No abstract

Fixed-rail metro (or 'subway') infrastructure is generally unable to provide access to all parts of the city grid. Consequently, feeder bus lines are an integral component of urban mass transit systems. While passengers prefer a seamless transfer between these two distinct transportation services, each service's operations are subject to a different set of factors that contribute to metro-bus transfer delay. Previous attempts to understand transfer delay were limited by the availability of tools to measure the time and cost associated with passengers' transfer experience. This paper uses data from smart card systems, an emerging technology that automatically collects passenger trip data, to understand transfer delay. The primary objective of this study is to use smart card data to derive a reproducible methodology that isolates high priority transfer points between the metro system and its feeder-bus systems. The paper outlines a methodology to identify transfer transactions in the smart card dataset, estimate bus headways without the aid of geographic location information, estimate three components of the total transfer time (walking time, waiting time, and delay time), and isolate high-priority transfer pairs. The paper uses smart card data from Nanjing, China as a case study. The results isolate eight high priority metro-bus transfer pairs in the Nanjing metro system and finally, offers several targeted measures to improve transfer efficiency.

This research generated new knowledge in the demographic and socioeconomic characteristics of airport-adjacent communities to better understand patterns of exposure to the negative externalities of hub airports over time. The research asked the following question: How has the population of historically marginalized groups living near airports changed with the rise of the jet age? The spatial analysis and descriptive statistics showed that airport-adjacent communities in multiairport regions generally have increased numbers of persons of color and increased numbers of renters compared with their respective metropolitan regions. In addition, the communities often underperform socioeconomically with respect to their region. The study also tested three theories from the literature to explain the relationship between airport infrastructure and the airport’s surrounding communities: the “power to resist” effect, the “push–pull locally unwanted land use” effect, and the “airport-centric activity center” effect.

Justifications for enhancements to airport capacity are often framed in relation to flight delay reductions, but improvements to flight predictability also offer substantial benefits to the health of the aviation system. “Predictability” is defined in this paper as block time adherence and is measured as the difference between scheduled and actual block time. This research, using historical data, quantifies the impact on flight predictability of one airport's enhancement of infrastructure capacity. A case study using statistical methodologies, including cluster analysis of national airspace days and quantile regression of flights, identifies how deployment of a fifth runway at Hartsfield–Jackson Atlanta International Airport in Georgia affected the predictability of flight arrivals. The analysis identifies four scenarios—defined according to the level of national airspace strain and terminal airspace weather disruption—for which inclusion of the fifth runway in the runway configuration is associated with either improvement or degradation in predictability. If broad gains are to be made in improving predictability for the national airspace, then capacity enhancements may offer a limited contribution to what must be a multifaceted solution.

The current institutional process for project-level environmental review, the government-required Environmental Impact Statement (EIS), requires assessment of the proposed project, the no-build alternative, and alternatives to the proposed project. Despite growing academic research to compare the environmental impacts of air and high-speed rail (HSR) infrastructure, there are few instances of multimodal alternatives analysis in airport and HSR EIS documents. In this paper, examples of EISs for air and HSR capacity-enhancement projects are chronicled to identify key challenges to completing modal alternative analysis in the EIS: the spatial heterogeneity of the physical infrastructure for air and HSR, the framing of EIS purpose and need statements, and the complicated interpretations of environmental impact significance thresholds. The paper concludes by proposing strategies to incentivize modal alternative assessments and highlight methods that are needed to perform high-quality comparative analysis to inform decision makers, whether in the context of the EIS or in upstream planning processes.