Innovative Applications of O.R.Simulation-based estimation of the real demand in bike-sharing systems in the presence of censoring
Section snippets
Introduction and background
A bike-sharing system is a network of GPS-enabled bicycles distributed around a city for rent as an active form of public transportation. There are two primary types of bike-sharing systems: stationed and stationless. In a stationed system, customers can pick up and drop off bikes only at designated stations, while in a stationless system, bikes can be picked up and dropped off anywhere. These systems can play an important role in reducing traffic congestion and carbon emissions, and improving
Preliminaries
In the remainder of the paper, the focus is primarily on estimating the demand for bikes, noting that the analysis would be similar for demand for docks as discussed in Section 6. The components discussed in this section are used extensively throughout the paper.
How Do IPT and CIAT distributions differ?
While it is clear that bike inter-pickup time (IPT) and actual customer inter-arrival time (CIAT) distributions may differ due to censoring, a formal analysis is needed to determine how the two may differ – an important question that the current literature leaves unanswered. This section investigates this question.
Simulation experiments are performed for different combinations of CIAT and BIAT distribution families selected based on a preliminary analysis of real-world data from the CitiBike
The demand estimation problem
Before defining the demand estimation problem, it is important to note that the analyst first needs to determine whether sufficient valid observations can be extracted from the data, and if so, existing data filtering approaches should be used to estimate the true demand. However, if this is not feasible due to lack of valid data (as a result of insufficient supply and high demand) and/or risk of mixing different demand patterns (as discussed above), then the demand estimation problem can be
Real-world application
We consider station 519 in the CitiBike system in the interval between 8:00 AM and 9:00 AM on weekdays in February 2018. Since data on disabled bikes and docks are available, a bike availability threshold of 0 is used to determine VIPT observations. On a typical weekday in that month, bike demand for the station starts to increase at around 6 AM, and with little or no rebalancing performed between 6:00 AM and 8:00 AM, the station has only a few functional bikes at 8:00 AM almost every weekday.
Discussion and conclusions
This paper provides a formal analysis on when and how bike/dock usage data censor the true demand for these resources in bike sharing systems. For situations where commonly used data filtering approaches in the bike-sharing literature are not applicable due to lack of valid historical demand data, the paper introduces a novel iterative methodology combining discrete-event simulation, nonparametric bootstrapping tests, and subset selection to harness the partial information on the underlying
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