International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 115–119, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-115-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4, 115–119, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-115-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Sep 2018

19 Sep 2018

EXPLORATION ON QUANTIFYING CARBON DIOXIDE (CO2) EMISSION FROM ROAD TRAFFIC IN MEGACITY

R. Cong, M. Saito, R. Hirata, A. Ito, and S. Maksyutov R. Cong et al.
  • National Institute for Environmental Studies, Center for Global Environmental Research, 305-8506 Tsukuba, Japan

Keywords: CO2 emissions, Vehicle emissions, Traffic census, Urban traffic, GIS, Tokyo emissions

Abstract. As the increasing concern for climate change, quantification on greenhouse gas (GHG) emissions in urban scale has been a key component for local climate actions. To explore an approach on estimating the GHG emissions closely to the real-world condition, in this paper, we make efforts on counting the carbon dioxide (CO2) emissions from road traffic in Tokyo. The road traffic emissions mapping is achieved by linking spatial road line data with detailed activity data (traffic census). Through the data processing by Geographic Information System technique, the emissions of each road segment are estimated basing on the daily average traffic amount and speed of vehicles on each road segment. As our estimation, the CO2 emissions from road traffic of Tokyo in 2015 are about 16,323 Gg. The highlight points mainly refer to linking the traffic census information for the observed road segments on map and allocation efforts for unserved ones. As the limited amount of observation points in traffic census, accurate estimation for unobserved road segments is a challenge. Our approach overcomes it by assumption of a familiar traffic condition for all road segments in the same sub-city area. This approach could simulate the traffic patterns closely to the real traffic condition so that it will more effectively support the emission mitigation policies on road traffic for local climate.