Project Summary
Port operations require a large number of diesel engines to power trucks, trains, ships, and cargo-handling equipment. Diesel engines contribute to vast amounts of air pollution that impairs the health of those residing or working in proximity to ports. Further, diesel exhaust emissions contribute substantially to regional air pollution. For years, traffic operations along highways have disproportionately burdened low-income and minority communities in the form of exposure to air pollutants and their associated health impacts. To address this issue, improved traffic safety and air quality are the primary objectives of the I-710 corridor project, a freeway improvement project along the I-710 freeway in Los Angeles County between Ocean Boulevard and State Route 60 (SR-60), which covers approximately 18 miles of the I-710. This study estimates air pollution and associated health effects of the fleet replacement of diesel trucks for autonomous trucks. The primary objective is to quantify the possible benefits associated with the incorporation of autonomous vehicles within the study area in the context of environmental justice. This project incorporates a microscopic traffic simulation to study traffic improvements associated with the addition of lanes to the I-710 and the replacement of San Pedro Bay ports diesel trucks with autonomous (self-driving), zero emission, trucks. The impact on traffic of autonomous trucks was simulated using TransModeler 5.0, which allows simulating autonomous vehicles with Cooperative Adaptive Cruise Control (CACC). CACC is a subset of the broader class of automatic vehicles speed and control systems. Emission estimates of nitrogen oxides (NOx) and particulate matter (PM2.5 and PM10) were then quantified using the Operating Mode (OpMode) lookup tables based on EPA’s MOVES model. Results show that autonomous truck operations could allow a 90% increase in demand while providing a 13% improvement in the traffic performance of port-related vehicles while reducing by 70% NOx, PM2.5, and PM10 emissions within the study area.
