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The paper compares the MOBILE5a, MOBILE6, Virginia Tech microscopic energy and emission model (VT-Micro), and comprehensive modal emissions model (CMEM) models for estimating hot-stabilized, light-duty vehicle emissions. Specifically, Oak Ridge National Laboratory (ORNL) and Environmental Protection Agency (EPA) laboratory fuel consumption and emission databases are used for model comparisons. The comparisons demonstrate that CMEM exhibits some abnormal behaviors when compared with the ORNL data, EPA data, and the VT-Micro model estimates. Specifically, carbon monoxide (CO) emissions exhibit abrupt changes at low speeds and high acceleration levels and constant emissions at negative acceleration levels. Furthermore, oxides of nitrogen (NOx) emissions exhibit abrupt drops at high engine loads. In addition, the study demonstrates that MOBILE5a emission estimates compare poorly with EPA field data, while MOBILE6 model estimates show consistency with EPA field data and VT-Micro model estimates over various driving cycles. The VT-Micro model appears to be accurate in estimating hot-stabilized, light-duty, normal vehicle tailpipe emissions. Specifically, the emission estimates of the VT-Micro and MOBILE6 models are consistent in trends with laboratory measurements. Furthermore, the VT-Micro and MOBILE6 models accurately capture emission increases for aggressive acceleration drive cycles in comparison with other drive cycles.Key words: transportation energy, transportation environmental impacts, VT-Micro Model, CMEM, MOBILE5, MOBILE6, fuel consumption models, emission models.

The evaluation of many transportation network improvements commonly is conducted by first estimating average speeds from a transportation or traffic model and then converting these average speeds into emission estimates based on an environmental model such as MOBILE. Unfortunately, recent research has shown that certainly average speed and perhaps even simple estimates of the amount of delay and the number of stops on a link are insufficient measures to fully capture the impact of intelligent transportation system strategies such as traffic signal coordination. In an attempt to address this limitation, the application of a series of multivariate fuel consumption and emission prediction models is illustrated, both within a traffic simulation model of a signalized arterial and directly to instantaneous speed and acceleration data from floating cars traveling down a similar signalized arterial. The application of these multivariate relationships is illustrated for eight light-duty vehicles, ranging in size from subcompacts to minivans and sport-utilities using data obtained from the Oak Ridge National Laboratory. The objective is to illustrate that the application of these instantaneous models is both feasible and practical and that it produces results that are reasonable in terms of both their absolute magnitude and their relative trends. This research is one step in a more comprehensive modeling framework for dealing with the impacts of intelligent transportation systems on energy consumption and vehicle emissions. Other steps include analyses of traffic diversion and induced demand and validation of the estimated fuel consumption and emissions using direct on-road measurements.

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...

Free-Flight is a paradigm of aircraft operations that permits the selection of more cost-effective routes for flights rather than simple traversals between designated way-points, from various origins to different destinations. In this paper, we consider the effect of this paradigm on sector workloads and potential conflicts or collision risks, based on current and projected levels of commercial air traffic. To accomplish this task, we first develop an Airspace Sector Occupancy Model (AOM) that identifies the occupancies of flights within three-dimensional (possibly nonconvex) regions of space called sectors, by utilizing an iterative procedure to trace each flight's progress through sector modules, that constitute the sectors. Next, we develop an Aircraft Encounter Model (AEM), which uses the information obtained from AOM to efficiently estimate the number and nature of blind-conflicts (i.e., conflicts under no avoidance or resolution maneuvers) resulting from a selected mix of flight plans. Besides identifying the existence of a conflict, AEM also provides useful information on the severity of the conflict and its geometry, such as the faces across which an intruder enters and exits the protective shell or envelope of another aircraft, the duration of intrusion, its relative heading, and the point of closest approach. For purposes of evaluation and assessment, we also develop a metric that provides a summary of the conflicts in terms of severities and difficulty of resolution. Finally, we apply these models to real data provided by the Federal Aviation Administration (FAA) for evaluating several Free-Flight scenarios under wind-optimized conditions. This study constitutes the first phase of a project undertaken by a joint FAA/Eurocontrol Collision Risk Modeling Group to develop tasks for investigating air traffic control strategies and related workload and collision risk consequences under various scenarios. Follow-on work will incorporate pilot blunders, random deviations, and air traffic control man-in-the-loop maneuvers within the context of the Free-Flight paradigm, using the basic tools developed in the present study.

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.

W e present a large-scale, airspace planning and collaborative decision-making model (APCDM) to enhance the management of the U.S. National Airspace System (NAS). Given a set of flights that must be scheduled during some planning horizon, along with alternative surrogate trajectories for each flight as prompted by various airspace restriction scenarios imposed by dynamic severe weather systems or space launch special use airspaces (SUA), we develop a mixed-integer programming model to select a set of flight plans from among these alternatives, subject to flight safety, air traffic control workload, and airline equity constraints. The model includes a three-dimensional probabilistic conflict analysis, the derivation of valid inequalities, the development of air traffic control workload metrics, and the consideration of equity among airline carriers in absorbing costs related to rerouting, delays, and possible cancellations. The resulting APCDM model has potential use for both tactical and strategic applications, such as air traffic control in response to severe weather phenomena or spacecraft launches, FAA policy evaluation (separation standards, workload restrictions, sectorization strategies), Homeland Defense contingency planning, and military air campaign planning. The model can also serve a useful role in augmenting the FAA's National Playbook of standardized flight profiles in different disruption-prone regions of the national airspace. The present paper focuses on the theory and model development; Part II of this paper will address model parameter estimations and implementation test results.