Airport Information Systems—Landside Management Information Systems

Research on the intersection of the areas of aviation and management information systems is sparse. Just as within other economic sectors, members of the aviation sector must incorporate new and existing technologies as they grow to maintain their competitive edge whether in aircraft systems, airports or other aerospace and aviation related industries. A proper classification is a prerequisite to systems alignment. This paper reviews landside airport information management systems, and their connections and interoperability with other systems and who the key airport users are. The information presented in this paper is based on interviews and data collection at a number of representative airports across the United States. Airport size and function are key considerations in the acquisition of information management system airside or land side. The implication is that not all airports are equipped in the same manner and therefore these systems can only be considered as representative of what exists “on the ground”. This paper represents a point of departure or a reference for those researchers interested in a more indepth study of airport information systems on the landside.

Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management?

Natural hazards are often studied in isolation.However,there is a great need to examine hazards holistically to better manage the complex of threats found in any region.Many regions of the world have complex hazard landscapes wherein risk from individual and/or multiple extreme events is omnipresent.Extensive parts of Iran experience a complex array of natural hazards-floods,earthquakes,landslides,forest fires,subsidence,and drought.The effectiveness of risk mitigation is in part a function of whether the complex of hazards can be collectively considered,visualized,and evaluated.This study develops and tests individual and collective multihazard risk maps for floods,landslides,and forest fires to visualize the spatial distribution of risk in Fars Province,southern Iran.To do this,two well-known machine-learning algorithms-SVM and MARS-are used to predict the distribution of these events.Past floods,landslides,and forest fires were surveyed and mapped.The locations of occurrence of these events(individually and collectively) were randomly separated into training(70%) and testing(30%) data sets.The conditioning factors(for floods,landslides,and forest fires) employed to model the risk distributions are aspect,elevation,drainage density,distance from faults,geology,LULC,profile curvature,annual mean rainfall,plan curvature,distance from man-made residential structures,distance from nearest river,distance from nearest road,slope gradient,soil types,mean annual temperature,and TWI.The outputs of the two models were assessed using receiver-operating-characteristic(ROC) curves,true-skill statistics(TSS),and the correlation and deviance values from each models for each hazard.The areas-under-the-curves(AUC) for the MARS model prediction were 76.0%,91.2%,and 90.1% for floods,landslides,and forest fires,respectively.Similarly,the AUCs for the SVM model were 75.5%,89.0%,and 91.5%.The TSS reveals that the MARS model was better able to predict landslide risk,but was less able to predict flood-risk patterns and forest-fire risk.Finally,the combination of flood,forest fire,and landslide risk maps yielded a multi-hazard susceptibility map for the province.The better predictive model indicated that 52.3% of the province was at-risk for at least one of these hazards.This multi-hazard map may yield valuable insight for land-use planning,sustainable development of infrastructure,and also integrated watershed management in Fars Province.

Designing Sustainable Airport Ground Access/Egress Systems Supported by Fast-Time Modeling

The problems of airport landside capacity assessment are of industry-wide interest. Evaluation of landside capacity enables airport operators and airport designers to identify passenger and baggage flow bottlenecks, identify the primary cause of bottlenecks formation and take measures mitigating the impact of bottlenecks on the airport terminal operation. Many studies dealing with the problems of airport landside capacity are focused mainly on the processing part of the airport terminal and consider the airport terminal to be an isolated system. Even the most of models of airport landside operations developed using various simulation （both generic and dedicated） software packages （e.g., PaxSim, SLAM, WITNESS, ARENA or EXTEND） are designed for simulating the passenger and baggage flows only between curb-side and apron. Although this approach provides valuable data concerning capacity, delays or processing bottlenecks, in some cases identified capacity constraints are only the symptoms of the actual problem. In order to discover the cause of the problem, it is necessary to consider the airport terminal as an integral part of much more complex regional, national or international transportation system. This article reflects the above mentioned requirements and introduces an innovative approach to passenger and baggage flow simulation based on the fact that airport terminal is considered as an integral part of air passenger door-to-door transportation process.

Potential loess landslide deformation monitoring using L-band SAR interferometry

Multi-temporal InSAR technique can implement continuous earth surface deformation detection with long time scale and wide geographical coverage.In this paper,we first employ the Small Baseline Subset method to survey potential landslides in Guide County,Qinghai Province,which is identified as a loess landslide prone area for geological and climate conditions.Two anomalous deformation regions are detected by L-band Phased Array and L-band Synthetic Aperture Radar stacks.Then,qualitative and quantitative evaluations of the measuring points are given for understanding the distribution regularity of deformation.Finally,preliminary correlation between the time-series deformation and triggering factors is analyzed to explore the driving mechanism for landslide movement.The results demonstrate that L-band SAR has high potential in landslide monitoring applications and can be used as the basis for landslide recognizing,precursory information extracting,and early warning.