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

2024-12-15

BigData Congress [Services Society]

AI Summary

This study addresses the challenge of real-time monitoring and coordinated control of city-scale carbon emissions. We propose a data-driven macroscopic emission Macroscopic Fundamental Diagram (eMFD) modeling framework leveraging probe vehicle trajectories. By integrating high-precision GPS trajectories, road attributes, land-use patterns, and vehicle-specific characteristics, we construct, for the first time, a machine learning–based eMFD model across a large-scale U.S. urban road network—explicitly capturing spatial heterogeneity in the emission–flow relationship. Unlike conventional homogeneous assumptions, our model enables spatially differentiated traffic management policies tailored to individual zones. Empirical evaluation demonstrates a 23.6% reduction in carbon emission estimation error compared to baseline methods and supports optimized OD demand allocation to jointly mitigate congestion and emissions. The framework provides a scalable methodology and practical toolkit for dynamic, spatially resolved urban carbon governance.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Security and Privacy: Large-scale security measurements

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.

Problem

• Modeling urban traffic emissions using probe vehicle data
• Developing machine learning-based macroscopic emission fundamental diagrams
• Optimizing traffic management to reduce network-wide emissions

Innovation

• Machine learning predicts network-wide emission rates
• Probe vehicle data enables large-scale emission modeling
• Data-driven framework analyzes location-dependent emission factors