Prediction and Analysis of Ground Stops with Machine Learning

A flight is considered to be delayed when it arrives 15 or more minutes later than scheduled. Delays attributed to the National Airspace System are one of the most common type of delays. Such delays may be caused by Traffic Management Initiatives (TMI) such as Ground Stops (GS), issued at affected airports. Ground Stops are implemented to control air traffic volume to specific airports where the projected traffic demand is expected to exceed the airports’ acceptance rate over a short period of time due to conditions such as inclement weather, volume constraints, closed runways, etc. Ground Stops can be considered to be the strictest Traffic Management Initiative (TMI), particularly because all flights destined to affected airports are grounded until conditions improve. Efforts have been made over the years to reduce the impact of Traffic Management Initiatives on airports and flight operations. However, these efforts have largely focused on otherTraffic Management Initiatives such as Ground Delay Programs (GDP), due to their frequency and duration compared to Ground Stops. Limited work has also been carried out on Ground Stops because of the limited amount of time that traffic management personnel often have between planning and implementing Ground Stops and external factors that influence decisions of traffic management personnel. Consequently, this research primarily focuses on the prediction of weather-related Ground Stops at Newark Liberty International (EWR) and LaGuardia (LGA) airports, with the secondary goal of gaining insights into factors that influence their occurrence. It is expected that this research will provide stakeholders with further insights into factors that influence the occurrence of weather-related Ground Stops at both airports. This is achieved by benchmarking Machine Learning algorithms in order to identify the best suited algorithm(s) for the prediction models, and identifying and analyzing key factors that influence the occurrence of weather-related Ground Stops at both airports. This is achieved by 1) fusing data from the Traffic Flow Management System (TFMS) and Automated Surface Observing Systems (ASOS) datasets, and 2) leveraging supervised Machine Learning algorithms to predict the occurrence of weather-related Ground Stops. The performance of these algorithms is evaluated using balanced accuracy, and identifies the Boosting Ensemble algorithm as the best suited algorithm for predicting the occurrence of Ground Stops at EWR and LGA. Further analysis also revealed that model performance is significantly better when using balanced datasets compared to imbalanced datasets

Evaluating the safety benefit of retrofitting motorways section with barriers meeting a new EU standard: Comparison of observational before–after methodologies

The road safety barriers are today designed and installed in compliance with the European standards for Road Restraint Systems (EN 1317), which lays down common requirements for the testing and certification in all EU countries. The introduction of the European Union (EU) regulation for safety barriers, which is based on performance, has encouraged European road agencies to perform an upgrade of the old barriers installed before 2000, with the expectation that there will be safety benefits at the retrofitted sites. Due to the high cost of such treatments, a benefit-cost analysis (BCA) is often used for site selection and ranking and to justify the investment. To this aim a crash modification factor (CMF) has to be applied and errors in the estimation of benefits are directly reflected in the reliability of BCA. Despite the benefits of empirical Bayes before–after (EB–BA) analysis or similar rigorous methods are well-known in the scientific world, these approaches are not always the standard for estimating the effectiveness of safety treatments. To this aim, the differences between the EB–BA and a naive comparison of observed crashes before and after the treatment are presented in the paper. Crash modification factors for total and target crashes are estimated by performing an EB–BA based on data from a motorway in Italy. As expected the results suggest a strong safety benefit for the ran-off-road crashes by reducing the number of severe crashes (fatal and injury). The statistical significance of results obtained by the EB–BA approach show that the retrofits are still cost-effective. The comparison pointed out as selection bias effects can overestimate the safety benefit of the retrofits when a naive approach is used to estimate the CMF and how those can significantly affect a benefit-cost analysis

What Are the Different Measures of Mobility Telling Us About Surface Transportation CO 2

The COVID‐19 pandemic led to widespread reductions in mobility and induced observable changes in atmospheric emissions. Recent work has employed novel mobility data sets as a proxy for trace gas emissions from traffic by scaling CO(2) emissions linearly with those near‐real‐time mobility data. Yet, there has been little work evaluating these emission numbers. Here, we systematically compare these mobility data sets to traffic data from local governments in seven diverse urban and national/state regions to characterize the magnitude of errors that result from using the mobility data. We observe differences in excess of 60% between these mobility data sets and local traffic data. We could not find a general functional relationship between the mobility data and traffic flow over all the regions and observe higher deviations from using such general relationships than the original data. Finally, we give an overview of the potential errors that come from estimating CO(2) emissions using (mobility or traffic) activity data. Future work should be cautious while using these mobility metrics for emission estimates