Research Papers: Design for Manufacture and the Life Cycle

Predicting Demand of Distributed Product Service Systems by Binomial Parameter Mapping: A Case Study of Bike Sharing Station Expansion

[+] Author and Article Information
Bryan C. Watson

Department of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: BCWatson@gatech.edu

Cassandra Telenko

Department of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: cassandra@gatech.edu

1Corresponding author.

This paper originally appeared as IMECE2018-87865 and is updated and revised.

Contributed by the Design for Manufacturing Committee of ASME for publication in the Journal of Mechanical Design. Manuscript received September 17, 2018; final manuscript received March 26, 2019; published online May 14, 2019. Assoc. Editor: Harrison M. Kim.

J. Mech. Des 141(10), 101701 (May 14, 2019) (12 pages) Paper No: MD-18-1695; doi: 10.1115/1.4043366 History: Received September 17, 2018; Accepted March 26, 2019

Quantitative approaches for estimating user demand provide a powerful tool for engineering designers. We hypothesized that estimating binomial distribution parameters n (user population size) and p (user population product affinity) from historical user data can predict demand in new situations for distributed product service systems. Distributed product service systems allow individuals to use shared products at different geographic locations as opposed to owning them. This approach is demonstrated on a major bike-sharing system (BSS) expansion. BSSs position rental bikes around a city in docks at prescribed locations. BSS operators must predict the rider demand when sizing new docking stations, but current demand estimation methods may not be suitable for distributed systems. The main contribution of this paper is the development and application of a revealed preference demand estimation method for distributed product service systems. While much current research seeks to solve distributed system operational problems, we estimate the user population characteristic to provide insight into the initial installation design problem. We introduce the use of spatial surface plots to extrapolate binomial parameters n and p over the service area. These surfaces allow more accurate prediction of relative ridership levels at new station locations. By utilizing Spearman's rho as a comparison benchmark, our approach yields a stronger correlation between our prediction and the observed new station utilization (rho = 0.83, stations = 46, p < 0.01) than the order implemented by the BSS operator (rho = 0.59, stations = 46, p < 0.01).

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Fig. 1

Possible n, p combinations that yield E[x] = µ = 3

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Fig. 2

Chicago 2014 and 2015 BSS subscriber ridership

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Fig. 3

2014 and analyzed 2015 stations with installed sizing

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Fig. 4

3D and contour plot of the n and p surfaces

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Fig. 5

3D and contour plot of the average n and p surfaces

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Fig. 6

Predicted 2015 hourly ridership and station capacity versus observed hourly ridership

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Fig. 7

Monte Carlo sensitivity analysis results



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