Estimating Vehicle Fuel Consumption and Emissions Using GPS Big Data

Zihan Kan, Luliang Tang, Mei-Po Kwan and Xia Zhang
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Zihan Kan: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Luliang Tang: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Mei-Po Kwan: Department of Geography & Geographic Information Science, University of Illinois at Urbana-Champaign, 1301 W Green Street, Urbana, IL 61801, USA
Xia Zhang: School of Urban Design, Wuhan University, Wuhan 430070, China

IJERPH, 2018, vol. 15, issue 4, 1-23

Abstract: The energy consumption and emissions from vehicles adversely affect human health and urban sustainability. Analysis of GPS big data collected from vehicles can provide useful insights about the quantity and distribution of such energy consumption and emissions. Previous studies, which estimated fuel consumption/emissions from traffic based on GPS sampled data, have not sufficiently considered vehicle activities and may have led to erroneous estimations. By adopting the analytical construct of the space-time path in time geography, this study proposes methods that more accurately estimate and visualize vehicle energy consumption/emissions based on analysis of vehicles’ mobile activities ( MA ) and stationary activities ( SA ). First, we build space-time paths of individual vehicles, extract moving parameters, and identify MA and SA from each space-time path segment (STPS). Then we present an N-Dimensional framework for estimating and visualizing fuel consumption/emissions. For each STPS, fuel consumption, hot emissions, and cold start emissions are estimated based on activity type, i.e., MA , SA with engine-on and SA with engine-off. In the case study, fuel consumption and emissions of a single vehicle and a road network are estimated and visualized with GPS data. The estimation accuracy of the proposed approach is 88.6%. We also analyze the types of activities that produced fuel consumption on each road segment to explore the patterns and mechanisms of fuel consumption in the study area. The results not only show the effectiveness of the proposed approaches in estimating fuel consumption/emissions but also indicate their advantages for uncovering the relationships between fuel consumption and vehicles’ activities in road networks.

Keywords: fuel consumption; emissions estimation; GPS trace; big data (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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