Understanding the use of bicycle sharing systems with statistics
Researchers analyze the usage patterns of bicycles in four large cities
in USA to make bicycle sharing systems more efficient

Date:
April 4, 2022
Source:
Tokyo University of Science
Summary:
Though bicycle sharing systems (BSSs) are popular in many
big cities, it is necessary to actively rebalance the number of
bicycles across the various ports with optimization algorithms. In
a recent study, researchers statistically analyzed the bicycle usage
patterns in four real-world BSSs to obtain realistic benchmarks for
testing these algorithms. Their findings can make BSS rebalancing
more efficient through an understanding of the social dynamics of
human movement.



FULL STORY ========================================================================== Bicycle sharing systems (BSSs) are a popular transport system in many
of the world's big cities. Not only do BSSs provide a convenient
and eco-friendly mode of travel, they also help reduce traffic
congestion. Moreover, bicycles can be rented at one port and returned
at a different port. Despite these advantages, however, BSSs cannot rely
solely on its users to maintain the availability of bicycles at all ports
at all times. This is because many bicycle trips only go in one direction, causing excess bicycles at some ports and a lack of bicycles in others.


==========================================================================
This problem is generally solved by rebalancing, which involves
strategically dispatching special trucks to relocate excess bicycles
to other ports, where they are needed. Efficient rebalancing, however,
is an optimization problem of its own, and Professor Tohru Ikeguchi and
his colleagues from Tokyo University of Science, Japan, have devoted much
work to finding optimal rebalancing strategies. In a study from 2021,
they proposed a method for optimally rebalancing tours in a relatively
short time. However, the researchers only checked the performance of
their algorithm using randomly generated ports as benchmarks, which may
not reflect the conditions of BSS ports in the real world.

Addressing this issue, Prof. Ikeguchi has recently led another study,
together with PhD student Ms. Honami Tsushima, to find more realistic benchmarks. In their paper published in Nonlinear Theory and Its
Applications, IEICE, the researchers sought to create these benchmarks by statistically analyzing the actual usage history of rented and returned bicycles in real BSSs. "Bike sharing systems could become the preferred
public transport system globally in the future. It is, therefore, an
important issue to address in our societies," Prof. Ikeguchi explains.

The researchers used publicly available data from four real BSSs located
in four major cities in USA: Boston, Washington DC, New York City,
and Chicago.

Save for Boston, these cities have over 560 ports each, for a total
number of bicycles in the thousands.

First, a preliminary analysis revealed that an excess and lack of bicycles occurred across all four BSSs during all months of the year, verifying
the need for active rebalancing. Next, the team sought to understand the temporal patterns of rented and returned bicycles, for which they treated
the logged rent and return events as "point processes." The researchers independently analyzed both point processes using three approaches,
namely raster plots, coefficient of variation, and local variation.

Raster plots helped them find periodic usage patterns, while coefficient
of variation and local variation allowed them to measure the global
and local variabilities, respectively, of the random intervals between consecutive bicycle rent or return events.

The analyses of raster plots yielded useful insights about how the four
BSSs were used in their respective cities. Most bicycles were used during daytime and fewer overnight, producing a periodic pattern. Interestingly,
from the analyses of the local variation, the team found that usage
patterns were similar between weekdays and weekends, contradicting
the results of previous studies. Finally, the results indicated that
the statistical characteristics of the temporal patterns of rented and
returned bikes followed a Poisson process - - a widely studied random distribution -- only in New York City. This was an important find,
given the original objective of the research team. "We can now create
realistic benchmark instances whose temporal patterns of rented and
returned bicycles follow the Poisson process. This, in turn, can help
improve the bicycle rebalancing model we proposed in our earlier work," explains Prof.

Ikeguchi.

Overall, this study paves the way to a deeper understanding of how
people use BSSs. Moreover, through further detailed analyses, it should
be possible to gain insight into more complex aspects of human life,
as Prof. Ikeguchi remarks: "We believe that the analysis of BSS data
will lead not only to efficient bike sharing but also to a better
understanding of the social dynamics of human movement." In any case,
making BSSs a more efficient and attractive option will, hopefully,
make a larger percentage of people choose the bicycle as their preferred
means of transportation.


========================================================================== Story Source: Materials provided by Tokyo_University_of_Science. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Honami Tsushima, Tohru Ikeguchi. Statistical analysis of usage
history of
bicycle sharing systems. Nonlinear Theory and Its Applications,
IEICE, 2022; 13 (2): 355 DOI: 10.1587/nolta.13.355 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/04/220404105652.htm

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