Estimating household travel energy consumption in conjunction with a travel demand forecasting model

Venu M. Garikapati, Daehyun You, Wenwen Zhang, Ram Pendyala, Subhrajit Guhathakurta, Marilyn A. Brown, Bistra Dilkina

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Citations (Scopus)

Abstract

This paper presents a methodology for the calculation of the consumption of household travel energy at the level of the traffic analysis zone (TAZ) in conjunction with information that is readily available from a standard four-step travel demand model system. This methodology embeds two algorithms. The first provides a means of allocating non-home-based trips to residential zones that are the source of such trips, whereas the second provides a mechanism for incorporating the effects of household vehicle fleet composition on fuel consumption. The methodology is applied to the greater Atlanta, Georgia, metropolitan region in the United States and is found to offer a robust mechanism for calculating the footprint of household travel energy at the level of the individual TAZ; this mechanism makes possible the study of variations in the energy footprint across space. The travel energy footprint is strongly correlated with the density of the built environment, although socioeconomic differences across TAZs also likely contribute to differences in travel energy footprints. The TAZ-level calculator of the footprint of household travel energy can be used to analyze alternative futures and relate differences in the energy footprint to differences in a number of contributing factors and thus enables the design of urban form, formulation of policy interventions, and implementation of awareness campaigns that may produce more-sustainable patterns of energy consumption.

Original languageEnglish (US)
Title of host publicationDemand Forecasting, Volume 1
PublisherNational Research Council
Pages1-10
Number of pages10
Volume2668
ISBN (Electronic)9780309441643
DOIs
StatePublished - 2017

Publication series

NameTransportation Research Record
Volume2668
ISSN (Print)0361-1981

Fingerprint

Energy utilization
Fuel consumption
Chemical analysis

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering

Cite this

Garikapati, V. M., You, D., Zhang, W., Pendyala, R., Guhathakurta, S., Brown, M. A., & Dilkina, B. (2017). Estimating household travel energy consumption in conjunction with a travel demand forecasting model. In Demand Forecasting, Volume 1 (Vol. 2668, pp. 1-10). (Transportation Research Record; Vol. 2668). National Research Council. https://doi.org/10.3141/2668-01

Estimating household travel energy consumption in conjunction with a travel demand forecasting model. / Garikapati, Venu M.; You, Daehyun; Zhang, Wenwen; Pendyala, Ram; Guhathakurta, Subhrajit; Brown, Marilyn A.; Dilkina, Bistra.

Demand Forecasting, Volume 1. Vol. 2668 National Research Council, 2017. p. 1-10 (Transportation Research Record; Vol. 2668).

Research output: Chapter in Book/Report/Conference proceedingChapter

Garikapati, VM, You, D, Zhang, W, Pendyala, R, Guhathakurta, S, Brown, MA & Dilkina, B 2017, Estimating household travel energy consumption in conjunction with a travel demand forecasting model. in Demand Forecasting, Volume 1. vol. 2668, Transportation Research Record, vol. 2668, National Research Council, pp. 1-10. https://doi.org/10.3141/2668-01
Garikapati VM, You D, Zhang W, Pendyala R, Guhathakurta S, Brown MA et al. Estimating household travel energy consumption in conjunction with a travel demand forecasting model. In Demand Forecasting, Volume 1. Vol. 2668. National Research Council. 2017. p. 1-10. (Transportation Research Record). https://doi.org/10.3141/2668-01
Garikapati, Venu M. ; You, Daehyun ; Zhang, Wenwen ; Pendyala, Ram ; Guhathakurta, Subhrajit ; Brown, Marilyn A. ; Dilkina, Bistra. / Estimating household travel energy consumption in conjunction with a travel demand forecasting model. Demand Forecasting, Volume 1. Vol. 2668 National Research Council, 2017. pp. 1-10 (Transportation Research Record).
@inbook{52e93ef5cac04f19b577d77b6c136823,
title = "Estimating household travel energy consumption in conjunction with a travel demand forecasting model",
abstract = "This paper presents a methodology for the calculation of the consumption of household travel energy at the level of the traffic analysis zone (TAZ) in conjunction with information that is readily available from a standard four-step travel demand model system. This methodology embeds two algorithms. The first provides a means of allocating non-home-based trips to residential zones that are the source of such trips, whereas the second provides a mechanism for incorporating the effects of household vehicle fleet composition on fuel consumption. The methodology is applied to the greater Atlanta, Georgia, metropolitan region in the United States and is found to offer a robust mechanism for calculating the footprint of household travel energy at the level of the individual TAZ; this mechanism makes possible the study of variations in the energy footprint across space. The travel energy footprint is strongly correlated with the density of the built environment, although socioeconomic differences across TAZs also likely contribute to differences in travel energy footprints. The TAZ-level calculator of the footprint of household travel energy can be used to analyze alternative futures and relate differences in the energy footprint to differences in a number of contributing factors and thus enables the design of urban form, formulation of policy interventions, and implementation of awareness campaigns that may produce more-sustainable patterns of energy consumption.",
author = "Garikapati, {Venu M.} and Daehyun You and Wenwen Zhang and Ram Pendyala and Subhrajit Guhathakurta and Brown, {Marilyn A.} and Bistra Dilkina",
year = "2017",
doi = "10.3141/2668-01",
language = "English (US)",
volume = "2668",
series = "Transportation Research Record",
publisher = "National Research Council",
pages = "1--10",
booktitle = "Demand Forecasting, Volume 1",

}

TY - CHAP

T1 - Estimating household travel energy consumption in conjunction with a travel demand forecasting model

AU - Garikapati, Venu M.

AU - You, Daehyun

AU - Zhang, Wenwen

AU - Pendyala, Ram

AU - Guhathakurta, Subhrajit

AU - Brown, Marilyn A.

AU - Dilkina, Bistra

PY - 2017

Y1 - 2017

N2 - This paper presents a methodology for the calculation of the consumption of household travel energy at the level of the traffic analysis zone (TAZ) in conjunction with information that is readily available from a standard four-step travel demand model system. This methodology embeds two algorithms. The first provides a means of allocating non-home-based trips to residential zones that are the source of such trips, whereas the second provides a mechanism for incorporating the effects of household vehicle fleet composition on fuel consumption. The methodology is applied to the greater Atlanta, Georgia, metropolitan region in the United States and is found to offer a robust mechanism for calculating the footprint of household travel energy at the level of the individual TAZ; this mechanism makes possible the study of variations in the energy footprint across space. The travel energy footprint is strongly correlated with the density of the built environment, although socioeconomic differences across TAZs also likely contribute to differences in travel energy footprints. The TAZ-level calculator of the footprint of household travel energy can be used to analyze alternative futures and relate differences in the energy footprint to differences in a number of contributing factors and thus enables the design of urban form, formulation of policy interventions, and implementation of awareness campaigns that may produce more-sustainable patterns of energy consumption.

AB - This paper presents a methodology for the calculation of the consumption of household travel energy at the level of the traffic analysis zone (TAZ) in conjunction with information that is readily available from a standard four-step travel demand model system. This methodology embeds two algorithms. The first provides a means of allocating non-home-based trips to residential zones that are the source of such trips, whereas the second provides a mechanism for incorporating the effects of household vehicle fleet composition on fuel consumption. The methodology is applied to the greater Atlanta, Georgia, metropolitan region in the United States and is found to offer a robust mechanism for calculating the footprint of household travel energy at the level of the individual TAZ; this mechanism makes possible the study of variations in the energy footprint across space. The travel energy footprint is strongly correlated with the density of the built environment, although socioeconomic differences across TAZs also likely contribute to differences in travel energy footprints. The TAZ-level calculator of the footprint of household travel energy can be used to analyze alternative futures and relate differences in the energy footprint to differences in a number of contributing factors and thus enables the design of urban form, formulation of policy interventions, and implementation of awareness campaigns that may produce more-sustainable patterns of energy consumption.

UR - http://www.scopus.com/inward/record.url?scp=85032835496&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85032835496&partnerID=8YFLogxK

U2 - 10.3141/2668-01

DO - 10.3141/2668-01

M3 - Chapter

VL - 2668

T3 - Transportation Research Record

SP - 1

EP - 10

BT - Demand Forecasting, Volume 1

PB - National Research Council

ER -