Data driven methods for effective micromobility parking

Ricardo Sandoval, Caleb Van Geffen, Michael Wilbur, Brandon Hall, Abhishek Dubey, William Barbour, Daniel B. Work

Transportation Research Interdisciplinary Perspectives 2021

PDF DOI

Why This Matters

Shared electric scooter systems create significant urban mobility benefits but generate management challenges, particularly regarding parking infrastructure placement and accessibility. Simply maximizing trip capture without considering environmental constraints can create conflicts with infrastructure accessibility standards. This work is innovative because it demonstrates how to balance operational efficiency with urban equity concerns, showing that data-driven approaches can improve both the effectiveness and fairness of micromobility infrastructure planning.

What We Did

This work proposes a data-driven methodology for locating and prioritizing shared electric scooter (SES) parking facilities using clustering algorithms and demand analysis. The approach addresses the problem of finding optimal parking locations by maximizing trip capture while considering environmental factors such as sidewalk width for ADA compliance. Case studies in Nashville and on the Vanderbilt campus demonstrate how clustering methods can identify high-demand parking zones and how incorporating infrastructure constraints affects facility placement.

Key Results

The analysis demonstrates that demand-based clustering can effectively identify optimal parking locations, capturing 300% more problematic narrow-sidewalk trips when infrastructure constraints are incorporated, with only a 13% trade-off in overall trip capture. Empirical results provide city planners with quantitative guidance on how many parking facilities are needed to serve scooter demand and how capacity should be allocated across locations. The findings show that considering built environment factors significantly improves both demand coverage and ADA compliance.

Full Abstract

Cite This Paper

@article{sandoval2021data,
  author = {Sandoval, Ricardo and {Van Geffen}, Caleb and Wilbur, Michael and Hall, Brandon and Dubey, Abhishek and Barbour, William and Work, Daniel B.},
  journal = {Transportation Research Interdisciplinary Perspectives},
  title = {Data driven methods for effective micromobility parking},
  year = {2021},
  issn = {2590-1982},
  pages = {100368},
  volume = {10},
  abstract = {In this work, we propose a data-driven method to use proven clustering algorithms for establishing shared electric scooter (SES) parking locations and assessing their anticipated utilization. We first address the problem of finding locations for a given number of parking facilities, based pur0ely on demand, that maximize the number of trips that would likely be parked at these facilities. We then formulate an enhanced version of the SES parking facility problem in which exogenous environmental factors are considered, such as sidewalk width. Parking SESs on narrow sidewalks raises accessibility concerns for other users of this infrastructure and capturing these trips in dedicated parking facilities is a valid priority to trade off with pure demand maximization. These methods are demonstrated in two case studies, which use a large SES dataset from Nashville, Tennessee, USA. We provide empirical results on how many facilities are needed to serve demand of SESs and necessary capacity allocation of the facilities. When the methodology considers sidewalk width in facility placement, the refined parking locations can address 300% more problematic trips parked along narrow sidewalks, with only a nominal sacrifice, around 13%, in the overall number of trips served.},
  contribution = {minor},
  doi = {https://doi.org/10.1016/j.trip.2021.100368},
  keywords = {micromobility, shared electric scooters, parking facility location, clustering, urban planning, accessibility, demand analysis},
  tag = {transit},
  url = {https://www.sciencedirect.com/science/article/pii/S2590198221000750}
}
Quick Info
Year 2021
DOI https://doi.org/10.1016/j.t...
Keywords
micromobility shared electric scooters parking facility location clustering urban planning accessibility demand analysis
Research Areas
transit planning
Search Tags

Data, driven, methods, effective, micromobility, parking, shared electric scooters, parking facility location, clustering, urban planning, accessibility, demand analysis, transit, planning, 2021, Sandoval, Van Geffen, Wilbur, Hall, Dubey, Barbour, Work