Shared E-Scooter Practices in Birmingham, Alabama: Analyzing Usage, Patterns, and Determinants
2. Literature Review
After reviewing the available literature, a general idea about the shared micromobility usage and spatial distribution patterns was built up, and their relationships with other environmental and geographical variables were understood. In addition to that, different analysis methods from different studies shed light on the various approaches that might be useful in examining the shared micromobility data in depth. The small but substantial number of studies has significantly contributed to the formulation of a cohesive framework.
4.1. Descriptive Analysis Results
These results confirm that the shared e-scooters did not materialize their full potential during the pilot deployment and that initiatives should be implemented to help increase the utilization rate of available micromobility devices in Birmingham in the future. Such initiatives include marketing and education campaigns targeting potential users, membership fee incentives and discounts, and the strategic placement of corals close to land uses that may generate customer demand for service.
4.2. Spatial Density Distribution Analysis Results
4.3. Regression Analysis Results
The present analysis reveals that the predictor variables exhibit p-values that are low (<0.001), thereby indicating a strong correlation with the e-scooter trip count. The z values of this model are also large, suggesting that the independent values have more significant effects on the dependent variable. In this model, t(12–15), t(15–18), and spring, summer, and fall variables project comparatively higher z values.
This study analyzed one year’s worth of shared VEO e-scooter trip data from the shared micromobility pilot program in Birmingham, Alabama that took place in 2021–2022. The data analysis yielded various insights regarding the utilization of shared e-scooters, revealing discernible patterns and trends.
During the Birmingham pilot micromobility study, the operational utilization rate for shared VEO e-scooters was just 3.65%, and each e-scooter device averaged only 2.32 trips/day, thus indicating a reluctance of users to embrace this new transportation mode when it was first introduced. In order to increase future ridership, marketing and educational campaigns are needed to promote shared micromobility modes among target groups and inform potential users of the advantages and benefits of choosing shared e-scooters over other modes of transportation, especially for short trips. Moreover, membership discounts, expanded hours of operation, and investment in infrastructure improvements are recommended to further improve the convenience, access, and overall appeal of shared e-scooter use in the future.
Overall, the analysis of the Birmingham pilot shared VEO e-scooter data revealed valuable information on user preferences and behaviors related to e-scooter use in the Birmingham area. The findings and recommendations provided herein are expected to support future planning efforts as local transportation agencies and micromobility providers continue their efforts to enhance shared mobility services in the region and serve current and future customers. The research methods used in this study can be replicated at other medium-sized cities that are in the process of introducing shared e-scooter services or evaluating micromobility usage patterns and their impact on local traffic operations. In future research, additional regression models can be generated, taking into consideration (a) the origin and destination location characteristics including land uses and (b) the demographic characteristics of users including gender, age, health status, income level, and education level. Due to data constraints and time limitations, these models can not be generated in this study. Another proposed extension of this study is to address safety-related concerns but by looking into crash records and examining the frequency and severity of traffic incidents involving e-scooters. Additionally, a comparative analysis of shared e-scooter usage patterns from other medium-sized cities can be conducted to gain a more comprehensive understanding of the prevailing state of e-scooter users’ preferences and identify location-specific similarities and differences. It is also recommended that user surveys be conducted to document users’ perceptions of and attitudes toward shared e-scooter use before and after they have been exposed to this mode.
The future of shared micromobility calls for a proactive strategy for addressing any remaining actual or perceived deployment obstacles and facilitate the promotion of micromobility modes as safe, practical, fun, and effective alternatives to automobile use for short-range trips. The findings from this research and the above-mentioned suggestions generate new insights that key stakeholders can use to facilitate planning micromobility policies and improve deployment practices. These, in turn, will help create a future in which shared micromobility becomes a crucial component of an environmentally friendly multimodal urban transportation landscape.
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