Characterize Shared E-scooter Travel Patterns using Comprehensive Travel Data in Charlotte, NC

Abstract: Shared e-scooters have gained popularity as a micro-mobility option in urban areas and have the potential to improve transportation convenience and efficiency by replacing walking, and to enhance energy and environmental benefit by saving car driving trips. With the success of e-scooter services, a huge amount of e-scooter travel and operation data has been accumulated. This study aims to process and analyze the e-scooter data collected from Charlotte, NC, to understand the detailed e-scooter user travel and trip-level activity through a series of descriptive and temporal-spatial analyses. With that, Charlotte’s micromobility travel patterns and insights can be identified. Furthermore, e-scooter data has been spatially aligned and comprehensively analyzed with a variety of travel-related data, such as land use, demographic census block groups, and public transit data at different resolution levels (e.g., micro- and macro-levels) to further explore the e-scooter use cases and the role of e-scooters in first-and-last-mile (FLM) transport in Charlotte. The micro-level analysis focuses on the ride pickup or drop-off (PUDO) points, transit accessibility, and land use characteristics. The micro-level e-scooter trips were further aggregated into the census block groups in a macro-level analysis, which provides insights into E-scooter’s travel demand patterns, such as origin-destination matrix, production-attraction of census block groups, and accessibility to e-scooter pickup and drop-off locations. The analyses offer unique sociodemographic perspectives on the intricate dynamics of e-scooter mobility in the Charlotte metropolitan area, which have the potential to be applied to enhance urban transportation services, such as transit accessibility, operations of microtransit and e-bikes. The comprehensive analysis approach and mobility patterns of the case study are essential to create an urban environment that is more accessible, efficient, and inclusive for both locals and tourists.