Robust identification of air traffic flow patterns in Metroplex terminal areas under demand uncertainty

Multi-Airport Systems (MAS), or Metroplexes, serve air traffic demand in cities with two or more airports. Due to the spatial proximity and operational interdependency of the airports, Metroplex airspaces are characterized by high complexity, and current system structures fail to provide satisfactory utilization of the available airspace resources. In order to support system-level design and management towards increased operational efficiency in such systems, an accurate depiction of major demand patterns is a prerequisite. This paper proposes a framework for the robust identification of significant air traffic flow patterns in Metroplex systems, which is aligned with the dynamic route service policy for the effective management of Metroplex operations. We first characterize deterministic demand through a spatio-temporal clustering algorithm that takes into account changes in the traffic flows over the planning horizon. Then, in order to handle uncertainties in the demand, a Distributionally Robust Optimization (DRO) approach is proposed, which takes into account demand variations and prediction errors in a robust way to ensure the reliability of the demand identification. The DRO-based approach is applied on pre-tactical (i.e. one-day planning) as well as operational levels (i.e. 2-h rolling horizon). The framework is applied to Time Based Flow Management (TBFM) data from the New York Metroplex. The framework and results are validated by Subject Matter Experts (SMEs).     

A hybrid machine learning model for short-term estimated time of arrival prediction in terminal manoeuvring area

4D trajectory prediction is the core element of future air transportation system, which is intended to improve the operational ability and the predictability of air traffic. In this paper, we introduce a novel hybrid model to address the short-term trajectory prediction problem in Terminal Manoeuvring Area (TMA) by application of machine learning methods. The proposed model consists of two parts: clustering-based preprocessing and Multi-Cells Neural Network (MCNN)-based prediction. Firstly, in the preprocessing part, after data cleaning, filtering and data re-sampling, we applied principal Component Analysis (PCA) to reduce the dimension of trajectory vector variable. Then, the trajectories are clustered into several patterns by clustering algorithm. Using nested cross validation, MCNN model is trained to find out the appropriate prediction model of Estimated Time of Arrival (ETA) for each individual cluster cell. Finally, the predicted ETA for each new flight is generated in different cluster cells classified by decision trees. To assess the performance of MCNN model, the Multiple Linear Regression (MLR) model is proposed as the comparison learning model, and K-means++ and DBSCAN are proposed as two comparison clustering models in preprocessing part. With real 4D trajectory data in Beijing TMA, experimental results demonstrate that our proposed model MCNN with DBSCAN in preprocessing is the most effective and robust hybrid machine learning model, both in trajectory clustering and short-term 4D trajectory prediction. In addition, it can make an accurate trajectory prediction in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) with regards to comparison models.     

Cruise activity and pollution: The case of Barcelona

One of the main causes of mortality worldwide is air pollution. To tackle this problem, local, regional and national governments have implemented policies to reduce emissions from industrial and on-road sources. However, when these policies are being designed, shipping emissions are often overlooked. There has been a drastic increase in the demand for cruises and its economic relevance is also growing in port-cities. Barcelona is Europe’s leading cruise port, and it is located near the centre of the city. In this context, this paper analyses the impact of cruise ships in the air quality of the entire city of Barcelona using a dataset with information about pollutants and the number of cruises arriving to the port. We show that there is a direct impact between cruises staying at the port and city pollution. Additionally, the size and age of the cruise also affect air quality. The larger (or newer) the cruise is, the higher the emission generated. Moreover, our simulations show that the whole city is affected by these emissions.     

Determinants of airports’ environmental effects

Aviation is a fast growing sector with increasing environmental concerns linked to aircraft emissions at airports and noise nuisance. This paper investigates the factors affecting the annual environmental effects produced by a national aviation system. The environmental effects are computed using certification data for each aircraft-engine combination. Moreover, we also take into account for the amount of environmental effects that is internalized at the airport, mainly through noise regulation. We study a dataset covering information on Italian airports during the period 1999–2008. We show that a 1% increase in airport’s yearly movements yields a 1.05% increase in environmental effects, a 1% in aircraft size (measured in MTOW) gives rise to a 1.8% increase and a 1% increase in aircraft age generates a 0.69% increase in environmental effects. Similar results but with smaller magnitudes are observed if airport internalization is considered. Our policy implications are that the tariff internalizing the total amount of externality is about euro 180 per flight, while the tariff limiting only pollution is about euro 60 and the one reducing noise is about euro 110. Moreover, our airport examples show that managers should prefer to address additional capacity by increasing frequency rather than aircraft size, since the former strategy is more environmental friendly.     

Does the increase of public transit fares deteriorate air quality in Beijing?

Since 2006, Beijing lowered its public transit fares as a way to improve air quality. However, Beijing increased public transportation fare prices from December 28, 2014, and commuters pay for the distance they traveled rather than a flat fare. This paper explores the effect of Beijing public transit fares increase on air quality. We collect daily data of air pollution and weather variables and use synthetic control method of Abadie and Gardeazabal (2003) to select control units. We then estimate a difference-in-differences model and assess the effect of the policy on air quality index (AQI). We find a 16.28% increase in air pollution in short run. However, we find no longer-run effect on air quality.     

基于故障树分析的爆炸品公路军事运输危险源辨识

针对爆炸品公路军事运输安全标准高、涉及因素多、组织难度大的特点,运用故障树理论,对运输过程中的潜在危险源进行定性与定量分析,明确了各相关事故诱发因素的重要程度,为指导部队顺利完成爆炸品公路军事运输任务提供理论参考。     

Status Quo and Future Development of Shanghai Air Cargo Industry

Air cargo industry has become a new growth point of Shanghai economy. It also plays an important role in China's air cargo transport. This paper analyzes the air cargo industry status quo and future trends from the aspects of Shanghai airport, cargo facility, air cargo traffic (ACT) and its structure. Then it prospects Shanghai airport and its cargo facility's future development. After that it completes the regression analysis and elastic analysis of air cargo traffic and total foreign trade value (TFTV) with SPSS software. Based on this, it forecasts the future trend of air cargo traffic. The results show that Shanghai airport serves beyond the scope of Yangtze River Delta, air cargo traffic has significant imbalance and will grow to around 5.3 million tones with TFTV growth by the end of “twelfth five-year” period though slower than TFTV, and the airport cargo facilities can accommodate the air cargo industry development. Finally, some useful countermeasures for stimulating Shanghai air cargo industry are put forward.     

一种多货种散货码头带式输送机回程水洗清扫装置

内河散货港口带式输送机的多元使用是当下的发展趋势,传统的带式输送机清扫方式不能满足多货种的运输要求,回程水清扫装置的应用可解决这一难题.提出一种回程水洗清扫装置设计方案,可提高输送带的清洁效果,解决输送不同货物、不同物料间的相互污染问题,提高整体设备的利用率.