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).

References

References
1.
Fowkes
,
T.
, and
Preston
,
J.
,
1991
, “
Novel Approaches to Forecasting the Demand for New Local Rail Services
,”
Transp. Res. Part A Gen.
,
25
(
4
), pp.
209
218
.
2.
Mont
,
O. K.
,
2002
, “
Clarifying the Concept of Product-Service System
,”
J. Clean Prod.
,
10
(
3
), pp.
237
245
.
3.
Baines
,
T. S.
,
Lightfoot
,
H. W.
,
Evans
,
S.
,
Neely
,
A.
,
Greenough
,
R.
,
Peppard
,
J.
,
Roy
,
R.
,
Shehab
,
E.
,
Braganza
,
A.
,
Tiwari
,
A.
,
Alcock
,
J. R.
,
Angus
,
J. P.
,
Basti
,
M.
,
Cousens
,
A.
,
Irving
,
P.
,
Johnson
,
M.
,
Kingston
,
J.
,
Lockett
,
H.
,
Martinez
,
V.
,
Michele
,
P.
,
Tranfield
,
D.
,
Walton
,
I. M.
, and
Wilson
,
H.
,
2007
, “
State-of-the-Art in Product-Service Systems
,”
Proc. Inst. Mech. Eng. Part B J. Eng. Manuf.
,
221
(
10
), pp.
1543
1552
.
4.
Erkoyuncu
,
J. A.
,
Roy
,
R.
,
Shehab
,
E.
, and
Cheruvu
,
K.
,
2011
, “
Understanding Service Uncertainties in Industrial Product-Service System Cost Estimation
,”
Int. J. Adv. Manuf. Technol.
,
52
(
9–12
), pp.
1223
1238
.
5.
Veryzer
,
R. W.
, and
De Mozota
,
B. B.
,
2005
, “
The Impact of User-Oriented Design on New Product Development: An Examination of Fundamental Relationships
,”
J. Prod. Innov. Manag.
,
22
(
2
), pp.
128
143
.
6.
Abras
,
C.
,
Maloney-krichmar
,
D.
, and
Preece
,
J.
,
2004
, “
User-Centered Design
,”
Encyclopedia of Human-Computer Interaction
,
W
.
Bainbridge
, ed.,
Sage Publications
,
Thousand Oaks
.
7.
He
,
L.
,
Chen
,
W.
,
Hoyle
,
C.
, and
Yannou
,
B.
,
2012
, “
Choice Modeling for Usage Context-Based Design
,”
ASME J. Mech. Des.
,
134
(
3
), pp.
1
26
.
8.
Thomas
,
R. J.
,
1985
, “
Problems in Demand Estimation for a New Technology
,”
J. Prod. Innov. Manag.
,
2
(
3
), pp.
145
157
.
9.
ITDP
,
2013
, “
The Bike-Sharing Planning Guide
,” p.
152
. https://www.itdp.org/wp-content/uploads/2014/07/ITDP_Bike_Share_Planning_Guide.pdf. Accessed February 19, 2018.
10.
Kumar
,
V. P.
, and
Bierlaire
,
M.
,
2012
, “
Optimizing Locations for a Vehicle Sharing System
,”
Swiss Transport Research Conference (STRC)
,
Ascona, Switzerland
, pp.
1
30
.
11.
Hankey
,
S.
,
Lindsey
,
G.
,
Wang
,
X.
,
Borah
,
J.
,
Hoff
,
K.
,
Utecht
,
B.
, and
Xu
,
Z.
,
2012
, “
Estimating Use of Non-Motorized Infrastructure: Models of Bicycle and Pedestrian Traffic in Minneapolis, MN
,”
Landsc. Urban Plann.
,
107
(
3
), pp.
307
316
.
12.
Chen
,
H. Q.
,
Honda
,
T.
, and
Yang
,
M. C.
,
2013
, “
Approaches for Identifying Consumer Preferences for the Design of Technology Products: A Case Study of Residential Solar Panels
,”
ASME J. Mech. Des.
,
135
(
6
), p.
061997
.
13.
Wang
,
M.
, and
Chen
,
W.
,
2015
, “
A Data-Driven Network Analysis Approach to Predicting Customer Choice Sets for Choice Modeling in Engineering Design
,”
ASME J. Mech. Des.
,
137
(
7
), p.
071409
.
14.
Haaf
,
C. G.
,
Michalek
,
J. J.
,
Ross Morrow
,
W.
, and
Liu
,
Y.
,
2014
, “
Sensitivity of Vehicle Market Share Predictions to Discrete Choice Model Specification
,”
ASME J. Mech. Des.
,
136
(
12
), p.
121402
.
15.
Kang
,
C.
,
2016
, “
A Simulation Method to Estimate Nonparametric Distribution of Heterogeneous Consumer Preference From Market-Level Choice Data
,”
ASME J. Mech. Des.
,
138
(
12
), p.
121402
.
16.
Dias
,
G. M.
,
Bellalta
,
B.
, and
Oechsner
,
S.
,
2015
, “
Predicting Occupancy Trends in Barcelona’s Bicycle Service Stations Using Open Data
,”
IntelliSys 2015—Proceedings of 2015 SAI Intelligent Systems Conference
,
London
.
17.
Mahony
,
E. O.
, and
Shmoys
,
D. B.
,
2015
, “
Data Analysis and Optimization for (Citi) Bike Sharing
,”
AAAI'15 Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence
,
Austin, TX
, pp.
687
694
.
18.
Froehlich
,
J.
,
Neumann
,
J.
, and
Oliver
,
N.
,
2009
, “
Sensing and Predicting the Pulse of the City Through Shared Bicycling
,”
IJCAI International Joint Conference on Artificial Intelligence
,
Pasadena, CA
.
19.
Montoliu
,
R.
,
2012
, “
Discovering Mobility Patterns on Bicycle-Based Public Transportation System by Using Probabilistic Topic Models
,”
Ambient Intelligence – Software and Applications. Advances in Intelligent and Soft Computing
, Vol.
153
,
P
Novais
,
K
Hallenborg
,
D
Tapia
,
J
Rodriguez
, eds.,
Springer
,
Berlin, Heidelberg
, pp.
145
153
.
20.
Fishman
,
E.
,
Washington
,
S.
, and
Haworth
,
N.
,
2013
, “
Bike Share: A Synthesis of the Literature
,”
Transp. Rev.
,
33
(
2
), pp.
148
165
.
21.
Shaheen
,
S.
,
Guzman
,
S.
, and
Zhang
,
H.
,
2010
, “
Bikesharing in Europe, the Americas, and Asia
,”
Transp. Res. Rec. J. Transp. Res. Board
,
2143
, pp.
159
167
.
22.
National Association of City Transportation Officials
,
2016
,
NACTO Bike Share Station Siting Guide
,
National Association of City Transportation Officials
. https://nacto.org/wp-content/uploads/2016/04/NACTO-Bike-Share-Siting-Guide_FINAL.pdf
23.
Fishman
,
E.
,
Washington
,
S.
,
Haworth
,
N.
, and
Watson
,
A.
,
2014
, “
Factors Influencing Bike Share Membership: An Analysis of Melbourne and Brisbane
,”
Transp. Res. Part A Policy Pract.
,
71
, pp.
17
30
.
24.
Bullock
,
C.
,
Brereton
,
F.
, and
Bailey
,
S.
,
2017
, “
The Economic Contribution of Public Bike-Share to the Sustainability and Efficient Functioning of Cities
,”
Sustain. Cities Soc.
,
28
, pp.
76
87
.
25.
Bonilla Alicea
,
R.
,
Watson
,
B.
,
Tamayo
,
L.
,
Shen
,
Z.
, and
Telenko
,
C.
,
2019
, “
Life Cycle Assessment to Quantify the Impact of Technology Improvements in Bike-sharing Systems
,”
J. Ind. Ecol.
pp.
1
11
.
26.
Chaudhari
,
A. M.
,
Zhenghui
,
S.
, and
Panchal
,
J. H.
,
2018
, “
Analyzing Participant Behaviors in Design Crowdsourcing Contests Using Causal Inference on Field Data
,”
ASME J. Mech. Des.
,
140
(
9
), p.
091401
.
27.
Wassenaar
,
H. J.
,
Chen
,
W.
,
Cheng
,
J.
, and
Sudjianto
,
A.
,
2005
, “
Enhancing Discrete Choice Demand Modeling for Decision-Based Design
,”
ASME J. Mech. Des.
,
127
(
4
), p.
514
.
28.
Morrow
,
W. R.
,
Long
,
M.
, and
MacDonald
,
E. F.
,
2014
, “
Market-System Design Optimization With Consider-Then-Choose Models
,”
ASME J. Mech. Des.
,
136
(
3
), p.
031003
.
29.
Frischknecht
,
B. D.
,
Whitefoot
,
K.
, and
Papalambros
,
P. Y.
,
2010
, “
On the Suitability of Econometric Demand Models in Design for Market Systems
,”
ASME J. Mech. Des.
,
132
(
12
), p.
121007
.
30.
Williams
,
N.
,
Azarm
,
S.
, and
Kannan
,
P. K.
,
2008
, “
Engineering Product Design Optimization for Retail Channel Acceptance
,”
ASME J. Mech. Des.
,
136
(
6
), p.
061402
.
31.
Wang
,
Z.
,
Azarm
,
S.
, and
Kannan
,
P. K.
,
2011
, “
Strategic Design Decisions for Uncertain Market Systems Using an Agent Based Approach
,”
ASME J. Mech. Des.
,
133
(
4
), p.
041003
.
32.
Sha
,
Z.
, and
Panchal
,
J. H.
,
2014
, “
Estimating Local Decision-Making Behavior in Complex Evolutionary Systems
,”
ASME J. Mech. Des.
,
136
(
6
), p.
061003
.
33.
Kang
,
N.
,
Feinberg
,
F. M.
, and
Papalambros
,
P. Y.
,
2016
, “
Autonomous Electric Vehicle Sharing System Design
,”
ASME J. Mech. Des.
,
139
(
1
), p.
101402
.
34.
Kang
,
N.
,
Feinberg
,
F. M.
, and
Papalambros
,
P. Y.
,
2015
, “
Integrated Decision Making in Electric Vehicle and Charging Station Location Network Design
,”
ASME J. Mech. Des.
,
137
(
6
), p.
061402
.
35.
Moran
,
P. A. P.
,
1951
, “
A Mathematical Theory of Animal Trapping
,”
Biometrika
,
38
(
3
), pp.
307
311
.
36.
Draper
,
N.
, and
Guttman
,
I.
,
1971
, “
Bayesian Estimation of the Binomial Parameter
,”
Technometrics
,
13
(
3
), pp.
667
673
.
37.
Byers
,
A. L.
,
Allore
,
H.
,
Gill
,
T. M.
, and
Peduzzi
,
P. N.
,
2003
, “
Application of Negative Binomial Modeling for Discrete Outcomes: A Case Study in Aging Research
,”
J. Clin. Epidemiol.
,
56
(
6
), pp.
559
564
.
38.
Olkin
,
I.
,
Petkau
,
A. J.
, and
Zidek
,
J. V.
,
1981
, “
A Comparison of n estimators for the binomial distribution
,”
J. Am. Stat. Assoc.
,
76
(
375
), pp.
637
642
.
39.
DasGupta
,
A.
, and
Rubin
,
H.
,
2005
, “
Estimation of Binomial Parameters When Both n, p Are Unknown
,”
J. Stat. Plan. Inference
,
130
(
1–2
), pp.
391
404
.
40.
Hall
,
P.
,
1994
, “
On the Erratic Behavior of Estimators of N in the Binomial N, P Distribution
,”
J. Am. Stat. Assoc.
,
89
(
425
), pp.
344
352
.
41.
Tang
,
V. K. T.
,
Sindler
,
R. B.
, and
Shirven
,
R. M.
,
1987
,
Bayesian estimation of n in a binomial distribution
,
Center for Naval Analysis
, pp.
1
30
.
Tech. Rep.
42.
O’Brien
,
O.
,
Cheshire
,
J.
, and
Batty
,
M.
,
2014
, “
Mining Bicycle Sharing Data for Generating Insights Into Sustainable Transport Systems
,”
J. Transp. Geogr.
,
34
, pp.
262
273
.
43.
Fishman
,
E.
,
Washington
,
S.
, and
Haworth
,
N.
,
2014
, “
Bike Share’s Impact on Car Use: Evidence From the United States, Great Britain, and Australia
,”
Transp. Res. Part D
,
31
, pp.
13
20
.
44.
Zhang
,
J.
,
Pan
,
X.
,
Li
,
M.
, and
Yu
,
P. S.
,
2016
, “
Bicycle-Sharing System Analysis and Trip Prediction
,”
Proceedings of IEEE International Conference on Mobile Data Management
,
Porto, Portugal
.
45.
Rudloff
,
C.
, and
Lackner
,
B.
,
2014
, “
Modeling Demand for Bikesharing Systems
,”
Transp. Res. Rec. J. Transp. Res. Board
,
2430
(
1
), pp.
1
11
.
46.
Ome
,
C.
, and
Latifa
,
O.
,
2014
, “
Model-Based Count Series Clustering for Bike Sharing System Usage Mining : A Case Study With the Vélib System of Paris
,”
ACM Trans. Intell. Syst. Technol.
,
5
(
3
), pp.
1
21
.
47.
Rixey
,
R.
,
2013
, “
Station-Level Forecasting of Bikesharing Ridership
,”
Transp. Res. Rec. J. Transp. Res. Board
,
2387
, pp.
46
55
.
48.
Feldman
,
D.
, and
Fox
,
M.
,
1968
, “
Estimation of the Parameter n in the Binomial Distribution Dorian Feldman; Martin Fox
,”
J. Am. Stat. Assoc.
,
63
(
321
), pp.
150
158
.
49.
MathWorks Inc.
matlab Documentation
.” https://www.mathworks.com/help/curvefit/tpaps.html Accessed December 14, 2018.
50.
Smith
,
A.
,
2015
, “
Crowdsourcing for Active Transportation
,”
ITE J.
,
85
(
5
), pp.
30
35
.
51.
Dittmar
,
H.
, and
Ohland
,
G.
,
2004
, “
The New Transit Town: Best Practices in Transit-Oriented Development
,”
Transportation (Amst).
,
42
(
01
), p.
42-0424
.
52.
Taylor
,
W. L.
,
1964
, “
Correcting the Average Rank Correlation Coefficient for Ties in Rankings
,”
J. Am. Stat. Assoc.
,
59
(
307
), pp.
872
876
.
You do not currently have access to this content.