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Estimating Vehicle Fuel Consumption and Emissions Using GPS Big Data.

Zihan Kan1, Luliang Tang2, Mei-Po Kwan3,4

  • 1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China. kzh@whu.edu.cn.

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PubMed
Summary
This summary is machine-generated.

This study introduces a new method to accurately estimate vehicle energy consumption and emissions by analyzing mobile and stationary activities. The approach achieves 88.6% accuracy, improving urban sustainability insights.

Keywords:
GPS tracebig dataemissions estimationfuel consumption

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Area of Science:

  • Environmental Science
  • Transportation Engineering
  • Data Science

Background:

  • Vehicle energy consumption and emissions negatively impact urban health and sustainability.
  • Existing methods for estimating emissions from GPS data often overlook crucial vehicle activities, leading to inaccuracies.
  • Accurate estimation requires considering both moving and stationary vehicle behaviors.

Purpose of the Study:

  • To develop and validate a novel method for accurately estimating and visualizing vehicle energy consumption and emissions.
  • To improve the precision of emission estimations by incorporating detailed analysis of vehicle activities.
  • To provide a framework for understanding the relationship between vehicle activities and their environmental impact.

Main Methods:

  • Utilized time geography's space-time path construct to model individual vehicle movements.
  • Identified and categorized vehicle activities into mobile activities (MA) and stationary activities (SA) from space-time path segments (STPS).
  • Developed an N-Dimensional framework to estimate fuel consumption, hot emissions, and cold start emissions based on activity type (MA, SA with engine-on, SA with engine-off).

Main Results:

  • The proposed method achieved an estimation accuracy of 88.6% in a case study.
  • Successfully estimated and visualized fuel consumption and emissions for both individual vehicles and a road network using GPS data.
  • Analysis revealed patterns and mechanisms of fuel consumption linked to specific vehicle activities on different road segments.

Conclusions:

  • The developed approach significantly enhances the accuracy of vehicle energy consumption and emissions estimation.
  • The framework effectively visualizes environmental impacts and uncovers detailed relationships between vehicle activities and fuel consumption in road networks.
  • This study offers valuable insights for urban planning and sustainable transportation strategies.