Long-Run Elasticity of Demand for Passenger Car Travel with Respect to Highway Tolls and Gasoline Prices for Long Distance Trips

Marshment, R S

doi:10.1061/9780784412442.368

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…This dataset was used in this study to estimate sets of econometric models on long-distance travels. The details of the model components and estimation results can be found in previous works by the authors ( 20, 21 ).…”

Section: Model Overviewmentioning

confidence: 99%

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

Zhang

Lu²,

Ghader

et al. 2020

Transportation Research Record

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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.

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Section: Model Overviewmentioning

confidence: 99%

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

Zhang

Lu²,

Ghader

et al. 2020

Transportation Research Record

View full text Add to dashboard Cite

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“…Unlike GDP per capita, it does not reflect the evolution of the average income per inhabitant within a certain territory, but only approaches the amount of those individuals potentially having a higher income. The only reference of Employment as an explanatory variable for road traffic is found in Zhang (2012) for the case of the Oklahoma toll network. This author considered employment of non-agricultural sectors, obtaining an elasticity of 0.25.…”

Section: Key Socioeconomic Variables Explaining Road Demand Evolutionmentioning

confidence: 99%

“…As for the socioeconomic variables in the model, short-run elasticities with respect to population averaged 1.31, while results for unemployment rate were surprisingly small (+0.0) on average. Burris and Huang (2011) For the case of the Oklahoma interurban toll network, Zhang et al (2012) Authority focused on the potential impacts that gas prices can have on toll revenues rather than purely on travel demand, with no deep analysis conducted on traffic data.…”

Section: Previous Research For Toll Roadsmentioning

confidence: 99%

Determining explanatory factors for road demand in interurban toll roads : an asserted approach

Sánchez¹

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Policies and Modeling of Energy Systems for Reaching European Bioenergy Targets

Leduc

Wetterlund

Dotzauer

et al. 2015

Handbook of Clean Energy Systems

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To mitigate carbon dioxide emissions, the European Union ( EU ) and many countries around the world have set into action different policy tools such as a carbon tax or feed‐in tariffs. Most instruments have, directly or indirectly, an impact on the use of biomass for energy purposes. The principal policy tools relevant to bioenergy production and applied in the EU are described in this article and their observed impact on bioenergy production levels is assessed. In addition, a model application on the policy tools is presented to assess future impact of policies on production of energy carriers from lignocellulosic biomass at the EU level and at the international level. This model application is based on the optimization model BeWhere , which determines the optimal location and technology of a set a bioenergy conversion plants, minimizing the cost of the supply chain. Sweden, Austria, and Finland are the three countries that are highlighted in the EU . An international perspective is also included by presenting a Brazilian case study. From the modeling results, it can be derived that integrated biofuel production would be the most competitive way for biofuel production and that a carbon tax and the emission trading scheme are the most cost‐efficient solutions to decrease emissions.

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Long-Run Elasticity of Demand for Passenger Car Travel with Respect to Highway Tolls and Gasoline Prices for Long Distance Trips

Cited by 3 publications

References 3 publications

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

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

Determining explanatory factors for road demand in interurban toll roads : an asserted approach

Policies and Modeling of Energy Systems for Reaching European Bioenergy Targets

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