Usage and Temporal Patterns of Public Bicycle Systems: Comparison among Points of Interest

Abstract

The public bicycle system is an important component of “mobility as a service” and has become increasingly popular in recent years. To provide a better understanding of the station activity and driving mechanisms of public bicycle systems, the study mainly compares the usage and temporal characteristics of public bicycles in the vicinity of the most common commuting-related points of interest and land use. It applies the peak hour factor, distribution fitting, and K-means clustering analysis on station-based data and performs the public bicycles usage and operation comparison among different points of interest and land use. The following results are acquired: (1) the demand type for universities and hospitals in peaks is return-oriented when that of middle schools is hire-oriented; (2) bike hire and return at metro stations and hospitals are frequent, while only the rental at malls is; (3) compared to middle schools and subway stations with the shortest bike usage duration, malls have the longest duration, valued at 18.08 min; and (4) medical and transportation land, with the most obvious morning return peak and the most concentrated usage in a whole day, respectively, both present a lag relation between bike rental and return. In rental-return similarity, the commercial and office lands present the highest level.

A Systematic Literature Review on Machine Learning in Shared Mobility

“…However, compared to other types of shared mobility, research in the area of demand analysis has been more focused on ride-sharing, and, especially, docked bike-sharing. The most widely used format in demand analysis is converting origin-destination information from trip data into spatio-temporal data, which is then supplemented by additional data such as weather [42,89,90,91], socio-economic [92,93,94], and environmental [95,96,97], to provide a more thorough understanding of demand patterns.…”

“…The primary data source for these analyses is OD trip data, especially when determining the number of vehicles [216,259,271]. Occasionally, this OD data is augmented with additional information such as landuse, infrastructure points-of-interest [90,96,193,264,265,273,277], or population statistics [266]. This enriched data proves especially valuable for studies on station locations, intermodal usage, and trip purposes.…”

Research on the Impact of COVID-19 on Micromobility Using Statistical Methods

“…In the world, shared scooters and bicycles are used with a station system or as a system that does not require a station [5,6]. A public bicycle system was compared in terms of its usage and temporal characteristics with public bicycles [7]. Each of the alternatives has its advantages and disadvantages when applied to urban mobility.…”