provides an excellent opportunity to employ a quasi-experimental study design to evaluate the policy's impacts on air quality and health outcomes across population subgroups using a health equity lens. We discuss why real-time evaluations of the NYC congestion pricing plan can potentially help optimise benefits for communities historically negatively affected by traffic-related air pollution. Assessing intended and unintended impacts on health equity is key to achieving these goals.

Spatial Differentiation Effect of Rural Logistics in Urban Agglomerations in China Based on the Fuzzy Neural Network

Rural logistics is particularly important for the sustainable development of Chinese urban agglomeration, which features the coexistence of urban circles and vast countryside. However, the existing literature generally focuses on urban logistics while ignoring rural logistics. Taking the Chengdu-Chongqing agglomeration as an example, this study constructed a rural logistics index system and proposed a new dynamic assessment model for rural logistics development using a fuzzy neural network, Moran index, and Kernel density estimation. The results are as follows: the development of rural logistics has been enhanced, and gaps among cities have gradually narrowed over the past decade. In particular, the spatial distribution of rural logistics showcases a dual-core structure in the Chengdu-Chongqing agglomeration, which is different from the unipolar structure manifested in other urban agglomerations. Because of administrative barriers, the impact from the dual-core cities is very different: Chengdu has a significant spillover effect on its surrounding cities, which is not the case for Chongqing. The findings are of great significance for local governments to provide decision-making support for the sustainable development of urban agglomerations.