Uncovering the spatiotemporal patterns of CO2 emissions by taxis based on Individuals' daily travel

Pengxiang Zhao, Mei Po Kwan, Kun Qin

Research output: Contribution to journalArticlepeer-review

Abstract

Traffic-related carbon dioxide (CO2) emissions have become a major problem in cities. Especially, the CO2 emissions induced by taxis account for a high proportion in total CO2 emissions. The availability of taxi trajectory data presents new opportunities for addressing CO2 emissions induced by taxis. Few previous studies have analyzed the impact of human trips on CO2 emissions. This paper investigates trip-related CO2 emission patterns based on individuals' travel behavior using taxi trajectory data. First, we propose a trip purpose inference method that takes into account the spatiotemporal attractiveness of POIs to divide human trips into different types. Further, we reveal the spatiotemporal patterns of CO2 emissions from various types of trips, including temporal regularity and periodicity as well as spatial distribution of “black areas”. Finally, comparative analysis of CO2 emissions for different kinds of trips based on trip behavior is conducted using three variables, namely trip distance, trip duration and trip speed. This study is helpful for us to understand how to make travel and cities more sustainable through modifying people's trip behaviors or taxi trips.

Original languageEnglish (US)
Pages (from-to)122-135
Number of pages14
JournalJournal of Transport Geography
Volume62
DOIs
StatePublished - Jun 2017

Keywords

  • CO emissions
  • Human trips
  • Spatiotemporal patterns
  • Taxi trajectory data
  • Trip behavior

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Transportation
  • Environmental Science(all)

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