Data driven methods for effective micromobility parking. (June 2021) - Sandoval, Ricardo; Van Geffen, Caleb; Wilbur, Michael; Hall, Brandon; Dubey, Abhishek; Barbour, William; Work, Daniel B.
Highlights: Clustering historical trip data is used to identify high-demand parking locations for shared urban mobility devices. A case study in Nashville, Tennessee, USA, shows that 20 parking locations is sufficient to capture 1 in 4 trips. Built environment factors such as sidewalk width are used to prioritize parking locations. Prioritized parking addresses 300% more narrow-sidewalk trips by sacrificing 13% total trips served. 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.