There are 3,091 counties in TSAM serving as the zones of travel activity in the continental United States. The trip-generation output is made up of two 3091 vectors: one for attractions and the other for productions for each county. Trip distribution fills up the cells between the vectors, creating a person-trip interchange table of demand between the two counties. Mode choice splits the demand between each county by mode of transportation. The mode choice model in TSAM and this paper estimates both the demand by mode between counties and the demand flows in the airport network associated with the counties. This is achieved by embedding an airport choice model in the mode choice model. Hence the model is both a mode choice and a partial trip assignment model. The framework for the process is shown in Figure 1. The modes of transportation considered in the TSAM model are commercial airline, automobile, SATS, and train. However, the focus in this paper is on the baseline model, which has only automobile and commercial airline modes. The trip assignment in TSAM involves converting the airport-to-airport person trips into aircraft operations, generating flights by using a time-of-day profile, and loading the flights on the National Airspace System to estimate the impact of aircraft operations in the system. The complete travel demand model is fully documented elsewhere (1-3).NASA is using TSAM to forecast future airport demands and assist the Joint Program Development Office (JPDO) in planning the next-generation air transportation system. NASA is also using TSAM to study demand for supersonic aircraft, tilt rotors, and short take-off and landing aircraft. This shows that the model is relevant and the output is critical to policy makers. This paper presents a family of logit models that have been developed since the SATS program to estimate intercity travel demand in the United States. LITERATURE REVIEW Review of Disaggregate Nationwide Travel Demand ModelsBetween 1976 and 1990, four major attempts were made to develop disaggregate national-level intercity mode choice models in the United States. All the models used versions of National Travel Surveys (NTS) conducted by the Bureau of the Census and the Bureau of Transportation Statistics (BTS). The first was a multinomial logit model by Stopher and Prashker in 1976, which used the 1972 NTS (4). Grayson developed a multinomial logit model by using the 1977 version of the NTS (5). Morrison and Winston were the first to apply a nested logit model (6). They used the log-sum variable to hierarchically nest three models: decision to rent a car, destination choice, and mode choice. Later, Koppelman extended Morrison's approach to Nested and mixed logit models were developed to study national-level intercity transportation in the United States. The models were used to estimate the market share of automobile and commercial air transportation of 3,091 counties and 443 commercial service airports in the United States. Models were calibrated with the use of the 1995 American Tra...

This paper addresses the static aircraft sequencing problem over a mixed-mode single runway (or closely interacting parallel runways), which commonly constitutes a critical bottleneck at airports. In contrast with disjunctive formulations, our modeling approach takes advantage of the underlying structure of an asymmetric traveling salesman problem with time-windows. This enables the development of efficient preprocessing and probing procedures, and motivates the derivation of several classes of valid inequalities along with partial convex hull representations to enhance problem solvability via tighter reformulations. The lifted model is further embedded within the framework of two proposed heuristics that are compared against the traditional first-come first-served (FCFS) heuristic with landing priority: an optimized FCFS policy (OFCFS) and a threshold-based suboptimized heuristic (TSH) with an a priori fixing of the relative order of aircraft that are sufficiently time-separated. Computational results using real data based on Doha International Airport (DOH) as well as simulated instances are reported to demonstrate the efficacy of the proposed exact and heuristic solution methods. In particular, for the DOH instances, heuristics OFCFS and TSH achieved an attractive runway utilization (4.3% and 5.0% makespan reduction, respectively, over the base FCFS policy with landing priority), while exhibiting limited aircraft position deviations (0.45 and 0.49 deviations on average, respectively, from the base FCFS positions with landing priority, with similar results being obtained for the simulated instances). The superiority of the proposed optimization models over previous disjunctive formulations is also demonstrated for challenging problem instances, resulting in over 50% CPU savings for the larger instances in our test-bed.

The purpose of this paper is to present a simplified method to estimate aircraft fuel consumption using an artificial neural network. The models developed here are can be implemented in fast-time airspace and airfield simulation models. A representative neural network aided fuel consumption model was developed using data given in the aircraft performance manual. The data used in this study was applicable to the Fokker 100 aircraft powered by Rolls-Royce Tay 650 engines. A second data set was applied to the SAAB 2000 turboprop aircraft with good results. The methodology can be extended to any type of aircraft including piston and turboprop type vehicles with confidence. The neural network was trained to estimate fuel consumption of an example aircraft. Results were compared to the actual performance provided in the aircraft performance manual and found to be accurate for possible implementation in fast-time simulation models. The result from the neural network model was compared with analytical models. The results of this study illustrate that a threelayer artificial neural network with nonlinear transfer functions can accurately represent complex aircraft fuel consumption functions for climb, cruise and descent phases of flight.

The complete historical US air travel demand is not available to the general public. In this paper, we propose a route-based optimization model to estimate the historical US air travel demand. We show that the distribution of estimated demand follows a logit model. An iterative solution algorithm is proposed to solve the optimization model. The route utility is designed as a function of route characteristics. A feedbackadjustment scheme is proposed to estimate the model coefficients in the route utility function. In the numerical example, we apply our method to estimate the US air travel demand in the year 1995. Copyright

A systems engineering methodology was used to study the National Aeronautics and Space Administration’s (NASA’s) Small Aircraft Transportation System (SATS) concept as a feasible mode of transportation. The proposed approach employs a multistep intercity transportation planning process executed inside a Systems Dynamics model. Doing so permits a better understanding of SATS impacts to society over time. The approach is viewed as an extension to traditional intercity transport models through the introduction of explicit demand–supply causal links of the proposed SATS over the complete life cycle of the program. The modeling framework discussed is currently being used by the Virginia SATS Alliance to quantify possible impacts of the SATS program for NASA’s Langley Research Center. There is discussion of some of the modeling efforts carried out so far and of some of the transportation modeling challenges facing the SATS program ahead.