Macroscopic Emission Modeling of Urban Traffic Using Probe Vehicle Data: A Machine Learning Approach

TL;DR

This paper introduces a machine learning approach to model urban traffic emissions at a network level using probe vehicle data, enabling real-time emission monitoring and optimized traffic management.

Contribution

It is the first to apply machine learning to large-scale probe vehicle data for network-wide emission modeling in U.S. urban areas, providing data-driven eMFDs.

Findings

Generated data-driven eMFDs for U.S. cities.

Revealed location dependence of emissions on infrastructure and land use.

Enabled real-time emission estimation and traffic management insights.

Abstract

Urban congestions cause inefficient movement of vehicles and exacerbate greenhouse gas emissions and urban air pollution. Macroscopic emission fundamental diagram (eMFD)captures an orderly relationship among emission and aggregated traffic variables at the network level, allowing for real-time monitoring of region-wide emissions and optimal allocation of travel demand to existing networks, reducing urban congestion and associated emissions. However, empirically derived eMFD models are sparse due to historical data limitation. Leveraging a large-scale and granular traffic and emission data derived from probe vehicles, this study is the first to apply machine learning methods to predict the network wide emission rate to traffic relationship in U.S. urban areas at a large scale. The analysis framework and insights developed in this work generate data-driven eMFDs and a deeper understanding of their location dependence on network, infrastructure, land use, and vehicle characteristics, enabling transportation authorities to measure carbon emissions from urban transport of given travel demand and optimize location specific traffic management and planning decisions to mitigate network-wide emissions.