Regional Emission and Health Impact Assessment of Implementation of Micromobility: An El Paso, TX Case Study 

Abstract
With the rapid population growth in urban areas, micromobility has become an attractive mode of transportation for short or first/last mile trips in recent years. Micromobility, ranging from human-powered bikes to electric scooters, shared or personal, docked or dockless, offers a good solution and can be an efficient substitution for short vehicle trips. Recently, cities have initiated programs and policies to promote micromobility and transform urban travel behavior despite facing practical challenges in real-world implementation. The growing popularity of micromobility warrants a comprehensive assessment of its potential benefits to the triple bottom line of urban sustainability.

This study shed light on the regional role of micromobility, considering all three aspects of sustainability. The project simulated the substitution of short car trips in dense urban core areas with micromobility options on a dynamic traffic assignment (DTA) platform such that local congestion relief can be examined in the regional context for El Paso, Texas. The regional traffic impacts were further translated into economic impacts measured in revenue and medical expenses, environmental impacts measured in greenhouse and air pollutant emissions, and societal impacts measured in public health outcomes attributable to traffic-related air pollution and related environmental justice implications.

This study conducted the analysis using the Platform to Assess Transportation, Health, and Sustainability (PATHS), previously known as TEMPO, developed by the research team. PATHS is a cloud-based platform for automating a suite of models. Within PATHS, a DTA model simulates vehicle activity to estimate the vehicle mile traveled and speed for each link in the system. MOVES-Matrix, a multi-dimensional emission rate database generated from US Environmental Protection Agency’s (EPA) MOtor Vehicle Emission Simulator (MOVES), is used to quantify the greenhouse gas emissions and air pollutants for each link, including CO2, PM2.5, PM10, NOX, and VOC. The resultant pollutant emissions are converted to concentrations using the EPA’s AERMOD system, and the attributable health impact is computed. The modeling platform was applied to El Paso, Texas. The levels of micromobility adoption were formulated based on two critical travel behavior variables: 1) the average distance of a trip taken via a micromobility mode, 2) the probability of a very short car trip being replaced by a micromobility trip. Car trips that originated and terminated in high traffic zones (downtown and university campus) were removed from the roster of vehicle trips with probabilities that depended exponentially on the trip length. Three scenarios (low, medium, and high adoption) were developed and compared to the base scenario, zero micromobility activity. The results have shown no significant improvement in regional congestion and emissions despite the localized emissions, pollutant concentration, and respiratory disease benefits, showing the effectiveness of the micromobility adoption at the local level. Furthermore, the concentrations and attributable health impacts are analyzed and visualized at the census block level for any equity concerns, and aggregated to the regional level to examine potential savings in medical care expenses.