Dockless mobility has been the biggest disruptive force in the shared mobility industry solving the “first-last” mile issue. With their high adoption levels combined with little to no regulation regarding their usage, these users have been driving along with motorized vehicles exposing them to major concerns. Exposure to traffic-related air pollution (TRAP) is an important factor due to their direct exposure to vehicular exhaust and increased breathing during riding. This study aims to answer key research questions related to understanding the travel behavior patterns and exposure to traffic pollution for a sample of e-scooter users in Austin, Texas. The travel behavior patterns were evaluated through geospatial analysis of 3.4 million e-scooter trip data collected in 2018 and an online survey. The analysis identified the most important origins and destinations, and peak ridership time, and the survey helped in understanding the demographics, and key factors influencing the use of e-scooters. A chain of modeling components involving estimation of traffic activities, emissions, meteorology, and pollutant dispersion is used to model the pollutant concentrations. The e-scooter exposure to TRAP is obtained by integrating the spatial-temporal dynamics of pollutant concentrations with the real-time commuting patterns of e-scooters. This is one of the early studies focusing on both the operational characteristics of e-scooters and evaluating their exposure levels to traffic-related air pollution based on their ridership patterns. The findings are useful for policymakers and planners when planning for infrastructure changes air pollution control measures, incentive programs, and policies to motivate shared mobility.
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