Micromobility implementation challenges and opportunities: Analysis of e-scooter parking and high-use corridors

•Provides method for analyzing areas of high parking demand.•Identifies e-scooter vendor launching sites.•Compares techniques for analyzing roadway segments of high micromobility demand.•Discusses policy implications and potential environmental impacts of micromobility.•Examines the accuracy of shortest path models in predicting e-scooter paths. To enhance the effectiveness of dockless shared micromobility vehicle services, address challenges created by these services, and achieve better implementation performance, cities need efficacious strategies to tackle two major challenges: parking demand and infrastructure improvements. This paper offers two techniques for analyzing areas of high parking demand and roadway segments of high micromobility vehicle demand. The proposed processes support efforts to assign free-floating parking and reduce vehicle clutter across cities, and assist transportation planners in identifying locations for road infrastructure improvement and ordinance enforcement. An unsupervised learning approach using vendor launching site analysis is used to appropriately locate dockless vehicle parking zones. Furthermore, this paper compares three methods for identifying high-demand corridors. Two shortest path models are generated to predict trip paths for trip data without trajectories, considering rider route preferences and infrastructure types. A Simplified Matching Heuristic (SMH) that uses trip data with trajectories, is developed herein to match trip trajectory data to the network street links. These analytical processes are applied in a case study of Dallas, TX. Results show that methods using shortest path models may poorly predict the actual path taken by e-scooter users. A discussion of policy implications for micromobility planning and management and potential environmental impacts of emerging transportation technologies are also presented.