A Simple Sinuosity-Based Method using GPS data to Support Mitigation Policies for Public Buses GHG Emissions

TL;DR

This paper presents a simple, GPS data-driven method using sinuosity to estimate urban bus GHG emissions, enabling detailed spatial-temporal analysis to support climate mitigation policies.

Contribution

It introduces a novel, replicable sinuosity-based algorithm for GHG estimation from GPS data, validated with real-world data from Rio de Janeiro.

Findings

GPS-based estimates align with fuel consumption data

Method provides finer spatial-temporal emission details

Policy insights can be derived from the estimates

Abstract

It is clear by now that climate change mitigation relies on our capacity to guide urban systems towards a low-carbon phase and that the urban transportation sector plays a major role in this transition. It is estimated that around 30% of total CO2 emissions worldwide come from the urban transportation sector. Regardless of its importance, detailed estimations of transport-related emissions in cities are still rare to find, hindering our capacity to understand and reduce them. This work aims to develop a replicable and fast method for GHG estimation from GPS (Global Positioning System) data and to introduce a simple sinuosity-based algorithm for such. We applied the method for 1 year of GPS data in the city of Rio de Janeiro. Our results were compared to top-down estimations from fuel consumption and proved to be valid after a simple data filling process. Our GPS-based approach allowed for much finer spatial and temporal descriptions of emissions and we further showed possible policy insights that can be extracted from the estimated emissions based on the proposed method.