Abstract
Vehicle emissions pose a significant challenge for cities worldwide, yet a comprehensive analysis of the relationship between mobility metrics and total vehicle emissions at a high resolution remains elusive. In this work, we introduce the Mobile Data Emission System (MODES), a pioneering framework that integrates various sources of individual mobility data on an unprecedented scale. Our model is validated with direct measurements from a network of high-density sensors analyzed before and during the COVID-19 pandemic shelter-in-place orders. MODES is used as a laboratory for scaling analysis. Informed by individual trips, we estimate the traffic CO2 emissions at a metropolitan scale with a combination of 3 accessible metrics: vehicle kilometers traveled (VKT), congestion levels, and vehicle efficiency. Given their ranges of variation, VKT has the greatest role in amplifying vehicular emissions up to 500%, followed by vehicle efficiency that would range from 20% to 300% of the average passenger combustion vehicles. In comparison, congestion amplifies vehicle emissions of individual travels by up to 50%. We confirm that cities in the Bay Area with high population density are consistently characterized by low per-person VKT. Nevertheless, high population density comes at the expense of increased congestion. Since VKT is the governing factor, overall densifying of the urban landscape reduces transportation emissions despite its impacts on congestion.
