Search

Following airline mergers and network reorganizations aimed at reducing operational costs, consolidated air services at large hub airports have encouraged air travelers to forego use of their smaller local airports to access large hub airports offering superior air services farther away. This study investigates airport leakage in areas of Wisconsin and Michigan served by small airports, where air travelers may leak to neighboring large hubs. Using a proximity-based service area definition, three airports experiencing leakage are identified, and a hierarchical logit airport choice model is applied that accounts for air service characteristics and access distance for travelers coming from these airports’ service areas. Results show that a similar mean number of flight legs at both the local and substitute (large hub) airports will encourage leakage at Dane County Regional and Gerald R. Ford International airports, indicating that adding direct flights alone will not be sufficient to combat leakage. Comparable access distances to local and substitute airports have opposite effects on the local markets of Gerald R. Ford International and Milwaukee Mitchell International airports—promoting leakage at the former but discouraging it at the latter. Furthermore, proportional increases in airfares at local airports lead to uneven losses of markets in investigated service areas. Overall, the study provides empirical evidence of long-distance airport leakage in parts of the U.S. Midwest, and how its implications can be used by small airports seeking to further understand and respond to travelers’ airport choices within their local markets.

Laboratory studies show that bisphenol A (BPA) leaches from bisphenol A-glycidyl methacrylate (bisGMA)-based dental materials. We aimed to quantify the extent to which children are exposed to BPA from dental treatment with bisGMA materials, by amount of treatment and type of sedation. We hypothesized that posttreatment urinary BPA (uBPA) concentrations would be higher among patients with more surfaces treated with bisGMA-based materials and among patients receiving general anesthesia compared with pretreatment concentrations. We conducted a prospective cohort study in 211 children, 4 to 12 y old, who had no prior resin-based dental treatment. We measured uBPA concentrations twice before treatment and at 2 d and 1, 4, and 16 wk posttreatment. We abstracted treatment data (surfaces treated) from the chart. We generated descriptive statistics and compared pre- and posttreatment uBPA concentrations using generalized estimating equations. Participants were 51% female, 46% non-White, and 74% publicly insured. The median age was 6 y. The mean number of tooth surfaces exposed to BisGMA materials (composites/sealants) was 7.5 (SD 5.3). Overall, uBPA concentrations were 86% higher (95% confidence interval [CI] 42% to 143%, P < 0.001) at 2 d posttreatment compared with pretreatment concentrations. The uBPA concentrations 2 d posttreatment versus pretreatment tended to be higher (112%, 95% CI 53% to 194%) among those receiving treatment on >4 surfaces than those receiving treatment on ≤4 surfaces (50%, 95% CI −2% to 130%). Two days after treatment, uBPA was significantly higher than pretreatment concentrations in children receiving nitrous oxide but not in those receiving general anesthesia. Among all findings, uBPA concentrations returned to baseline by 4 wk. Children experience short-term increases in BPA from dental treatment. The impact of relatively high, short-term BPA exposure on child health is unknown. Given the widespread use of BisGMA-based dental materials and that chronic low-dose BPA exposure may adversely affect child health, strategies that minimize BPA exposure could potentially improve child health.

This paper explores the application of count models to represent the relationship between flight disruptions and weather. Throughout the world, flights are regularly disrupted by delays at airports and in the terminal airspace, and less frequently by diversions and cancelations. Many delay studies have been conducted for large American and European airports, in part due to the availability of high-quality data. However, such high-quality data is not as readily available for other airports throughout the world. In this study, excess-zero count models are built using a publicly available dataset for Iqaluit Airport (YFB) in Northern Canada, to determine the influence of different weather components on disruption counts. Visibility and crosswind speeds are shown to have the largest influence on flight disruptions. The models are also applied using Aviation System Performance Metrics (ASPM) flight data for Anchorage Airport (ANC) in Alaska; the data is systematically degraded to match completeness of the Iqaluit data to test the models. The results verify that an excess-zero model using incomplete data yields results similar to that of a count model with complete data, demonstrating that an excess-zero model can overcome data incompleteness to yield acceptable results. Although count models have been applied extensively in the transportation literature, the authors believe this to be the first application to flight disruptions, and the first quantitative model of operations at a northern Canadian airport. This paper demonstrates that challenges in data availability—the case for most airports throughout the world—can be addressed with novel statistical modeling applications, and thus, delay studies can be conducted for almost any airport.

This study introduces a novel method of merging disparate but complementary datasets and applying machine learning techniques to ground delay program (GDP) data. More specifically, it aims to characterize GDPs with respect to changing weather forecasts, GDP plan parameters, and operational performance. The analysis aims to gain insights into GDP usage patterns (implementation and revisions), with respect to these key dimensions. It also aims to gain insights into how GDP cancelations and revisions correlate with operational efficiency and predictability. The results could be used to help traffic managers and air carriers understand complex patterns in the evolution of GDPs, so that they might, for example, better anticipate or even plan a response to a change in weather conditions. The focus is on GDPs at Newark Liberty International Airport (EWR), from 2010 through 2014. A master dataset was generated by merging several datasets on GDPs, weather forecasts, and individual flight information. Several scenarios of GDP evolution were then identified by reducing the dimensionality of the master GDP dataset, then applying cluster analysis on the lower dimensional data. It was found that GDPs at EWR can be categorized into 10 types based on weather forecasts, realized weather, GDP scope, arrival rates, and duration. The characteristics of these 10 GDP clusters were further explored by examining the relationships between GDP scenarios and their performance. It was found that GDPs under stable, low-severity weather and with large scope may score higher on the efficiency metric than expected. When GDPs called in the same weather conditions have high program rates, medium durations, and narrow scopes, capacity utilization was higher than expected—less affected flights lead to fewer cancelations and more arrivals (albeit delayed), and therefore, higher capacity utilization. Results also suggest that program rates are set more conservatively than needed for some poor weather conditions that end earlier than expected. GDPs with fewer revisions were associated with a higher predictability score but lower efficiency score. These findings can provide greater insights and knowledge about GDPs for future planning purposes. More specifically, the findings could, for example, be used to support discussion around, or even future guidance regarding, how to set and adjust GDP program rates. In future work additional data could be utilized to provide a more comprehensive operational picture of GDPs, and a wider range of performance metrics could be considered. It is also recommended that the patterns of how GDPs evolve over their lifetimes be further explored using other machine learning techniques that may provide new and useful insights.

The safety of locations operating under high-speed conditions could significantly differ from that of locations operating under low-speed conditions. Therefore, different approaches must be adopted when speed and safety are analyzed and managed at locations operating under different regimes. However, it is necessary first to understand the factors affecting the speed–collision classification of a site. Locations operating under high speeds are typically expected to have more collisions compared with locations in which speeds are low. Some locations, however, might experience a high collision rate even when speeds are low, or vice versa. This study aimed to identify the factors that affected the site classification into any of those categories by using data collected on roads in Edmonton, Alberta, Canada. Locations were divided into four speed–collision bins (high collision, high speed; high collision, low speed; low collision, high speed; low collision, low speed), and geographic information system maps of locations were produced to explore the spatial distribution of those locations. Moreover, logistic regression was used to understand the role of different factors in identifying the speed–collision bin to which a certain location belonged. The results reveal that locations with high collision rates but low speeds have a relatively high population of heavy vehicles and trucks as well as high speed variability. As for locations with low collision rates and high speeds, these sites were found to have a high level of protection through the presence of medians and shoulders with relatively low access density.

The May 2016 wildfire in Fort McMurray in northern Alberta, Canada—the costliest wildfire disaster in Canadian history—led to an areawide evacuation by road and air. Traffic count and flight data were used to assess the characteristics of the evacuation, including estimates of people movements by vehicle and aircraft. The vehicle counts were compared first with historic values to examine traffic patterns and were then used to create an evacuation response curve, which revealed an expected S-shaped curve and highlighted how quickly the evacuation occurred. Finally, data for people evacuated by aircraft were combined with data for people evacuated by ground vehicle to construct a curve of the cumulative number of evacuees leaving the region. This study identified several key implications for evacuation planning and operations. The decision to evacuate residents to temporary shelters in the north was instrumental in the quick removal of everyone from immediate danger via all possible exits. Although an unplanned contraflow operation added roadway capacity out of Fort McMurray, the underuse of the secondary route suggested that the management of traffic routing might have reduced congestion. The evacuation response curve emphasized the volatility of the wildfires, with the resulting evacuations occurring under conditions of a greater immediacy than hurricane evacuations. Finally, the significant role of air transportation in this evacuation indicated that multimodal emergency evacuation plans may be critical for remote communities and sparse networks. These findings may be applied to evacuation planning and policy to improve the efficiency and efficacy of evacuations.

This paper presents a method for obtaining airfield capacity estimates using historical data from FAA's Aviation System Performance Metrics (ASPM) database. The process first involves merging individual flights and quarter-hour airport runway operations data sets from ASPM to create a new data set. Data for Newark Liberty International Airport (EWR) in New Jersey and San Diego International Airport in California from 2006 to 2011 were used. Then, filters for meteorological condition, runway configuration, called rates, and fleet mix were applied to the two airport data sets. The filtered data sets were then used in a censored regression model of capacity that included queue length (number of aircraft waiting to arrive or depart) and arrival–departure throughput count splits as independent variables. These attributes were found to affect airfield capacity at statistically significant levels, and parameters had expected signs and magnitudes. Additionally, capacities under ideal conditions were found to be reasonably close to other sources. The model also confirmed that average capacities at EWR during hours when a ground delay program (GDP) was running were lower than when there was no GDP in effect. The method described in this paper could be used to more precisely quantify airfield capacities in specific conditions of particular interest to air traffic controllers and airport operators to better facilitate decisions that rely heavily on a good understanding of capacity in these conditions. The data exploration and preparation undertaken as part of the study reveal some of the finer points of the ASPM data and how they can be used in a more meaningful way for airfield capacity estimation.

Speeding is a leading factor in road collisions and is found to contribute to approximately one-third of all fatal collisions. Speed enforcement is one of the most common countermeasures used to reduce speed. However, a gap exists in the literature regarding the effectiveness of automated mobile photo enforcement on urban arterial roads. This study addresses this gap using the before-and-after empirical Bayes method to account for regression to the mean effects and other confounding factors. Locally developed safety performance functions and yearly calibration factors for different collision severities were obtained by using a reference group of urban arterial roads. The evaluation period covers 8 years. Collision records, deployment information, traffic counts, and road geometric data were collected. The results showed consistent reductions in different collision severities; the reductions ranged from 14% to 20%, with the highest reductions observed for severe collisions. The enforced segments were further categorized according to site selection criteria and deployment hours to examine the effect of enforcement on collision reduction. More reductions were found at segments that had more collisions during the before period and longer deployment hours. The study also compared the safety effects of continuous and discontinuous enforcement strategies on different arterials, and the analysis revealed that continuous enforcement achieved more reductions across all severities and types of collisions. The study also investigated the spillover effects on adjacent unenforced approaches. Significant reductions were found, and these results are discussed with regard to the general and specific deterrence of the enforcement.

Transit signal priority (TSP) strategies are widely used to reduce bus travel delay and to increase bus service reliability. State-of-the-art strategies enable dynamic (and optimal), rather than predetermined, TSP plans to reflect real-time traffic conditions. These dynamic plans are called adaptive TSP. Existing adaptive TSP strategies normally use a performance index (PI), which is a weighted summation of all types of delays, to evaluate each candidate TSP plan and the weights to reflect the corresponding priority. The performance of an adaptive TSP depends on three factors: delay estimation, weights determination, and optimization formulation. In this context, there are three key academic contributions: (a) an enhanced bus delay estimation model based on advance detection, (b) a mechanism to adjust the PI weights dynamically to reflect the changing necessity of TSP under different conditions, and (c) TSP optimization formulated into a quadratic programming problem with an enhanced delay-based PI to obt...

Many runway capacity estimation models are available today, and developers usually claim that their models have been validated. However, information about the validation process is often limited, and different models are validated at differing levels of complexity. This paper proposes two validation methodologies for use in testing model predictions against reality. It demonstrates the two methodologies on two models [the airfield capacity model (ACM) and the runway simulator (rS)] and two airports (San Francisco International and Los Angeles International in California). The results indicate that when arrivals and departures are considered separately, both ACM and rS tend to overpredict capacities under good visibility conditions and to predict larger ranges of capacity values than are seen empirically. However, when considering total operations (arrivals and departures together), the results show that both models failed to provide good estimates of total throughput at both airports. Overall, arrival and departure capacity estimates from rS typically reflect empirical capacities better than those from ACM.

A growing body of research indicates that the activation of negative stereotypes can impede cognitive performance in adults, whereas positive stereotypes can facilitate cognitive performance. In two studies, we examined the effects of positive and negative stereotypes on the cognitive performance of children in three age groups: lower elementary school, upper elementary school, and middle school. Very young children in the lower elementary grades (kindergarten-grade 2) and older children in the middle school grades (grades 6–8) showed shifts in performance associated with the activation of positive and negative stereotypes; these shifts were consistent with patterns previously reported for adults. The subtle activation of negative stereotypes significantly impeded performance, whereas the subtle activation of positive stereotypes significantly facilitated performance. Markedly different effects were found for children in the upper elementary grades (grades 3–5). These results suggest that the development of stereotype susceptibility is a critical domain for understanding the connection between stereotypes and individual behavior.