Hojong Baik

Forecasting Model for Air Taxi, Commercial Airline, and Automobile Demand in the United States

A nationwide model predicts the annual county-to-county person round-trips for air taxi, commercial airline, and automobile at 1-year intervals through 2030. The transportation systems analysis model (TSAM) uses the four-step transportation systems modeling process to calculate trip generation, trip distribution, and mode choice for each county origin-destination pair. Network assignment is formulated for commercial airline and air taxi demand. TSAM classifies trip rates by trip purpose, household income group, and type of metropolitan statistical area from which the round-trip started. A graphical user interface with geographic information systems capability is included in the model. Potential applications of the model are nationwide impact studies of transportation policies and technologies, such as those envisioned with the introduction of extensive air taxi service using very light jets, the next-generation air transportation system, and the introduction of new aerospace technologies.

Enhanced Models for a Mixed Arrival-Departure Aircraft Sequencing Problem

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.

Integrated Model for Studying Small Aircraft Transportation System

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.

Time-dependent network assignment strategy for taxiway routing at airports

A time-dependent network assignment strategy is proposed for efficiently handling aircraft taxiway operations at airports. The suggested strategy is based on the incremental assignment technique that is frequently adopted in many urban transportation studies. The method assumes that the current aircraft route is influenced by previous recent aircraft assignments in the network. This simplified assumption obviates the need for iterative rerouting procedures for attaining some pure equilibrium state, which in any case might not be achievable in practical airport taxiway operations. The main benefit of applying the time-dependent network assignment approach to taxiway operations is the reduction and avoidance of possible conflicts that produce delays. Also proposed is a prototype of a fully time-dependent network assignment scheme that dispatches aircraft based also on future anticipated assignment. The suggested methodology could be adopted in the deployment of automated taxiway guidance systems that are planned for future implementation at congested airports.

Nationwide Impacts of Very Light Jet Traffic in the Future Next Generation Air Transportation System (NGATS)

*† ‡ § ** †† This paper describes a methodology to predict on-demand air taxi services using emerging Very Light Jets (VLJ) technology in the future National Air Transportation System (NAS). The paper describes airspace and airport impacts of VLJ traffic considering an improved Next Generation Air Transportation System (NGATS). The analysis presented fits within the framework of the Transportation Systems Analysis Model (TSAM) developed by the Air Transportation Systems Laboratory at Virginia Tech for NASA Langley Research Center. TSAM uses traditional air transportation systems engineering techniques to: 1) predict the number of intercity trips generated in the country based on socio-economic factors, 2) distribute these trips across the country, 3) predict the most likely modes of transportation used to execute these trips, 4) predict flights and trajectories associated with air transportation trips, and 5) predict impacts of the intercity trips generated in the National Airspace System (NAS).

NAS Demand Predictions, Transportation Systems Analysis Model (TSAM) Compared with Other Forecasts

The current work incorporates the Transportation Systems Analysis Model (TSAM) to predict the future demand for airline travel. TSAM is a multi-mode, national model that predicts the demand for all long distance travel at a county level based upon population and demographics. The model conducts a mode choice analysis to compute the demand for commercial airline travel based upon the traveler’s purpose of the trip, value of time, cost and time of the trip,. The county demand for airline travel is then aggregated (or distributed) to the airport level, and the enplanement demand at commercial airports is modeled. With the growth in flight demand, and utilizing current airline flight schedules, the Fratar algorithm is used to develop future flight schedules in the NAS. The projected flights can then be flown through air transportation simulators to quantify the ability of the NAS to meet future demand. A major strength of the TSAM analysis is that scenario planning can be conducted to quantify capacity requirements at individual airports, based upon different future scenarios. Different demographic scenarios can be analyzed to model the demand sensitivity to them. Also, it is fairly well know, but not well modeled at the airport level, that the demand for travel is highly dependent on the cost of travel, or the fare yield of the airline industry. The FAA projects the fare yield (in constant year dollars) to keep decreasing into the future. The magnitude and/or direction of these projections can be suspect in light of the general lack of airline profits and the large rises in airline fuel cost. Also, changes in travel time and convenience have an influence on the demand for air travel, especially for business travel. Future planners cannot easily conduct sensitivity studies of future demand with the FAA TAF data, nor with the Boeing or Airbus projections. In TSAM many factors can be parameterized

Utilizing Traveler Demand Modeling to Predict Future Commercial Flight Schedules in the NAS

The current work incorporates the Transportation Systems Analysis Model (TSAM) to predict the future demand for airline travel. TSAM is a multi-mode, national model that predicts the demand for all long distance travel at a county level based upon population and demographics. The model conducts a mode choice analysis to compute the demand for commercial airline travel based upon the traveler’s purpose of the trip, value of time, cost and time of the trip,. The county demand for airline travel is then aggregated (or distributed) to the airport level, and the enplanement demand at commercial airports is modeled. With the growth in flight demand, and utilizing current airline flight schedules, the Fratar algorithm is used to develop future flight schedules in the NAS. The projected flights can then be flown through air transportation simulators to quantify the ability of the NAS to meet future demand. A major strength of the TSAM analysis is that scenario planning can be conducted to quantify capacity requirements at individual airports, based upon different future scenarios. Different demographic scenarios can be analyzed to model the demand sensitivity to them. Also, it is fairly well know, but not well modeled at the airport level, that the demand for travel is highly dependent on the cost of travel, or the fare yield of the airline industry. The FAA projects the fare yield (in constant year dollars) to keep decreasing into the future. The magnitude and/or direction of these projections can be suspect in light of the general lack of airline profits and the large rises in airline fuel cost. Also, changes in travel time and convenience have an influence on the demand for air travel, especially for business travel. Future planners cannot easily conduct sensitivity studies of future demand with the FAA TAF data, nor with the Boeing or Airbus projections. In TSAM many factors can be parameterized

A Preliminary Assessment of Airport Noise and Emission Impacts Induced by Small Aircraft Transportation System Operations

[Abstract] This paper evaluates potential noise and emission impacts associated with an advanced Small Aircraft Transportation System (SATS). Specifically, the analysis presented in this paper quantifies possible noise and emission contributions of advanced single-engine and multi-engine piston-powered aircraft and very light jetpowered aircraft. The noise impact analysis is carried out using the standard Federal Aviation Administration (FAA) Integrated Noise Model (INM). The emission influence is modeled using the FAA Emission and Dispersion Modeling System (EDMS). The noise signature and emission parameters of a new generation Very Light Jet (VLJ) are modeled in our analysis. Major emission pollutant level is estimated at 3,415 airports. Noise contours studies are conducted at five airport noise impact spanning both metropolitan and rural General Aviation (GA) airports. Sensitivity analysis is conducted to evaluate influence of the fleet composition and advanced approach procedures in the present and future years. Nomenclature I. Introduction HE Small Aircraft Transportation System (SATS) is a concept proposed by the National Aeronautics and Space Administration (NASA) to employ advanced small aircraft (propeller and jet-powered) to satisfy point-to-point, on-demand air transportation services using existing underutilized airports. The SATS program represents a joint effort by government, industry and academia to improve the intercity mobility of various communities in the country. Part of the SATS program goals is to develop aircraft technologies and four operational technical capabilities to make this a reality. From the beginning of the program, SATS proponents identified noise impacts as critical to the acceptance to the concept. To understand potential noise impacts at airports, the Virginia Tech Air Transportation Systems Lab developed a Transportation System Analysis Model (TSAM) to assess impacts of SATS in the National Airspace System (1). TSAM uses county-level socio-economic data to forecast the number of intercity trips in the United States. The model uses proven transportation engineering methods to predict the number of travelers selecting among various modes of transportation (i.e., auto, airline, and other technologies like Very Light Jets and piston-powered aircraft operating as air-taxis). The demand for on-demand air transportation services has been evaluated at 3,415 SATS technology enabled airports. The demand function at each airport is characterized in terms of daily person-trips and daily flight arrivals and departures. This paper presents the evaluation of noise and emission impacts performed typical SATS enabled airports using the TSAM model. The noise impacts of SATS operations are assessed at five representative airports using the standard Federal Aviation Administration (FAA) Integrated Noise Model (INM) version 6.1c. The emission influences are modeled at 3,415 SATS compatible airports using the standard FAA Emission and Dispersion Modeling System (EDMS). Three representative SATS aircraft are modeled in our study: 1) a new generation Very Light Jet (VLJ) aircraft, 2) an advanced technology Single-Engine (SE), piston-powered aircraft, and 3) an advanced technology MultiEngine (ME), piston-powered aircraft. The VLJ aircraft is modeled as a new vehicle with advanced low-thrust, medium by-pass ratio turbofan engines in INM. VLJ aircraft have relatively slow approach and takeoff speeds (belonging to approach speed group A) and the Sound Exposure Level (SEL) curves have been adjusted to account for lower thrust produced by VLJ engines. The emission matrices of the VLJ including Carbon Monoxide (CO), Total Hydrocarbon (THC), Non-Methane Hydrocarbons (NMHC), Nitrogen Oxides (NOx) and Sulfur Oxides (Sox)

Integrating Air Transportation System Demand Predictions in Preliminary Aircraft Design

This paper describes a methodology to integrate air transportation demand estimates in the preliminary aircraft design process. The paper describes the adaptation of the Transportation Systems Analysis Model (TSAM) developed by the Air Transportation Systems Laboratory at Virginia Tech for NASA Langley Research Center to predict potential demand of aerospace vehicle concepts. TSAM uses traditional air transportation systems engineering techniques to: 1) predict the number of intercity trips generated in the country based on socio-economic factors, 2) distribute these trips across the country, 3) predict the most likely modes of transportation used to execute these trips, 4) predict flights and trajectories associated with air transportation trips, and 5) predict impacts of the intercity trips generated in the National Airspace System (NAS). The paper includes a case study to estimate the potential demand for advanced tilt-rotor aircraft technology operating in the Northeast Corridor in the United States. I.Introduction Traditional aircraft design analysis requires clear aircraft mission requirements and estimates of the number of vehicles to be produced in the program’s life cycle. Mission requirements are traditionally setup by the aircraft design team in consultation with the customer (typically airlines for commercial vehicle development). The determination of the potential market for the vehicle to be designed is more challenging to define. Airlines and aircraft manufacturers continuously revise their estimates of vehicle demand based on historical market outlooks. This uncertainty is perhaps best epitomized in the current battle between Airbus and Boeing about the potential demand for long-range commercial transport aircraft. Airbus justifies the design of very-large capacity aircraft such as the A380 on the grounds of mature origin-destination market consolidation and capacity constraints at existing hub airports. Boeing justifies the development of the 787 and 777-200LR aircraft on the grounds of market fragmentation across long-haul markets. This illustrates that existing techniques to predict market demand for new aerospace vehicles is a difficult task requiring an understanding of the interactions between social and technological factors. Very few models seem to exist to predict the potential demand of novel aerospace technologies. This is the main trust of this effort. NASA’s Systems Analysis Branch (SAB) is responsible for the evaluation of new aerospace vehicle concepts and thus has a vested interest at improving the modeling capabilities to predict vehicle demand.

A structural equation model of activity participation and travel behavior using longitudinal data

Abstract This paper presents a dynamic structural equation model (SEM) that explicitly addresses complicated causal relationships among socio-demographics, activity participation, and travel behavior. The model assumes that activity participation and travel patterns in the current year are affected by those in previous years. Using the longitudinal dataset collected from Puget sound transportation panel ‘wave 3’ and ‘wave 4,’ these assumptions are tested with suggested SEMs. Within each wave, the model is structured to have a three-level causal relationship that describes interactions among endogenous variables under time-budget constraints. The resulting coefficients representing the activity durations indicate that people tend to allocate their time according to the importance and the obligation of the activity level. Results from the dynamic SEM confirm the fact that peoples current activity and travel behavior do have effects on those in the future. The resulting model also shows that activity participation and travel behavior in ‘wave 3’ are closely related to those in ‘wave 4.’ These explicit explanations of relationships among variables could provide important perspectives in the activity-based approach which becomes recognized as a better analytical tool for the transportation planning and policy making process.