Abstract

Proceedings of the 48th IEEE Conference on Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. The accurate estimation of airport capacity is critical for the efficient planning of landing and takeoff operations, and the mitigation of congestion-induced delays. The analysis of tradeoffs between arrival and departure capacity at an airport, represented by the airport capacity envelope, has been the main focus of prior research. The increasing demand for air traffic operations has resulted in the growth of multi-airport systems, in which several major airports that are in close proximity of each other serve the same geographical region. The arrival and departure flows into these airports interact with each other, and it is necessary to consider inter-airport arrival-departure capacity tradeoffs while scheduling operations. This paper proposes a statistical technique based on quantile regression, for systematically analyzing arrival-departure capacity tradeoffs in multi-airport systems using observations of flight operations. The proposed technique enables the identification of key factors (such as, runway configuration geometry, weather conditions, etc.) that influence both the capacity envelopes of individual airports, and the capacity envelope of the multi-airport system as a whole. The approach is demonstrated through an analysis of the capacity envelopes of the New York area multi-airport system (comprising Newark (EWR), John F. Kennedy (JFK) and LaGuardia (LGA) airports). Document type: Conference object

Abstract

In recent years, the growing prevalence of social networks makes it possible to utilize human users as sensors to inspect city environment and human activities. Consequently, valuable insights can be gained by applying data mining techniques to the data generated through social networks. In this work, a practical approach to combine data mining techniques with statistical analysis is proposed to implement crowd sensing in a smart city. A case study to analyze the relationship between weather conditions and traffic congestion in Beijing based on tweets posted on Sina Weibo platform is presented to demonstrate the proposed approach. Following the steps of data pre-processing and topic determination, we applied Granger Causality Test to study the causal relationships between weather conditions, traffic congestion and human outdoor activity. The mediation analysis is also implemented to verify human outdoor activity as a mediator variable significantly carrying the influence of good weather to traffic congestion. The result demonstrates that outdoor activity serves as a mediator transmitting the effect of good weather on traffic congestion. In addition, the causes of negative emotion are also studied.