Modelling shopping transport energy performance to explore low carbon potentials

Ming Bai, Susan Krumdieck

Research output: Contribution to journalArticle

Abstract

Using shopping activity as an example, this paper presents a probabilistic model that can be used to estimate shopping transport energy consumption in the absence of empirical data, and analyse the potential of reducing shopping trips by car. Based on a combination of the Huff and Gravity models, some new metrics were developed to quantify the shopping activities. The properties of shopping facilities, spatial distribution, travel distance and travel patterns are the key part of model to determine how much transport energy is consumed. With the assistance of a GIS system, an international comparison between two cities in New Zealand and China was conducted using this model to investigate the differences and relevant factors that can affect transport energy use. The results showed that the residential density plays a critical role in reducing shopping transport energy use. The majority of residents living in these cities could adapt to non-motorised trips for essential shopping activities.

Original languageEnglish
Pages (from-to)73-84
Number of pages12
JournalRoad and Transport Research
Volume25
Issue number1
Publication statusPublished - Mar 2016

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering

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