Antonio A. Trani

DEVELOPMENT OF VT-MICRO MODEL FOR ESTIMATING HOT STABILIZED LIGHT DUTY VEHICLE AND TRUCK EMISSIONS

Abstract The paper applies a framework for developing microscopic emission models (VT-Micro model version 2.0) for assessing the environmental impacts of transportation projects. The original VT-Micro model was developed using chassis dynamometer data on nine light duty vehicles. The VT-Micro model is expanded by including data from 60 light duty vehicles and trucks. Statistical clustering techniques are applied to group vehicles into homogenous categories. Specifically, classification and regression tree algorithms are utilized to classify the 60 vehicles into 5 LDV and 2 LDT categories. In addition, the framework accounts for temporal lags between vehicle operational variables and measured vehicle emissions. The VT-Micro model is validated by comparing against laboratory measurements with prediction errors within 17%.

Requirements for Evaluating Traffic Signal Control Impacts on Energy and Emissions Based on Instantaneous Speed and Acceleration Measurements

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.

An Airspace Planning and Collaborative Decision-Making Model: Part I--Probabilistic Conflicts, Workload, and Equity Considerations

We 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 FAAsNational 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.

Logit Models for Forecasting Nationwide Intercity Travel Demand in the United States

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 Travel Survey. Separate models were developed for business and nonbusiness trip purposes. The explanatory variables used in the utility functions of the models were travel time, travel cost, and travelers household income. Given an input county-to-county trip demand table, the models were used to estimate county-to-county travel demand by automobile and commercial airline between all counties and commercial-service airports in the United States. The model has been integrated into a computer software framework called the transportation systems analysis model that estimates nationwide intercity travel demand in the United States.

An Airspace-Planning and Collaborative Decision-Making Model: Part IICost Model, Data Considerations, and Computations

In Part I of this paper, we presented a large-scale airspace-planning and collaborative decision-making (APCDM) model that is part of a Federal Aviation Administration (FAA)-sponsored effort to enhance the management of the 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, we developed 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 considerations. The present paper offers insights related to, and a detailed description of, implementing this APCDM model, including the development of a comprehensive cost model, a study for prescribing a set of appropriate parameter values for the overall model, and an investigation on incorporating a suitable set of valid inequalities in the model formulation. Computational results are presented based on several test cases derived from the Enhanced Traffic Management System (ETMS) data provided by the FAA. The results indicate that under plausible probabilistic trajectory error assumptions and with the incorporation of star subgraph convex hull-based valid inequalities, the model offers a viable tool that can be used by the FAA for both tactical and strategic applications.

An Airspace Planning Model for Selecting Flight-plans Under Workload, Safety, and Equity Considerations

In this paper, we present an airspace planning model (APM) that has been developed for use in both tactical and strategic planning contexts under various airspace scenarios. Given a set of flights for a particular time horizon, along with (possibly several) alternative flight-plans for each flight that are based on delays and diversions, due to special-use airspace (SUA) restrictions prompted by launches at spaceports or adverse weather conditions, this model prescribes a set of flight-plans to be implemented. The model formulation seeks to minimize and delay fuel-cost-based objective function, subject to the constraints that each flight is assigned one of the designated flight-plans, and that the resulting set of flight-plans satisfies certain specified workload, safety, and equity criteria. These requirements ensure that the workload for air-traffic controllers in each sector is held under a permissible limit, that any potential conflicts are routinely resolvable, and that the various airlines involved derive equitable levels of benefits from the overall implemented schedule. To solve the resulting 0--1 mixed-integer programming problem more effectively using commercial software (e.g., CPLEX-MIP), we explore the use of reformulation techniques designed to more closely approximate the convex hull of feasible solutions to the problem. We also prescribe a polynomial-time heuristic procedure that is demonstrated to provide solutions to the problem within 0.01% of optimality. Computational results are reported on several scenarios based on actual flight data obtained from the Federal Aviation Administration (FAA) to demonstrate the efficacy of the proposed approach for air-traffic management (ATM) purposes.

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.

MICROFRAMEWORK FOR MODELING OF HIGH-EMITTING VEHICLES

High emitters represent a small fraction of the vehicle fleet, yet they are responsible for a large portion of the total mobile source emissions. Drive-cycle-specific high-emitter cut points for the identification of high-emitter vehicles are first developed. A microscopic model for estimating high-emitter vehicle emissions by using second-by-second emission data is developed subsequently. The proposed model estimates vehicle emissions with a margin of error of 10% when compared with in-laboratory bag emission measurements. The model was incorporated in the INTEGRATION traffic assignment and simulation software for the environmental assessment of alternative traffic operational projects, including emerging intelligent transportation system initiatives.

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.