scholarly journals Agent-Based Simulation of Long-Distance Travel: Strategies to Reduce CO2 Emissions from Passenger Aviation

2021 ◽  
Vol 6 (2) ◽  
pp. 271-284 ◽  
Author(s):  
Alona Pukhova ◽  
Ana Tsui Moreno ◽  
Carlos Llorca ◽  
Wei-Chieh Huang ◽  
Rolf Moeckel
Keyword(s):  
Travel Demand ◽  
Ghg Emissions ◽  
Air Travel ◽  
Demand Model ◽  
Long Distance ◽  
Agent Based ◽  
Distance Travel ◽  
Transport Emissions ◽  
Long Distance Travel ◽  
Travel Demand Model

Every sector needs to minimize GHG emissions to limit climate change. Emissions from transport, however, have remained mostly unchanged over the past thirty years. In particular, air travel for short-haul flights is a significant contributor to transport emissions. This article identifies factors that influence the demand for domestic air travel. An agent-based model was implemented for domestic travel in Germany to test policies that could be implemented to reduce air travel and CO<sub>2</sub> emissions. The agent-based long-distance travel demand model is composed of trip generation, destination choice, mode choice and CO<sub>2</sub> emission modules. The travel demand model was estimated and calibrated with the German Household Travel Survey, including socio-demographic characteristics and area type. Long-distance trips were differentiated by trip type (daytrip, overnight trip), trip purpose (business, leisure, private) and mode (auto, air, long-distance rail and long-distance bus). Emission factors by mode were used to calculate CO<sub>2</sub> emissions. Potential strategies and policies to reduce air travel demand and its CO<sub>2</sub> emissions are tested using this model. An increase in airfares reduced the number of air trips and reduced transport emissions. Even stronger effects were found with a policy that restricts air travel to trips that are longer than a certain threshold distance. While such policies might be difficult to implement politically, restricting air travel has the potential to reduce total CO<sub>2</sub> emissions from transport by 7.5%.

Estimation of a Long-Distance Travel Demand Model using Trip Surveys, Location-Based Big Data, and Trip Planning Services

2018 ◽  
Vol 2672 (47) ◽  
pp. 103-113 ◽  
Author(s):  
Carlos Llorca ◽  
Joseph Molloy ◽  
Joanna Ji ◽  
Rolf Moeckel
Keyword(s):  
Travel Demand ◽  
Choice Model ◽  
Mode Choice ◽  
Demand Model ◽  
Long Distance ◽  
Trip Planning ◽  
Combining Data ◽  
Distance Travel ◽  
Long Distance Travel ◽  
Travel Demand Model

Long-distance trips are less frequent than short-distance urban trips, but contribute significantly to the total distance traveled, and thus to congestion and transport-related emissions. This paper develops a long-distance travel demand model for the province of Ontario, Canada. In this paper, long-distance demand includes non-recurrent overnight trips and daytrips longer than 40 km, as defined by the Travel Survey for Residents in Canada (TSRC). We developed a microscopic discrete choice model including trip generation, destination choice, and mode choice. The model was estimated using travel surveys, which did not provide data about destination attractiveness and modal level of service. Therefore, a data collection method was designed to obtain publicly available data from the location-based social network Foursquare and from the online trip planning service Rome2rio. In the first case, Foursquare data characterized land uses and predominant activities of the destination alternatives, by the number of user check-ins at different venue types (i.e., ski areas, outdoor or medical activities, etc.). In the second case, the use of Rome2rio data described the modal alternatives for each observed trip. Combining data from travel surveys, Foursquare, and Rome2rio, coefficients of the model were estimated econometrically. It was found that the Foursquare data on number of check-ins at destinations was statistically significant, especially for leisure trips, and improved the goodness of fit compared with models that only used population and employment. Additionally, Rome2rio mode-specific variables were found to be significant for mode choice selection, making the resulting model sensitive to changes in travel time, transit fares, or service frequencies.

Integration of National Long-Distance Passenger Travel Demand Model with Tennessee Statewide Model and Calibration to Big Data

2017 ◽  
Vol 2653 (1) ◽  
pp. 75-81 ◽  
Author(s):  
Vincent L. Bernardin ◽  
Nazneen Ferdous ◽  
Hadi Sadrsadat ◽  
Steven Trevino ◽  
Chin-Cheng Chen
Keyword(s):  
Big Data ◽  
Travel Demand ◽  
Data Sources ◽  
Demand Model ◽  
Long Distance ◽  
Passenger Travel ◽  
Distance Model ◽  
National Model ◽  
Distance Travel ◽  
Long Distance Travel

The Tennessee Department of Transportation replaced the quick-response-based long-distance component in its statewide model by integrating the new national long-distance passenger travel demand model in a new statewide model and calibrating it to long-distance trips observed in cell phone origin–destination data. The national long-distance model is a tour-based simulation model developed from FHWA research on long-distance travel behavior and patterns. The tool allows the evaluation of many policy scenarios, including fare or service changes for various modes, such as commercial air, intercity bus, Amtrak rail, and highway travel. The availability of this tool presents an opportunity for state departments of transportation in developing statewide models. Commercial big data from cell phones for long-distance trips also pre-sents an opportunity and a new data source for long-distance travel patterns, which previously have been the subject of limited data collection, in the form of surveys. This project is the first to seize on both of these opportunities by integrating the national long-distance model with the new Tennessee statewide model and by processing big data for use as a calibration target for long-distance travel in a statewide model. The paper demonstrates the feasibility of integrating the national model with statewide models, the ability of the national model to be calibrated to new data sources, the ability to combine multiple big data sources, and the value of big data on long-distance travel, as well as important lessons on its expansion.

Person-Based Micro-Simulation Demand Model for National Long-Distance Travel in the U.S.A.

2020 ◽  
Vol 2674 (6) ◽  
pp. 297-309
Author(s):  
Lei Zhang ◽  
Yijing Lu ◽  
Sepehr Ghader ◽  
Carlos Carrion ◽  
Arash Asadabadi ◽  
...  
Keyword(s):  
Large Scale ◽  
Travel Demand ◽  
Demand Model ◽  
Long Distance ◽  
Fuel Price ◽  
Travel Survey ◽  
Passenger Travel ◽  
Micro Simulation ◽  
Distance Travel ◽  
Long Distance Travel

As the nation and various states engage in funding transportation infrastructure improvements to meet future long-distance passenger travel demand, it is imperative to develop effective and practical modeling methods for analysis of long-distance passenger travel. Evaluating national-level infrastructure improvements requires a reliable analysis tool to model the demand for long-distance travel. The national travel demand model presented in this paper implements a person-level tour-based micro-simulation approach for modeling individuals’ long-distance or national activities in the U.S.A. This paper reviews the model framework, explains the model calibration, and presents applications of the model for policy evaluation and demand prediction. The model was estimated using the latest long-distance travel survey in the U.S.A., which is the 1995 American Travel Survey. As the estimation data is old, and no new long-distance travel survey with appropriate sample size is available to re-estimate the model, model calibration is the solution used to update the model and make it capable of capturing up-to-date travel patterns. Calibrating such a large-scale model can be challenging, because each calibration iteration is very costly. This paper describes the calibration effort conducted on the national long-distance micro-simulation model to showcase how a large-scale travel demand model can be calibrated efficiently. A fuel price scenario is analyzed to show how the national travel demand will change under a national fuel price increase scenario in the future year 2040. Another scenario analysis corresponding to construction of high-speed rail (HSR) is conducted to observe the effects of adding a HSR system to the northeast corridor on travel demand from a national perspective.

Model Calibration and Forecasts of Air Travel Demand with Categorized Household Socioeconomic Attributes

2020 ◽  
Vol 2674 (6) ◽  
pp. 363-371
Author(s):  
Jungin Kim ◽  
Ikki Kim ◽  
Jaeyeob Shim ◽  
Hansol Yoo ◽  
Sangjun Park
Keyword(s):  
Travel Behavior ◽  
Household Income ◽  
Travel Demand ◽  
Behavioral Model ◽  
Income Group ◽  
Air Travel ◽  
Jeju Island ◽  
Demand Model ◽  
Air Travel Demand ◽  
Travel Demand Model

The objectives of this study were to (1) construct an air demand model based on household data and (2) forecast future air demand to explain the relationship between air demand and individual travel behavior. To this end, domestic passenger air travel demand at Jeju Island in South Korea was examined. A multiple regression model with numerous explanatory variables was established by examining categorized household socioeconomic data that affected air demand. The air travel demand model was calibrated for 2009–2015 based on the annual average number of visits to Jeju Island by households in certain income groups. The explanatory variable was set using a dummy variable for each household income group and the proportion of airfare to GDP per capita. Higher household income meant more frequent visits to Jeju Island, which was well-represented in the model. However, the value of the coefficient for the highest income was lower than the value for the second-highest income group. This suggested that the highest income group preferred overseas travel destinations to domestic ones. The future air demand for Jeju airport was predicted as 26,587,407 passengers in 2026, with a subsequent gradual increase to approximately 33,000,000 passengers by 2045 in this study. This study proposed an air travel demand model incorporating household socioeconomic attributes to reflect individual travel behavior, which contrasts with previous studies that used aggregate data. By constructing an air travel model that incorporated socioeconomic factors as a behavioral model, more accurate and consistent projections could be obtained.

scholarly journals Creating an integrated agent-based travel demand model by combining mobiTopp and MATSim

2019 ◽  
Vol 151 ◽  
pp. 776-781 ◽  
Author(s):  
Lars Briem ◽  
Nicolai Mallig ◽  
Peter Vortisch
Keyword(s):  
Travel Demand ◽  
Demand Model ◽  
Agent Based ◽  
Travel Demand Model

scholarly journals Validation of an agent-based travel demand model with floating car data

2019 ◽  
Vol 37 ◽  
pp. 242-249
Author(s):  
Carlos Llorca ◽  
Sasan Amini ◽  
Ana Tsui Moreno ◽  
Rolf Moeckel
Keyword(s):  
Travel Demand ◽  
Demand Model ◽  
Agent Based ◽  
Floating Car Data ◽  
Travel Demand Model

scholarly journals Territorializing International Travel Emissions: Geography and Magnitude of the Hidden Climate Footprint of Brussels

2021 ◽  
Vol 6 (2) ◽  
pp. 285-298 ◽  
Author(s):  
Kobe Boussauw ◽  
Jean-Michel Decroly
Keyword(s):  
Global Warming ◽  
Fossil Fuel ◽  
Regional Climate ◽  
Strong Dependence ◽  
Air Travel ◽  
International Travel ◽  
International Mobility ◽  
Long Distance ◽  
Distance Travel ◽  
Long Distance Travel

In the present article we investigate the geography and magnitude of the climate footprint of long-distance travel with Brussels, Belgium, as a destination. The internationally networked position of this city goes hand in hand with a strong dependence on international mobility, which largely materializes in impressive volumes of long-distance travel and associated consumption of important amounts of fossil fuel. Despite a surge in concerns about global warming, the climate footprint of most international travel, notably air travel, is not included in the official national and regional climate inventories, or in other words, it is not territorialized. The official climate footprint of the Brussels-Capital Region attained 3.7 Mton CO<sub>2</sub>eq per year (in 2017). Based on our exploratory calculations, however, the total estimated climate footprint of all Brussels-bound international travel equalled an additional 2.7 Mton CO<sub>2</sub>eq. In terms of geographical distribution, over 70% of international travellers to Brussels come from Europe, while these represent only 15% of the climate footprint of all international travel to Brussels. We conclude that the practice of not allocating emissions caused by international travel to territorial units has kept the magnitude and complexity of this problem largely under the radar and contributes to the lack of societal support for curbing growth of international aviation.

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