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Results for “travel demand forecasting”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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49 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1973

Forecasting the demand potential for STOL air transportation

A process for predicting the potential demand for STOL aircraft was investigated to provide a conceptual framework, and an analytical methodology for estimating the STOL air transportation market. It was found that: (1) schedule frequency has the strongest effect on the traveler's choice among available routes, (2) work related business constitutes approximately 50% of total travel volume, and (3) air travel demand follows economic trends.

Fan, S.↗
DOE OSTI2021

A data-driven operational model for traffic at the Dallas Fort Worth International Airport

Airports are on the front line of significant innovations, allowing the movement of more people and goods faster, cheaper, and with greater convenience. As air travel continues to grow, airports will face challenges in responding to increasing passenger vehicle traffic, which leads to lower operational efficiency, poor air quality, and security concerns. This paper evaluates methods for traffic demand forecasting combined with traffic microsimulation, which will allow airport operations staff to accurately predict traffic and congestion. Using two years of detailed data describing individual vehicle arrivals and departures, aircraft movements, and weather at Dallas-Fort Worth (DFW) International Airport, we evaluate multiple prediction methods including the Auto Regressive Integrated Moving Average (ARIMA) family of models, traditional machine learning models, and DeepAR, a modern recurrent neural network (RNN). We find that these algorithms are able to capture the diurnal trends in the surface traffic, and all do very well when predicting the next 30 minutes of demand. Longer forecast horizons are moderately effective, demonstrating the challenge of this problem and highlighting promising techniques as well as potential areas for improvement. Traffic demand is not the only factor that contributes to terminal congestion, because temporary changes to the road network, such as a lane closure, can make benign traffic demand highly congested. Combining a demand forecast with a traffic microsimulation framework provides a complete picture of traffic and its consequences. The result is an operational intelligence platform for exploring policy changes, as well as infrastructure expansion and disruption scenarios. To demonstrate the value of this approach, we present results from a case study at DFW Airport assessing the impact of a policy change for vehicle routing in high demand scenarios. This framework can assist airports like DFW as they tackle daily operational challenges, as well as explore the integration of emerging technology and expansion of their services into long term plans.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Energy-efficient multimodal mobility networks in transportation digital twins: Strategies and optimization

The study proposes a comprehensive Transportation Mobility (TransitMo) framework covering conceptual design, model formulation, optimization, simulation, and impact analysis of the transportation mobility system. TransitMo is composed of a transportation digital twin developed in Simulation of Urban MObility (SUMO) and an Intelligent Traffic Management and Control Center (ITMCC) that identifies the best ways to improve the movement of people within urban areas using various modes of transportation. This study encompasses advanced modeling techniques, algorithms, and strategic testing to optimize energy efficiency and mobility in a multimodal shared mobility network. TransitMo’s practical applications are exemplified through a city-scaled simulation network in Chattanooga, TN, employing demographic data to analyze historical traffic patterns and forecast future demands. Central to this methodology are three models: the User Preference Model (UP), the Energy Consumption Model (EC), and the System Optimization Model (SO). These models work in concert to iteratively devise the optimal travel incentives and minimize the total system cost in a real-time manner. In conclusion, test results verified that the proposed adaptive incentive program and optimized bus scheduling can improve network performance by increasing public transit ridership.

42 ENGINEERING↗
DOE OSTI2021

The Transportation Energy and Mobility Pathway Options (TEMPO) Model: Overview and Validation of V1.0 [Slides]

This presentation documents the approaches and methods used in the Transportation Energy & Mobility Pathway Options™ (TEMPO) model to evaluate passenger and freight demand for transportation and mobility services, project vehicle ownership and technology adoption decisions, and determine transport mode choices to derive scenarios of future energy use and emissions. TEMPO is an all-inclusive transportation demand model that covers the entire United States, with an implicit spatial resolution and an hourly temporal resolution that allows for generating time-resolved energy use profiles to assess multisectoral integration aspects. Key features of the TEMPO model include the ability to perform endogenous out-of-sample forecasting to extrapolate recent emerging trends and analyze impacts of disruptive technological breakthroughs and behavioral changes. TEMPO employs an innovative representation of passenger mobility demand stemming from household-level decisions that determine vehicle adoption, ownership, and use based on sociodemographics (e.g., income, household composition), technology attributes (e.g., travel cost, time), geography (e.g., urban, suburban, rural) and population-specific multiday mobility and travel requirements. This representation enables a more forward-looking perspective on the use of new mobility options and the adoption of alternative fuel vehicles, as well as a more accurate representation of their energy usage profiles than previous modeling approaches. A comparison with the U.S. Energy Information Administration’s Annual Energy Outlook showcases the ability of TEMPO to accurately replicate widely accepted projections by representing the key elements of the entire transportation sector at the appropriate level of resolution. TEMPO is intended to generate future scenarios of technology adoption, energy use, and emissions in the transportation sector to compare alternatives, inform decision makers, and assess integration with energy infrastructure and supply systems at an appropriate spatiotemporal resolution.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1979

The 30/20 GHz fixed communications systems service demand assessment. Volume 1: Executive summary

Demand for telecommunications services is forecasted for the period 1980-2000, with particular reference to that portion of the demand associated with satellite communications. Overall demand for telecommunications is predicted to increase by a factor of five over the period studied and the satellite portion of demand will increase even more rapidly. Traffic demand is separately estimated for voice, video, and data services and is also described as a function of distance traveled and city size. The satellite component of projected demand is compared with the capacity available in the C and Ku satellite bands and it is projected that new satellite technology and the implementation of Ka band transmission will be needed in the decade of the 1990's.

Gamble, R. B.↗
NASA NTRS2019

Complex Dynamics of Air Traffic Flow

Air traffic in the United States has continued to grow at a steady pace since 1980, except for a dip immediately after the tragic events of September 11, 2001. There are different growth scenarios associated both with the magnitude and the composition of the future air traffic. The Terminal Area Forecast (TAF), prepared every year by the FAA, projects the growth of traffic in the United States. Both Boeing and Airbus publish market outlooks for air travel annually. Although predicting the future growth of traffic is difficult, there are two significant trends: heavily congested major airports continue to see an increase in traffic, and the emergence of regional jets and other smaller aircraft with fewer passengers operating directly between non-major airports. The interaction between air traffic demand and the ability of the system to provide the necessary airport and airspace resources can be modeled as a network. The size of the resulting network varies depending on the choice of its nodes. It would be useful to understand the properties of this network to guide future design and development. Many questions, such as the growth of delay with increasing traffic demand and impact of the en route weather on future air traffic, require a systematic understanding of the properties of the air traffic network. There has been a major advance in the understanding of the behavior of networks with a large number of components. Several theories have been advanced about the evolution of large biological and engineering networks by authors in diversified disciplines like physics, mathematics, biology and computer science. Several networks exhibit a scale-free property in the sense that the probabilistic distribution of their nodes as a function of connections decreases slower than an exponential. These networks are characterized by the fact that a small number of components have a disproportionate influence on the performance of the network. Scale-free networks are tolerant to random failure of components, but are vulnerable to selective attack on components. This paper examines two network representations for the baseline air traffic system. A network defined with the 40 major airports as nodes and with standard flight routes as links has a characteristic scale: all nodes have 60 or more links and no node has more than 460 links. Another network is defined with baseline aircraft routing structure exhibits an exponentially truncated scale-free behavior. Its degree ranges from 2 connections to 2900 connections, and 225 nodes have more than 250 connections. Furthermore, those high-degree nodes are homogeneously distributed in the airspace. A consequence of this scale-free behavior is that the random loss of a single node has little impact, but the loss of multiple high-degree nodes (such as occurs during major storms in busy airspace) can adversely impact the system. Two future scenarios of air traffic growth are used to predict the growth of air traffic in the United States. It is shown that a three-times growth in the overall traffic may result in a ten-times impact on the density of traffic in certain parts of the United States.

Scale-free Networks↗
NASA NTRS1999

The Integrated Airport Competition Model, 1998

This paper addresses recent model development by the Directorate General of Civil Aviation (DGCA) and Hague Consulting Group (HCG) concerning long-distance travel. Long-distance travel demand is growing very quickly and raising a great deal of economic and policy issues. There is increasing competition among the main Western European airports, and smaller, regional airports are fighting for market share. New modes of transport, such as high speed rail, are also coming into the picture and affect the mode split for medium distance transport within Europe. Developments such as these are demanding the attention of policy makers and a tool is required for their analysis. For DGCA, Hague Consulting Group has developed a model system to provide answers to the policy questions posed by these expected trends, and to identify areas where policy makers can influence the traveller choices. The development of this model system, the Integrated Airport Competition Model/integraal Luchthaven Competitie Model (ILCM), began in 1992. Since that time the sub-models, input data and user interface have been expanded, updated and improved. HCG and DGCA have transformed the ILCM from a prototype into an operational forecasting tool.

Veldhuis, J.↗
NASA NTRS1999

The Integrated Airport Competition Model, 1998

This paper addresses recent model development by the Directorate General of Civil Aviation (DGCA) and Hague Consulting Group (HCG) concerning long-distance travel, Long-distance travel demand is growing very quickly and raising a great deal of economic and policy issues. There is increasing competition among the main Western European airports, and smaller, regional airports are fighting for market share. New modes of transport, such as high speed rail, arc also coming into the picture and affect the mode split for medium distance transport within Europe. Developments such as these are demanding the attention of policy makers and a tool is required for their analysis. For DGCA, Hague Consulting Group has developed a model system to provide answers to the policy questions posed by these expected trends, and to identify areas where policy makers can influence the traveller choices. The development of this model system, the Integrated Airport Competition Model/Integral Luchthaven Competitive Model (ILCM), began in 1992. Since that time the sub-models, input data and user interface have been expanded, updated and improved. HCG and DGCA have transformed the ILCM from a prototype into an operational forecasting tool.

Veldhuis, J.↗
NASA NTRS2008

Distributed Aviation Concepts and Technologies

Aviation has experienced one hundred years of evolution, resulting in the current air transportation system dominated by commercial airliners in a hub and spoke infrastructure. While the first fifty years involved disruptive technologies that required frequent vehicle adaptation, the second fifty years produced a stable evolutionary optimization of decreasing costs with increasing safety. This optimization has resulted in traits favoring a centralized service model with high vehicle productivity and cost efficiency. However, it may also have resulted in a system that is not sufficiently robust to withstand significant system disturbances. Aviation is currently facing rapid change from issues such as environmental damage, terrorism threat, congestion and capacity limitations, and cost of energy. Currently, these issues are leading to a loss of service for weaker spoke markets. These catalysts and a lack of robustness could result in a loss of service for much larger portions of the aviation market. The impact of other competing transportation services may be equally important as casual factors of change. Highway system forecasts indicate a dramatic slow down as congestion reaches a point of non-linearly increasing delay. In the next twenty-five years, there is the potential for aviation to transform itself into a more robust, scalable, adaptive, secure, safe, affordable, convenient, efficient and environmentally friendly system. To achieve these characteristics, the new system will likely be based on a distributed model that enables more direct services. Short range travel is already demonstrating itself to be inefficient with a centralized model, providing opportunities for emergent distributed services through air-taxi models. Technologies from the on-demand revolution in computers and communications are now available as major drivers for aviation on-demand adaptation. Other technologies such as electric propulsion are currently transforming the automobile industry, and will also significantly alter the functionality of future distributed aviation concepts. Many hurdles exist, including technology, regulation, and perception. Aviation has an inherent governmental role not present in other recent on-demand transformations, which may pose a risk of curtailing aviation democratization .

Moore, Mark D.↗
NASA NTRS2015

Understanding Air Transportation Market Dynamics Using a Search Algorithm for Calibrating Travel Demand and Price

This paper presents a search algorithm based framework to calibrate origin-destination (O-D) market specific airline ticket demands and prices for the Air Transportation System (ATS). This framework is used for calibrating an agent based model of the air ticket buy-sell process - Airline Evolutionary Simulation (Airline EVOS) -that has fidelity of detail that accounts for airline and consumer behaviors and the interdependencies they share between themselves and the NAS. More specificially, this algorithm simultaneous calibrates demand and airfares for each O-D market, to within specified threshold of a pre-specified target value. The proposed algorithm is illustrated with market data targets provided by the Transportation System Analysis Model (TSAM) and Airline Origin and Destination Survey (DB1B). Although we specify these models and datasources for this calibration exercise, the methods described in this paper are applicable to calibrating any low-level model of the ATS to some other demand forecast model-based data. We argue that using a calibration algorithm such as the one we present here to synchronize ATS models with specialized forecast demand models, is a powerful tool for establishing credible baseline conditions in experiments analyzing the effects of proposed policy changes to the ATS.

Kumar, Vivek↗
DOE OSTI2025

Propagating synthetic populations with dynamic Bayesian networks: a framework for long-horizon demographic forecasting

This study presents a dynamic demographic microsimulator using dynamic Bayesian networks to forecast long–term changes in household and individual life events. Leveraging longitudinal Panel Study of Income Dynamics (PSID) data, two networks for individuals and households were modeled to simulate transitions in employment, income, education, marriage, childbirth, leaving the parental home, home ownership, mortality, and household formation or dissolution. Across 1,000 simulation runs spanning 24 years, household–level outcomes remain highly accurate and individual–level predictions reasonable. Although accuracy naturally declines with projection horizon, performance remains promising at both levels. This study addresses a key limitation of existing population synthesis models, which typically generate only a single static snapshot of the population. In conclusion, by introducing a framework that propagates cross-sectional outputs into the future, the microsimulator enables the tracking of demographic evolution over time, enhances realism in population-based simulations, and supplies credible inputs to agent-based travel demand models.

Demographic modeling↗
DOE OSTI2022

Technology Integration to Gain Commercial Efficiency for the Urban Goods Delivery System, Meet Future Demand for City Passenger and Delivery Load/Unload Spaces, and Reduce Energy Consumption

This three-year project supported by the U.S. Department of Energy Vehicle Technologies Office has the potential to radically improve the urban freight system in ways that help both the public and private sectors. Working from 2018-2021, project researchers at the University of Washington’s Urban Freight Lab and collaborators at the Pacific Northwest National Laboratory have produced key data, tested technologies in complex urban settings, developed a prototype parking availability app, and helped close major knowledge gaps. All the fruits of this project can be harnessed to help cities better understand, support and actively manage truck load/unload operations and their urban freight transport infrastructure. Project learnings and tools can be used to help make goods delivery firms more efficient by reducing miles traveled and the time it takes to complete deliveries, benefitting businesses and residents who rely on the urban freight system for supplies of goods. And, ultimately, these project learnings and tools can be used to make cities more livable by minimizing wasted travel, which, in turn, contributes to reductions in fuel consumption and emissions. Cities today are challenged to effectively and efficiently manage their infrastructure to absorb the impacts of ever-increasing e-commerce-fueled delivery demand. All delivery trucks need to park somewhere to unload and load. Yet today’s delivery drivers have no visibility on available parking until they arrive at a site, which may be full. That means they can wind up cruising for parking, which wastes time and fuel and contributes to congestion. Once drivers do find parking, the faster they can unload at the spot, the faster they free up space for other drivers, helping others avoid circling for parking. This makes the parking space—and thus the greater load/unload network—more productive. To this end, the research team successfully met the project’s three goals, developing and piloting strategies and technologies to: 1. Reduce parking-seeking behavior in the study area by 20%; 2. Reduce parcel truck dwell time (the time a truck spends in a spot to load/unload) in the study area by 30%; 3. Increase curb space, alley space and private loading bay occupancy rates in the study area. The research team met these goals by creating and piloting on Seattle streets OpenPark, a first-of-its-kind real-time and forecasting curb parking app customized for commercial delivery drivers—giving drivers the “missing link” in their commonly used routing tools that tell them how best to get to delivery locations, but not what parking is available to use when they get there. Installing in-ground sensors on commercial vehicle load zones (CVLZs) and passenger load zones (PLZs) in the 10-block study area in Seattle’s downtown neighbourhood of Belltown let researchers glean real-time curb parking data. The research team also met project goals by piloting three parcel lockers in public and private spaces open to any delivery carrier, creating a consolidated delivery hub that lets drivers complete deliveries faster and spend less time parked. Researchers collected and analysed data to produce the first empirical, robust, statistically significant results as to the impact of the lockers, and app, on on-the-ground operations. In addition to collecting and analyzing sensor and other real-time and historical data, researchers rode along with delivery drivers to confirm real-world routing and parking behavior. Researchers also surveyed building managers on their private loading bay operations to understand how to boost usage.

97 MATHEMATICS AND COMPUTING↗