This three sentence summary provides the key details about the document: This document proposes an integrated auction and market design method for parking space sharing and allocation using IoT technologies. It develops an IoT-enabled urban parking management cloud platform to share private parking spaces among drivers using a price-compatible top trading cycles and chains mechanism, and reallocate public parking spaces using a one-sided Vickrey-Clarke-Groves auction. Experimental results show the proposed mechanism achieves 20-30% system profitability and ex post budget balance for the platform.

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 1
IoT-Enabled Parking Space Sharing
and Allocation Mechanisms
Xiang T. R. Kong , Su Xiu Xu, Meng Cheng, and George Q. Huang
Abstract—This paper is among the first proposing an inte-
grated auction and market design method for the parking space
sharing and allocation problem. Drivers (agents) who fail to
exchange their own parking spaces can then rent them to the
platform. The platform receives private parking spaces from
agents and manages some public parking spaces. We first develop
the urban parking management cloud platform through Internet
of Things. Based on this systemic framework, parking spaces
are shared among agents via a price-compatible top trading
cycles and chains (PC-TTCCs) mechanism and the platform’s
parking spaces are reassigned via a one-sided Vickrey–Clarke–
Groves (O-VCG) auction. Both the PC-TTCC mechanism with
rule e (PC-TTCC [e]) and O-VCG auction are effective in terms
of strategy-proofness and (allocative or Pareto) efficiency. In the
PC-TTCC [e] mechanism, the platform’s payment rule used in
private parking space sharing is determined based on historical
O-VCG auction prices. Our experimental results further show
that the proposed mechanism results in system profitability
of 20%–30% and ex post budget balance for the platform.
Note to Practitioners—This paper was motivated by the prob-
lem of constantly climbing parking needs in major cities. This
paper suggests a new approach for intelligent parking space shar-
ing, allocation, and pricing from the integrated market design and
auction perspective based on Internet of Things/cloud technologi-
cal architecture. The proposed mechanisms are effective in terms
of strategy-proofness and efficiency, leading to remarkable system
profitability. Reasonable agents’ cost saving, bidders’ value, and
ex post budget balance for the platform can also be guaranteed
in a big city with larger population. Several key managerial
implications have been gained. First, a public platform should
choose the integrated mechanism that realizes higher agents’ cost
Manuscript received May 5, 2017; revised September 29, 2017;
accepted December 9, 2017. This paper was recommended for publication
by Associate Editor C. Occhiuzzi and Editor J. Li upon evaluation of the
reviewers’ comments. This work was supported in part by Zhejiang Provincial,
Hangzhou Municipal, Lin’an City governments, in part by ITF Innovation
and Technology Support Program of Hong Kong Government under
Grant ITP/079/16LP, in part by HKSAR RGC GRF under Grant 17212016
and Grant 17203117, and in part by the National Natural Science Foundation
of China under Grant 71671116 and Grant 71701079. (Corresponding author:
Su Xiu Xu.)
X. T. R. Kong is with the Department of Transportation Economics
and Logistics Management, College of Economics, Shenzhen University,
Shenzhen 518061, China, and also with the HKU-ZIRI Laboratory for
Physical Internet, Department of Industrial and Manufacturing Systems Engi-
neering, The University of Hong Kong, Hong Kong (e-mail: kongxtr@hku.hk).
S. X. Xu is with the Institute of Physical Internet, School of Electrical and
Information Engineering, Jinan University (Zhuhai Campus), Zhuhai 519070,
China (e-mail: xusuxiu@gmail.com).
M. Cheng and G. Q. Huang are with the HKU-ZIRI Laboratory for Physical
Internet, Department of Industrial and Manufacturing Systems Engineering,
The University of Hong Kong, Hong Kong (e-mail: mchenghku@qq.com;
gqhuang@hku.hk).
Color versions of one or more of the figures in this paper are available
online at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TASE.2017.2785241
saving. Second, agents should be encouraged to rent their private
parking slots to the platform for reaching more agents’ welfare.
Third, the platform should leverage his owned public parking
spaces to achieve higher system profitability and agents’ cost
saving. Fourth, compared with Vickrey–Clarke–Groves auction,
the simpler first-price auction may lead to higher cost saving for
agents in some cases even if it cannot realize allocative efficiency
and incentive compatibility. Finally, the platform’s profit will
increase and the agents’ cost saving will decrease with the per-
centage of no show. Preliminary simulation experiments suggest
that this approach is feasible but it has not yet been incorporated
into a prototype system nor verified in real-world applications.
Regarding future work, some other factors such as transaction
costs, parking uncertainty, and release of traffic congestion can
be included in the proposed mechanism. Our integrated price-
compatible top trading cycles and chains [e] and one-sided
Vickrey–Clarke–Groves mechanisms can exploit the allocation
and pricing problems in B2B e-commerce logistics, on-demand
traffic fleet management, and ridesharing optimization.
Index Terms—Efficient auction, Internet of Things (IoT)-
enabled cloud, mechanism design, parking space sharing and
allocation, strategy proofness.
I. INTRODUCTION
WITH the growing percentage of car ownership in
metropolitan areas, parking has become a worldwide
challenge. According to the latest statistics of Beijing Trans-
port Authority (2016), more than 2.5 million public parking
spaces should be available in Beijing. Due to the limited
parking spaces, it is usually difficult to find a parking space
especially in commuting time. Around 30% of the traffic is
caused by the cars in congested downtown of 11 major cities
are cruising for parking, and the average cruising time is
8.1 min per car [32]. It is no surprise that existing parking
issues would further lead to severe urban traffic congestion and
environmental pollution [33]. However, the increasing urban
parking challenge is not merely because of the insufficient
parking spaces. Instead, an important reason is that effective
parking information sharing and resource allocation is not in
place. For instance, the parking spaces in a hospital are quite
limited, whereas there are a number of private parking slots
available in the surrounding residential areas.
Indeed, some famous commercial platforms such as Airbnb
and Uber have reshaped the travel and accommodation mar-
kets [37], [40]. The recent technological advances in real-
time data capturing, processing, and analytics can further
enable the effective sharing of parking spaces in big cities,
like Internet-of-Things (IoT) devices, ubiquitous communica-
tions, and cloud computing [16], [35]. There have been some
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2 IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
studies using the integration of IoT-based-technologies and
game theory to solve the challenging parking space allocation
problem [2], [8], [26], [39]. However, existing research mainly
focuses on the public parking assignment problems and their
objective is to minimize the total travel distance/time.
The rise of sharing economy might provide a good solution
for urban parking problem. Xu et al. [38] are among the first
to exploit the potential benefits in the sharing of massive
private parking slots. A private parking slot is the one that
is exclusively possessed by someone. As reported by the
Hong Kong Transport Department (2016), private-use parking
spaces represent nearly 70% of the total number of parking
spaces in Hong Kong. Although the great potential in private
parking space sharing has been recognized, an IoT-enabled
integrated mechanism that can be used to effectively assign
both public and private parking spaces remains open.
To the best of our knowledge, little attention has been
devoted to the parking space sharing and allocation problem by
integrating the auction and market design approaches, as well
as the IoT/cloud technological architecture. In particular, this
paper aims to answer the following questions.
1) How to create an IoT-enabled architecture for proactive
parking space sharing, allocating, and pricing under
various parking scenarios?
2) What is a Pareto-efficient, strategy-proof matching
mechanism for parking space sharing with money flow?
3) What is an allocatively efficient auction mechanism for
parking space allocation?
4) What are the magnitude of system profitability and
the platform’s strategic behavior under various realistic
settings?
In this paper, we consider the following case. A driver
(agent) who fails to exchange his private parking space can
then transfer it to the platform by receiving a corresponding
payment. The platform owns two types of parking spaces:
one is received from agents and another is the public parking
spaces. We first develop the IoT-enabled urban parking man-
agement (IoT-UPM) cloud. Based on this systemic framework,
parking spaces are shared among agents via a price-compatible
top trading cycles and chains (PC-TTCCs) mechanism. The
PC-TTCC mechanism is proposed by extending the exist-
ing market design theories [1], [27]–[31], [34]. There is no
money in the traditional matching markets such as marriage
and kidney exchange. The PC-TTCC mechanism with rule
e (PC-TTCC [e]) is effective in terms of allowing money
flow and Pareto efficiency. The platform’s parking spaces are
reassigned via a one-sided Vickrey–Clarke–Groves (O-VCG)
auction [5], [9], [36]. The O-VCG auction realizes strategy-
proofness and allocative efficiency. In the PC-TTCC [e] mech-
anism, the platform’s payment rule used in private parking
space sharing is determined based on historical O-VCG auc-
tion prices.
Our experimental results further show that the proposed
mechanism results in system profitability of 20%–30% and ex
post budget balance for the platform. To realize higher system
profitability and agents’ cost saving, the platform should lever-
age her owned public parking spaces and encourage agents to
rent out their private parking spaces. However, if the historical
auction prices tend to be high, the platform will pay high prices
for her received parking spaces during parking space sharing.
This paper is organized as follows. In Section II, we review
related theories and developments in parking literature.
In Section III, we develop the overall framework of IoT-UPM
cloud. In Section IV, we propose effective mechanisms for
parking space sharing and allocation. In Section V, com-
putational studies are conducted to examine the magnitude
of system profitability and the platform’s strategic behavior,
as well as the effects of some key factors. Conclusions and
future work are given in Section VI.
II. LITERATURE REVIEW
In this section, we briefly review related work:
1) IoT-enabled intelligent parking management; 2) market
design theory for parking space sharing; and 3) auction-based
mechanism for parking space allocation.
A. IoT-Enabled Intelligent Parking Management
IoT has brought about a new paradigm in many fields
including intelligent urban parking management [3], [7], [11].
Nowadays, a parking space can be reserved by a smartphone
easily via Internet access. A map of the driver’s current posi-
tion based on the GPS data along with the status of nearby car
parks can also be sent to their mobile phones immediately [12].
Geng and Cassandras [10] proposed a smart parking system
which assigns and reserves an optimal parking space based on
the driver’s cost function based on proximity to destination and
parking cost. A mixed-integer linear programming solution
was used for optimal resource allocation with current and
updated information. The presented models only allowed the
reservation for limited period of time (e.g., few minutes), used
fixed price and revenue was not taken into account. Also,
decentralized auction mechanism for parking space allocation
has not yet been considered. Ji et al. [15] proposed a high-
level view of the middleware and operational platform for car
parking services based on IoT/cloud technologies. They paid
more attention on generic design and implementation issues
of car park administration in a smart city, whereas, dynamic
parking space sharing, allocation, and pricing problems are not
considered. Pham et al. [25] introduced a novel algorithm that
helps users automatically find a free parking space at the least
cost to increase the efficiency of the existing cloud-based smart
parking system and developed a network architecture based
on the IoT technology. Its operations were concerned with the
contemporary design and behavioral responses to systems that
use variable message signs to inform drivers about available
parking spaces.
There are almost no IoT-enabled combined mechanism
design of dynamic sharing, allocation, and pricing in the
existing car parking literature. Moreover, the existing studies
on parking problem only focus on open parking slots. How-
ever, our work considers the exchanging and reassignment of
both public and private urban parking slots during working
hours. It is also a critical research problem that how to
design effective mechanisms considering the interrelationship
between parking slots sharing and (re)assignment to obtain

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KONG et al.: IOT-ENABLED PARKING SPACE SHARING AND ALLOCATION MECHANISMS 3
the fairly ideal system profitability. In this paper, we aim to
exploit the potential of urban parking space sharing based on
market design theory and auction mechanism.
B. Market Design Theory for Parking Space Sharing
Kidney exchange problem is one of most classical topics in
market design theory. Roth et al. [28] introduced an indirect
exchange scheme to deal with kidney exchange problem.
A family of top trading cycles and chains (TTCCs) mech-
anisms were established with seven chain selection rules
(a, b, c, d, e, f , and g) to enhance the welfare of patients.
Roth et al. [29] developed the pairwise kidney exchange
mechanism where kidney exchange is only allowed between
two patient-donor pairs. As proved by Krishna and Wang [18],
the TTCC mechanism with rule e is strategy-proof and Pareto
efficient. Most recently, Sönmez and Ünver [34] proposed a
two-way kidney exchange framework with Pareto-efficient
matching. Both compatible and incompatible patient-donor
pairs were examined in this framework and patients have
no preferences over compatible kidneys. Obviously, there is
no money in the kidney exchange market. Xu et al. [38]
extended the TTCC mechanism to solve the private parking
slot sharing problem with money flow. Specifically, they
considered that one agent who fails in parking slot exchange
can always “transfer” (rent) his parking slots to the platform.
Then, the platform makes payments to the agents (owners)
who provide the parking slots, and will be in charge of the
parking slots he receives during the regular working hours.
The proposed PC-TTCC mechanism has theoretical properties
as strategy-proofness and Pareto efficiency. In this paper,
PC-TTCC [e] mechanism is employed for an IoT-enabled
parking space sharing network with money flow. Furthermore,
in the PC-TTCC [e] mechanism, the platform’s payment rule
used in private parking space sharing is determined based on
historical auction prices.
C. Auction-Based Mechanisms for Parking Space Allocation
Recent studies have looked into open parking slots alloca-
tion problem with centralized authority and advanced infor-
mation technologies [6], [23], [24]. Ayala et al. [2] proposed
two pricing schemes for a parking authority who can use
the parking availability information and drivers’ cost to set
prices to entice drivers and minimize total driving distance.
The auction-based scheme resulted in a Nash equilibrium
assignment. Hashimoto et al. [13] developed an auction-based
parking reservation system to reduce the amount of space
searing time. Simulation combined with the driver parking
duration model were used to evaluate system performance.
An auction-based system for realizing centralized parking
allocation schemes were put forward by Kokolaki et al. [17].
In this scheme, drivers submitted bids for public parking
spaces and a central authority coordinates the parking assign-
ments and associated payments. This approach was compared
against the conventional parking search method under fixed
cost. Results showed that the auctioning system increases the
revenue of the public parking operator while exploiting the
drivers’ differentiated interests. Zou et al. [39] used auction
Fig. 1. IoT-UPM cloud platform.
approaches to assign open parking slots. But agents have to
report their truthful information. Kotb et al. [21] introduced a
new smart parking system that is based on the combination
of intelligent resource allocation, real-time reservations, and
dynamic pricing policies using mixed-integer linear program-
ming to minimize drivers’ total costs and maximize the parking
resource utilization. This work presented in this paper is
among the first to combine dynamic parking sharing and
allocation models to overcome the parking problem while
systematically examining the impact of the proposed mech-
anisms on platform’s strategic behavior under various realistic
settings. Our extensive simulation results indicate that the
proposed combined mechanism results in remarkable system
profitability and (ex post) budget balance for the platform.
III. CREATION OF IOT-ENABLED URBAN PARKING
MANAGEMENT (IOT-UPM) CLOUD
Car parking value chains are distributed and dependent on
complex information and physical infrastructures. IoT-enabled
ubiquitous tools are needed to reduce the complexity of
parking management systems while supporting collabora-
tion among value chain partners. Fig. 1 depicts a view of
an intelligent IoT-enabled cloud platform, which integrates
both physical world and cyber world and could serve as a
generic framework for “urban parking space sharing, allo-
cation, and pricing.” It mainly consists of local deployment
and cloud deployment with core components of IoT data
framework (IoT-DF) (i.e., smart gateway, cloud-edge data
processing modules), private slot sharing services and spaces
allocation/pricing services. The use of IoT-UPM cloud plat-
form will bring a paradigm shift from the traditional urban
parking to a new data-driven intelligent urban parking, and its
key impacts lie in the following four aspects.
1) In terms of technologies, the traditional information
infrastructure will be shifted to the cloud, which enables

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4 IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
a unified and flexible environment for real-time data
collection and services integration.
2) In terms of decision making, through using smart
phones, it changes the way for motorists to collect real-
time parking information, interact with each other, and
make proactive decisions even during driving processes.
3) For public/private parking management platform,
the shared cloud infrastructure can effectively ease the
imbalance problem of supply and demand with the
assistance of private parking spaces sharing. It also
unifies the urban parking administration as an efficient
and economical process.
4) For drivers (agents), the better travel plans can be
arranged and the waiting time caused by parking can
be greatly reduced. The motorists who need a parking
slot can be rapidly and easily paired with neighboring
parking spaces.
On the bottom layer of IoT-UPM cloud, different sens-
ing and ubiquitous technologies could be utilized locally to
build up IoT-based smart parking environment, such as the
radio frequency identification for car parking access con-
trol and laser, microwave radar, or closed-circuit television
with video image processing for detecting the status of the
car parking lots. To enable the IoT-UPM cloud to work
as an integral management platform, different car parking
areas must be distinguished in providing “best” parking
spaces by executing different business roles and applications.
Based on their properties, the representative car parking
areas have been identified, including residential/community
parking areas, on-street areas, and public areas (e.g., for
shopping mall/hotel/restaurant/transportation hub). The sen-
sors deployed in these car parking areas periodically or real-
timely send updated information as regards occupancy of the
parking slots to the smart gateway, which push this data further
to the IoT-UPM cloud. Developed legacy systems can also be
connected and integrated with IoT-UPM cloud.
On the middle layer of IoT-UPM cloud, IoT-DF is proposed
to streamline data collection from various data sources con-
sidering different standards, data models, and communication
protocols. IoT-DF has two main components: smart gateway
and cloud-edge data processing module. Smart gateway must
address two major pools of data sources, i.e., IoT devices
deployed on parking spaces and legacy systems developed
to manage car parking, by providing several key functions
as follows: 1) it connects (wired or wirelessly) and hosts
a set of registered IoT assets; 2) it processes caches and
exchanges real-time data and events locally and temporally;
and 3) it provides facilities for service definition, configuration
and execution locally.
Cloud-edge data processing module is a processing engine
for gateway-level information which can be further broken
down to events generated from each active gateway unit.
The real-time data aggregated from smart gateways might
include parking time period, prices, locations, vehicle-related
information, etc. It decodes received messages into parameters
such as event ID and invokes a corresponding handler to
process the requested service based on the parameters decoded.
Cloud-edge data processing module also addresses the
functional requirements of information aggregation, event pat-
tern matching, management of activated critical events, and
associated event instances. Business-level execution notifica-
tion (e.g., occupancy of the parking slots) will be automatically
generated and responded to the platform for further decision
support.
In the cloud layer of IoT-UPM platform, private slot sharing
and space allocation/pricing mechanisms are encapsulated as
services for users’ invoking and application anywhere and
anytime. The private slot sharing service enables efficient
exchange of agents’ private parking space via PC-TTCC mech-
anism. The agent can also transfer his/her slots to the platform
by receiving a corresponding payment if the parking space
exchanging is not successful. Hence, the IoT-UPM platform
owns two types of parking spaces: one is received from
agents, another is the public parking spaces. The space allo-
cation/pricing service can automatically assign the platform’s
parking spaces to the public via a fair auction mechanism
with reasonable price. In the state of information fully sharing
and transparency, the designed auction mechanism would
be beneficial for the private parking organizations especially
because more people will join in for bidding. The platform can
also bring other value-added services for the private parking
organization as incentives such as free advertising or the
usage of platform networking as a word-of-mouth marketing
vehicle.
All these services are managed through the portal of
IoT-UPM cloud. Web portal organizes all related information
together in several adaptive views to satisfy different value
chain partners in parking activities. The basic sharing and
allocation process is described as follows.
1) The basic sharing process of private parking spaces in
IoT-UPM cloud.
a) Each driver (agent) can browse necessary informa-
tion about the exchange pool of parking spaces in
IoT-UPM platform. Each agent can also determine
his preferences over the parking spaces in the
exchange pool.
b) Agents who are looking for parking slots exchange
send requests to IoT-UPM platform via their
mobile devices (e.g., smartphones or vehicle-
mounted computers). A request is accompanied by
parking space information such as location and
admission to his/her parking space.
c) The private slot sharing module collects all driver
requests real timely and makes an overall space
sharing among agents based on the PC-TTCC
mechanism.
d) The agent gets one parking space for free if he suc-
cessfully exchanges his parking space. Otherwise,
he can rent his parking space to the platform and
get a certain payment.
2) The basic allocation process of private/public parking
spaces in IoT-UPM cloud.
a) The platform owns public parking spaces and
meanwhile receives some private parking slots
from agents. Each bidder can browse necessary

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KONG et al.: IOT-ENABLED PARKING SPACE SHARING AND ALLOCATION MECHANISMS 5
information about the auction pool of parking
spaces in IoT-UPM platform.
b) During the auction, each bidder submits an XOR
bid which contains three atomic bids at most.
c) The space allocation/pricing module collects all
bidders information on a real-time basis while
carrying out space allocation/pricing among bid-
ders over a certain time window based on O-VCG
auction mechanism. In addition to “location” data,
real-time data of parking time period, prices, and
parking behaviors (e.g., “no-show” information)
are also captured as the mechanism inputs.
d) Each bidder receives a VCG payment. Historical
auction prices are recorded and sent back to slot
sharing module to determine the payment rule used
in private parking space sharing.
IV. PARKING SPACE SHARING AND ALLOCATION
A. Problem Description
We consider a city with a number of public and private
parking slots, which are centralized monitored and controlled
by an urban parking management platform called IoT-UPM
cloud. Due to the adoption of intelligent IoT sensors and
devices, the platform operates the parking space sharing net-
work by collecting information on parking availability from
both private and public slots and using effective mechanisms
to facilitate parking space sharing and allocation in a city.
In terms of parking space sharing, plethora of private
parking slots are the main objects to be exchanged. A parking
space that is exclusively owned by someone is called “a private
parking space.” We refer to the owner of a private parking
space as “an agent.” A number of agents in a metropolitan area
can exchange or rent their private parking slots on IoT-UPM
cloud platform anytime and anywhere. Each agent owns one
private parking space and needs one parking space from others
in the sharing network. Each agent is also self-interested. The
private parking space of one agent will be vacant when he
drives to work. But his parking space is likely to be preferred
by some others who work nearby. Agents can hence benefit
from exchanging their own parking spaces during regular
working hours, like the time slots from 9:00 A.M. to 6:00 P.M.
Moreover, the sharing of private parking spaces among agents
is only valid during regular working hours. One agent, who
fails to exchange his parking space, can always “transfer”
(rent) his parking space to the IoT-UPM cloud platform during
regular working hours. The platform will then pay to the agents
who temporarily offer their parking spaces. The payment rule
is given by the platform. Each agent is associated with a fixed
price. The platform tries to benefit from his public and received
private parking spaces.
Each agent announces the platform his parking space infor-
mation such as location and admission to his parking space.
The necessary information about the exchange pool of parking
spaces among agents is known to all agents. Each agent
then can determine his preferences over the parking spaces
in the exchange pool. For example, one agent would like
to select the parking space closest to his workplace as the
first choice, the one second closest as the second choice,
and so on. The parking spaces in one residential community
can be viewed as the same. Since ties are broken arbitrarily,
each agent has strict preferences over parking spaces. Clearly,
no agent gets worse when participating in the parking space
sharing. If one agent successfully exchanges his parking space
that means he gets one parking space for free; otherwise, he
can rent his parking space to the platform and get a certain
payment.
In terms of parking space allocation, the platform’s parking
spaces are assigned to the public via a combinatorial auction.
The platform is the auctioneer. The bidders can be the tourists
of the city or the office workers who do not have private
parking spaces. Each bidder submits an XOR bid, which is
a set of indivisible (atomic) bids. One atomic bid is specified
by a set of parking spaces, the time slots of parking spaces,
and a corresponding price. Each parking space is split to
several parking time slots based on its parking availability.
For example, the duration of each parking time slot can be
0.5 h. For each parking space, there is no overlap between any
two parking time slots. Also, if two or more parking spaces
are involved in an atomic bid, then no time overlap will exist
among these parking spaces.
The problem faced by the platform is twofold: 1) to develop
a Pareto-efficient parking space sharing mechanism that allows
money flow and 2) to develop an allocative and efficient
combinatorial auction mechanism that maximizes the total
welfare of bidders and the platform (auctioneer). Note that
the payment rule used in private parking space sharing is
determined based on historical auction prices.
B. Mechanism Design
1) Price-Compatible Top Trading Cycles and Chains
(PC-TTCC) Mechanism: Suppose that agents ai are ordered in
a priority list based on their indices starting with the smallest
index. Let (si, ai ) be a space-agent pair. Let S be the set of
parking spaces in the exchange pool. Let Si be the set of
parking spaces that are preferred by agent ai except his own
space si .
Let w be the option that if an agent fails in parking space
exchange, then he would like to rent his parking space to the
platform by receiving a fixed payment. Let pi(si ) be the fixed
price associated with agent ai. The platform is price maker,
and the pricing rule specified by the option w is predetermined.
Ties are broken arbitrarily. Each agent has strict preferences
Pi over Si ∪{si , w}. The bottom of Pi is either si or w; that is,
the lowest priority of Pi is either si or w. If agent ai choose
si as the bottom of Pi that means he will not transfer his own
parking space si to the platform even when he fails in parking
space exchange.
The payment rule used in private parking space sharing
is determined based on historical auction prices. The price
of parking space si during regular working hours in day t,
pi(si , t), is given by
pi(si , t) =
α T
l=1 ˆpi(si,l)
T
(1)

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6 IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
where α is a constant and 0 < α < 1; ˆpi(si ,l) represent the
adjusted auction price of parking space si in the recent lth
auction (1 ≤ l ≤ T ); T is a constant integer.
Consider the lth auction. Suppose that each parking space
si is split to K parking time slots, where K is a constant.
Then, for parking space si, each parking time slot k (1 ≤
k ≤ K) is associated with an adjusted auction price ˆpi(si,k ,l)
and ˆpi(si,l) = K
k=1 ˆpi(si,k ,l). If the parking time slot si,k
is eventually involved in an (atomic) bid b(si,k) with K (si,k )
time slots, where 1 ≤ K (si,k ) ≤ K; then ˆpi(si,k ,l) can be
given by
ˆpi(si,k ,l) =
p(b(si,k),l)
K (si,k )
(2)
where p(b(si,k),l) is the payment corresponding to the
selected bid b(si,k) in the lth auction.
Thus, pi(si , t) in (1) can be rewritten as
pi(si , t) =
α
T
T
l=1
K
k=1
p(b(si,k),l)
K (si,k )
. (3)
For simplicity, in what follows we use pi(si ) to denote the
price and the time (note: the script t is suppressed in the price
pi(si, t) and this should cause no confusion).
We next simply review the PC-TTCCs mechanism proposed
by Xu et al. [38]. The PC-TTCC mechanism contains multi-
ple rounds. In each round, every agent ai points either to a
parking space in Si ∪{si} or the option w (i.e., ai → sj or ai →
w), and every parking space points to its paired agent ai (i.e.,
si → ai).
Definition 1: A cycle is an order list of parking spaces
and agents (s1, a1, s2, a2, . . . , sm, am), where parking space s1
points to agent a1, agent a1 points to parking space s2, . . . ,
parking space sm points to agent am, and agent am points to
parking space s1. In this cycle, agent a1 is assigned parking
space s2, agent a2 is assigned parking space s3, . . . , and agent
am is assigned parking space s1.
The definition of cycle is standard in the market design
literature (e.g., [1], [27], [28], and [31]). Since each parking
space or agent can be included in at most one cycle, no two
cycles intersect.
Definition 2 [38]: An aw-chain is an ordered list of parking
spaces and agents (s1, a1, s2, a2, . . . , sm, am), where parking
space s1 points to agent a1, agent a1 points to parking space
s2, . . . , parking space sm points to agent am, and agent am
points to the option w. In this aw-chain, agent am rents out
his parking space sm to the platform who pays pm(sm) to agent
am but receives parking space s1.
Definition 3 [38]: An sw-chain is an ordered list of parking
spaces and agents (s1, a1, s2, a2, . . . , sm, am, sj ), where park-
ing space s1 points to agent a1, agent a1 points to parking
space s2, . . . , parking space sm points to agent am, agent
am points to parking space sj , and parking space sj points
to the platform (w). In this sw-chain, the platform assigns
his received parking space sj to am, and parking space s1 is
offered to the platform.
In fact, aw-chain and sw-chain are the modified versions
of the w-chain introduced by Roth et al. [28]. Hence, an aw-
chain or sw-chain can also be called a w-chain. If a w-chain
is removed (or clinched), then each agent in the chain gets the
parking space that he points to and leaves the mechanism, and
the parking space s1 is removed (or kept). Clearly, there is no
money flow in an sw-chain.
For our purposes, we only adopt the following chain
selection rule (named rule e) introduced by Roth et al. [28].
Choose the w-chain (either aw-chain or sw-chain) starting with
the highest priority slot-agent pair, and clinch it. Rule e means
that the platform will try to reassign all of his received parking
spaces.
The PC-TTCC mechanism with rule e (denoted by
PC-TTCC [e]) is presented as follows.
Step 1) In each round, every agent ai points to his myopic
best parking space or the option w, whichever is
more preferred, and each remaining parking space
si points to its paired agent ai or the platform (w).
Step 2) There exists at least one cycle or one w-chain
([28, Lemma 1]). Proceed to Step 3 if there is no
cycle. Otherwise, locate each cycle and carry out the
matching; that is, each agent in the cycle is assigned
the parking space he is pointing to. Remove all
the agents and parking spaces in the cycle. Repeat
until no cycle exists. The PC-TTCC mechanism
terminates if no agent is left; otherwise, proceed
to the next round and return to Step 1.
Step 3) Select a w-chain according to the chain selection
rule e, and carry out the matching based on the
definition of w-chain. The PC-TTCC mechanism
terminates if no agent is left; otherwise, proceed
to the next round and return to Step 1.
The main result is as follows.
Theorem 1 [38]: Given the payment rule specified by the
option w, the PC-TTCC [e] mechanism is strategy-proof and
Pareto efficient.
Theorem 1 means that it is dominant for each agent to
report their real preferences (i.e., strategy-proofness). Also, the
PC-TTCC [e] mechanism finds a Pareto-efficient matching
solution; that is, no other matching makes at least one agent
strictly better off without hurting others (i.e., Pareto effi-
ciency).
2) One-Sided Vickrey–Clarke–Groves (O-VCG) Auction
Mechanism: Recall that the platform has a number of public
and received private parking spaces. Each parking space is
split to several parking time slots. The duration of all parking
time slots is the same (e.g., 15 min or 0.5 h). We refer
to a parking space with a specific time slot as “one item.”
Suppose that in an auction the platform has H items, which are
heterogeneous. Let be the set of feasible allocations, where
ϕ ∈ . A feasible allocation needs to assign all H items. Since
there are sufficient parking space lessees (bidders) in a city,
H items will be assigned eventually. For the platform, the cost
of H items is fixed. Without loss of generality, let the cost of
H items be zero. Consequently, allocative efficiency means
that the auction maximizes the value (welfare) of bidders.
Let N be the set of bidders. Each bidder n ∈ N submits
an XOR bid, in which every atomic bid includes a bundle of
items h ⊆ H and a bid price vn(h). In our proposed auction,

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KONG et al.: IOT-ENABLED PARKING SPACE SHARING AND ALLOCATION MECHANISMS 7
each bidder will tell the truth (see Theorem 2). That is, vn(h)
is the true value of bidder n for the bundle h.
An efficient allocation can be obtained by solving the
following integer program (IP):
IP: max
n∈N h⊆H
vn(h)xn(h) (4)
s.t.
h⊆H
xn(h) = 1, ∀n ∈ N, (5)
ϕ∈
y(ϕ) = 1 (6)
xn(h) −
ϕ:ϕn =h
y(ϕ) = 0, ∀n ∈ N, ∀h ⊆ H
xn(h) ∈ {0, 1}, ∀n ∈ N, ∀h ⊆ H,
y(ϕ) ∈ {0, 1}, ∀ϕ ∈ (7)
where objective (4) is to maximize bidders’ total value. Note
that h can be empty. xn(h) = 1 means that bundle h is
assigned to bidder n. y(ϕ) = 1 means that feasible allocation
ϕ is chosen. Constraints (5) guarantee XOR bids; that is, for
each bidder n ∈ N, at most one atomic bid can be chosen.
Constraints (6) mean only one feasible allocation will be
chosen. Constraints (7) guarantee that the supply is equal to
the demand, where ϕn represents the bundle that is assigned
to bidder n in the feasible allocation ϕ.
We introduce the O-VCG auction as follows. Let V (N)
be the value of (IP), and V (Nn) be the value of (IP) if
bidder n were excluded from this auction. Ties are broken
arbitrarily. Note that the bilateral VCG auction fails budget
balance, which means that the platform will run at a deficit
(e.g., [19] and [22]).
Our O-VCG auction moves as follows.
Step 1) Each bidder n submits a sealed valuation function
vn(h), to the platform (auctioneer).
Step 2) The platform solves the (IP) and thus determines the
set of winners N∗. A bundle hn is assigned to bidder
n ∈ N∗, where {hn}n∈N∗ is an efficient allocation
achieving V (N).
Step 3) Each bidder n ∈ N receives a VCG payment of
pn = vn(hn) + (V (N) − V (Nn)) for hn.
Our main result is as follows.
Theorem 2: The O-VCG auction realizes strategy-proofness,
allocative efficiency, individual rationality, and budget
balance.
Proof:
1) Strategy-Proofness: If bidder n ∈ N tells the truth, he
receives a VCG payment, pn = vn(hn) + (V(N) −
V (Nn)). When he bids as if his valuation function
is ˆvn, denote the resulting bundle allocated to the bidder
as ˆhn and the corresponding VCG payment ˆpn. Let N
be the set of winners if bidder n submits ˆvn. Suppose
that other bidders in N{n} report truthfully.
If truthful reporting is not a dominant strategy for bidder
n, then
vn(hn) − pn(hn) < vn(ˆhn) − ˆpn(ˆhn).
By the rule of VCG payment, the above inequality
implies that
V (N) − V (Nn)
<
⎡
⎣
j=n, j∈N
v(ˆh j ) + v(ˆhn)
⎤
⎦ − V (Nn)
⇔ V(N) <
j=n, j∈N
v(ˆh j ) + v(ˆhn)
which contradicts the fact that {hn}n∈N∗ is an efficient
allocation achieving V (N).
2) Allocative Efficiency, Individual Rationality, and Bud-
get Balance: Since strategy-proofness has been proved,
the solution of (IP) finds an efficiency allocation that
maximizes bidders’ welfare. Also, observe that the plat-
form will not run at a deficit (i.e., budget balance). Since
V(N) − V(Nn) ≥ 0, no bidder gets negative utility in
the auction (i.e., individual rationality).
Theorem 2 implies that the O-VCG auction is basically valid
for the problem of parking space allocation. In the O-VCG
auction, the dominant bidding strategy is truthful telling (i.e.,
strategy-proofness), which is so simple that more parking
space lessees (bidders) will engage in the auction. Also, the
O-VCG auction maximizes bidders’ welfare (i.e., allocative
efficiency) and thus bring more benefits to the platform in the
long run.
V. EXPERIMENTAL RESULTS
A. Simulation Setup
The performances of the integrated PC-TTCC [e] and
O-VCG mechanism for parking space sharing and allocation
in an IoT-enabled realistic setting are examined in our simula-
tions. Private parking spaces are shared among agents via the
PC-TTCC [e] mechanism. The platform’s public and private
parking spaces are reassigned via the O-VCG auction. The
PC-TTCC [e] mechanism is strategy-proof and Pareto-efficient
(Theorem 1). The O-VCG auction realizes strategy-proofness,
allocative efficiency, individual rationality, and budget balance
(Theorem 2). The payment rule used in private parking space
sharing is determined based on historical auction prices,
as expressed by (3). Our main concerns are the magnitude of
benefits realized by the integrated PC-TTCC [e] and O-VCG
mechanism in the IoT-enabled parking sharing and allocation
network, and how the platform’s profit differs in various
realistic settings.
Our experiment considers a parking space sharing and
allocation network with a 15-km radius and four business
clusters. Each cluster has a 3-km radius. In each cluster,
there are ten residential communities and ten office buildings.
Each residential community has five agents. Also, there are
ten residential communities and ten office buildings outside
these four clusters. For simplicity, we only use linear dis-
tances. The distance between agents’ residential community
and working place is larger than 10 km. The distance between
agents’ exchanged parking place and working place is less
than 0.8 km (about 10-min walking distance). The shortest
distance between two business centers is larger than 2 km.

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8 IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
The parking fee of each parking space for 8 h follows a normal
distribution with a mean of 50 (CNY or HKD) and variance
of 5.
In each regular working day, there are two auction sessions.
In each auction session, the availability of one parking space
is 4 h, like from 8 A.M. to 12 A.M. or from 14 P.M. to 18 P.M.
The platform owns ten public parking spaces and meanwhile
receives some private parking slots from agents. There are
100 agents (bidders) participating in the auction. Each bidder
submits an XOR bid which contains three atomic bids at most.
We refer to a parking space with a 0.5-h time slot as “one
item.” Thus, for a parking space, one atomic bid includes at
most eight items. There are some “buffers” (temporary parking
areas) for real-world parking space sharing and allocation.
Also, each atomic bid includes at most two parking spaces.
For one item, the bidding price of each agent follows a normal
distribution with a mean of 4 (CNY or HKD) and variance
of 0.5. We assume one agent’s bidding price is linear with
respect to the number of items.
The reported solutions are averages of 20 randomly gener-
ated instances. For each instance, the locations of four clusters
are randomly generated, and then 40 residential communities
and 40 office buildings randomly locate within the generated
four clusters. Also, ten residential communities and ten office
buildings randomly locate outside these four clusters. In each
instance, we set 30 days for realistic parking space sharing
and allocation (i.e., a cycle of 30 days). Note that, the auction
prices of the first six days are initial inputs for calculating the
average auction price to determine the payment rule that is
used in private parking space sharing.
Each agent has 5% of possibility of choosing w-option as
his least preferred choice (i.e., the bottom of his preferences).
The priority list of agents is generated according to their
corresponding prices pi(si, t), as depicted by (3). A higher
priority is associated with a smaller index and a higher
price. Ties are broken arbitrarily. In this simulation study,
the constant α in (3) is set to 90%.
The performances of the integrated PC-TTCC [e] and
O-VCG mechanism are measured by the following six indexes.
“Agents’ cost saving” ($) represents the agents’ average total
cost saving (per working day) realized by the PC-TTCC [e]
mechanism. “Platform’s profit” ($) represents the platform’s
(average) profit under the integrated PC-TTCC [e] and O-VCG
mechanism. “Bidders’ value” ($) represents the winning bid-
ders’ (average) total value realized by the O-VCG mechanism.
“Exchange ratio” (%) represents the (average) percentage of
agents whose parking spaces are successfully exchanged under
the PC-TTCC [e] mechanism. “Number of received parking
spaces” represents the (average) number of private parking
spaces received by the platform. “System profitability” (%)
represents the (average) profitability realized by the integrated
PC-TTCC [e] and O-VCG mechanism. Specifically, “system
profitability” (%) is given by (8), as shown at the bottom of
TABLE I
IMPACT OF THE NUMBER OF AGENTS
this page, where “agents’ cost” ($) represents the agents’ total
parking fee without the integrated PC-TTCC [e] and O-VCG
mechanism. Note that each agent submits his acceptable
maximal parking fee (i.e., real value) to the platform under
the O-VCG mechanism.
B. Results
Table I summarizes the effects of the number of agents.
“Number of agents” represents the number of agents in each
residential community, {1, 5, 10}. In this experiment, the num-
ber of bidders is 300. As shown in Table I, it seems that the
integrated PC-TTCC [e] and O-VCG mechanism realizes ex
post budget balance for the platform, whose profit ranges from
U.S. $733.91 to U.S. $782.93. As the number of agents in each
community increases, the integrated PC-TTCC [e] and O-VCG
mechanism results in higher agents’ cost saving. For example,
when there are ten agents in each community, the agents’
cost saving reaches U.S. $5157.14, nearly 14 times larger than
that under the case of one agent in each community. Observe
that if the platform receives more private parking spaces, then
higher bidders’ value will be realized. In the case of five
agents, the platform receives 10.66 parking spaces and realizes
bidders’ value of U.S. $1475.97. Recall that the platform
manages ten public parking spaces. The number of agents also
has a positive impact on the exchange ratio, which ranges from
7.11% to 15.35%. Although the system profitability decreases
with the number of agents, it is kept within 21.92%–32.40%.
Especially, there is only a small change of system profitability
(1.68%) as the number of agents increases from 5 to 10.
In Table II, “bid price of one item” ($) represents the mean
of one agent’s bid price for one item. In our benchmark,
the mean of bid price for one item is 4. “Without feedback”
represents the case where the payment rule in private parking
space sharing is merely determined by a normal distribution
with a mean of 50 (CNY or HKD) and variance of 5. “With
feedback” is our benchmark where the payment rule in private
parking space sharing is determined by historical O-VCG
auction prices, as given by (3).
Table II demonstrates that both platform’s profit and bid-
ders’ value increase with bid price of one item. Interestingly,
when the bid price of one item is 3, the platform’s profit
is much higher in the case of “with feedback.” However,
the platform’s profit is significantly lower in the case of “with
feedback,” if the bid price of one item is 5. This is because if
auction prices tend to be high, the incorporation of historical
System profitability =
Cost saving of agents who successfully exchange + Bidders’ value
Agents’ cost + Bidders’ value
(8)

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KONG et al.: IOT-ENABLED PARKING SPACE SHARING AND ALLOCATION MECHANISMS 9
Fig. 2. Payment price in private parking space sharing during 30 days.
TABLE II
IMPACT OF BID PRICE
O-VCG auction prices will make the payment given in (3)
larger than the mean 50 (see Fig. 2). Likewise, if auction
prices tend to be low, such incorporation will result in the
payment pi(si , t) lower than the mean 50. In our benchmark,
the bid price of one item is 4 and the corresponding payment
pi(si, t) is close to the mean 50. An important managerial
implication is therefore that a private platform should not use
the historical auction prices to determine his payment rule in
private parking space sharing if auction prices tend to be high.
However, in such a case agents prefer the feedback mechanism
that brings larger cost saving. This result implies a public
platform will choose the integrated PC-TTCC [e] and O-VCG
mechanism even if the auction prices tend to high.
Table III illustrates the impact of the number of public park-
ing slots. Clearly, if the platform owns more public parking
spaces, the integrated PC-TTCC [e] and O-VCG mechanism
realizes higher platform’s profit and bidders’ value. Also, it
appears to be true that the system profitability concavely
increases with the number of public parking spaces. Finally,
it is worth noting that agents’ cost saving is larger if the
number of public parking slots increases. This is because the
average auction prices decreases with the number of public
parking slots given a fixed number of bidders. Then, as the
auction prices decrease, the payment pi(si , t) will decrease,
resulting in higher cost saving for agents.
In Table IV, we present the impact of possibility of choosing
w-option. In this experiment, the number of bidders is 300.
Recall that in our benchmark, each agent has 5% of possibility
of choosing w-option as his least preferred choice. If the pos-
sibility of choosing w-option increases, more private parking
slots will be involved in the auction so that higher agents’ cost
saving and bidders’ value will be achieved. From (8), it follows
that larger agents’ cost saving will result in higher system
profitability that ranges from 18.7% to 27.67%. According to
TABLE III
IMPACT OF THE NUMBER OF PUBLIC PARKING SLOTS
TABLE IV
IMPACT OF THE POSSIBILITY OF CHOOSING w-OPTION
TABLE V
IMPACT OF THE NUMBER OF BIDDERS
TABLE VI
COMPARISON OF THE FIRST-PRICE AUCTION AND VCG AUCTION
the PC-TTCC [e] matching rule, a high possibility of choosing
w-option will also make more agents successfully exchanged.
For example, in the case of 10%, the exchange ratio is 15.52%.
Table V shows the impact of the number of bidders. Both
platform’s profit and bidders’ value is remarkably promoted
as the number of bidders increase. Given a relatively fixed
number of parking spaces, a large number of bidders will
increase auction prices, which in turn increase the payment
pi(si , t). Thus, agents’ cost saving will decrease with the
number of bidders. For example, in the case of 200 bidders,
agents’ cost saving is U.S. $2443.58, lower than that under
the case of 50 bidders. If the number of bidders continues
to increase, the change of agents’ cost saving could be more
significant.
In Table VI, we compare the performances of the
first-price auction and VCG auction. In the first-price auction,
the winning bidder has to pay his bid, and the objective
of (IP) is to maximize the sum of winners’ bids. One’s
bid in the first-price auction is strictly lower than his real
value. Note that “first-95%” represents the first-price auction
in which the variable, the ratio of one’s bid to his real
value, follows a normal distribution with mean 95% and
standard variance 0.01; similarly, “first-90%” corresponds to
the normal distribution with mean 90% and standard variance
0.01; and so on. Although the first-price auction cannot realize
allocative efficiency and incentive compatibility, it may lead
to higher cost saving for agents in some cases. For example,
in the experiments of “first-70%,” the agents’ cost saving
is U.S. $2665.71, larger than that under the VCG auction.
This is because all bidders tend to bid much lower than

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10 IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
TABLE VII
IMPACT OF THE “NO-SHOW” BEHAVIOR
their real values. Then, according to the payment rule in the
PC-TTCC [e] mechanism, higher cost saving will be realized.
But in the experiments of “first-70%,” the platform’s profit is
much lower than that under VCG auction.
Table VII shows the impact of the “no-show” behavior.
In reality, some agents will have to give up the winning
parking slots. For example, some agents find the free and
more convenient parking slots in advance. Alternatively, some
agents have to cancel their travel plan due to the sudden severe
weather. In such a case, they will drop the booked parking
slots in another city or district that they would have visited.
In this computational test, the average percentage of no-show
ranges from 5% to 20%. The percentage of no-show follows
a normal distribution with mean (5%, 10%, and 20%) and
standard variance 0.05. Note that 0% of no-show represents
the benchmark case. When no-show occurs, the platform will
auction the extra parking slots to the public via a sequential
auction. There is no compensation for the no-shows. Since
the sequential auctions are conducted suddenly, the number of
bidders is only 50. Clearly, the platform’s profit will increase
by auctioning the extra parking slots. When the percentage of
no-show is 20%, the platform’s profit reaches U.S. $568.31,
nearly 16% larger than that in the benchmark case. Besides,
the agents’ cost saving decreases with the percentage of
no-show, and so bidders’ value does.
VI. CONCLUSION
Nowadays, the supply of parking infrastructures has not
been able to keep up with the increasing growth of traffic
mobility. Furthermore, the congested traffic of metropolitan
areas is usually caused by a number of cars that are cruising
for parking. To address constantly climbing parking needs,
parking spaces should be shared and allocated among different
businesses or residential communities in a city. Unfortunately,
there are few effective ways to balance the supply and demand
for both public and private parking spaces. To the best of our
knowledge, this paper is among the first to address the parking
space sharing and allocation problem from the integrated
market design and auction perspective.
In this paper, we first develop the IoT-UPM cloud. Based
on this systemic framework, parking spaces are shared among
agents via a PC-TTCC mechanism and the platform’s park-
ing spaces are reassigned via an O-VCG auction. Both the
PC-TTCC [e] mechanism and O-VCG auction are effective
in terms of strategy-proofness and (allocative or Pareto) effi-
ciency. In the PC-TTCC [e] mechanism, the platform’s pay-
ment rule used in private parking space sharing is determined
based on historical O-VCG auction prices. Our experimental
results further show that the proposed mechanism results in
system profitability of 20%–30% and ex post budget balance
for the platform.
Based on the experimental results, several key managerial
implications are also gained. First, a private platform should
not use the historical auction prices to determine his payment
rule in private parking space sharing if auction prices tend to be
high. However, in such a case a public platform should choose
the integrated PC-TTCC [e] and O-VCG mechanism that
realizes higher agents’ cost saving. Second, to reach higher
system profitability and agents’ cost saving, agents should be
encouraged to rent their private parking slots to the platform.
Third, the platform should leverage her owned public parking
spaces. For example, private and public parking spaces are
auctioned together. If the platform incorporates more public
parking spaces in one auction, then higher system profitability
and agents’ cost saving will be achieved. Fourth, compared
with VCG auction, the simpler first-price auction may lead
to higher cost saving for agents in some cases even if it
cannot realize allocative efficiency and incentive compatibility.
Finally, the platform’s profit will increase and the agents’ cost
saving will decrease with the percentage of no-show.
Regarding the future work, this paper can be extended to
addressing other resource sharing and allocation problems.
For example, our integrated PC-TTCC [e] and O-VCG mech-
anism can exploit the allocation and pricing problems in
B2B e-commerce logistics, auction logistics [20], on-demand
traffic fleet management, and ridesharing optimization (e.g.,
Uber, Didi). Second, some other factors such as transaction
costs and parking uncertainty can be incorporated into the
proposed mechanism. It would be useful to simulate different
parking arrival and departure scenarios. However, both the
mechanism design and revenue management problems will be
more challenging. Finally, it is worth incorporating several
traffic congestion factors into the proposed mechanism and
investigating the resulting release of traffic congestion.
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2016.
Xiang T. R. Kong received the M.Sc. and
Ph.D. degrees in industrial engineering from The
University of Hong Kong, Hong Kong, in 2012 and
2016, respectively.
Prior to studying at The University of Hong Kong,
he has conducted several real-life projects in
the logistics and shipping industry. He has pub-
lished several research publications which have
reported critical achievements from research results,
including Transportation Research Part B, the
International Journal of Production Research, Com-
puters and Industrial Engineering, Robotics and Computer-Integrated Man-
ufacturing, and Computers and Education. His current research interests
include physical internet (Internet of Things) for auction logistics, e-commerce
logistics, and intelligent warehousing.
Su Xiu Xu received the B.S. degree in mathematics
from the Harbin Institute of Technology, Harbin,
China, in 2008, and the Ph.D. degree in industrial
engineering from The University of Hong Kong,
Hong Kong, in 2014.
He is currently a Professor with the School
of Electrical and Information Engineering, Jinan
University, Guangdong, China. He has pub-
lished papers in such journals as Production
and Operations Management, Transportation Sci-
ence, IIE Transactions, Transportation Research
Part B, Transportation Research Part E, and the International Journal
of Production Economics. His major research interests are smart transportation
procurements, sharing economies, uncertainty and supply chain decisions, and
auction mechanism designs.
Prof. Xu is a member of the Institute of Industrial and Systems Engi-
neers (IISE) and the Institute for Operations Research and the Management
Sciences (INFORMS).
Meng Cheng received the B.Eng. degree in
mechanical engineering from the University of
Science and Technology of China, Hefei, China,
in 2011, and the Ph.D. degree in industrial engi-
neering from The University of Hong Kong, Hong
Kong, in 2015, respectively.
She has published several papers in reputable
journals, including Transportation Science, Trans-
portation Research Part B, and Transportation
Research Part E. Her research interests include
mechanism designs, auction theories, and machine
learnings.
George Q. Huang received the B.Eng. degree in
mechanical engineering from Southeast University,
Nanjing, China, and the Ph.D. degree in mechanical
engineering from Cardiff University, Cardiff, U.K.
He has conducted research projects in the field
of Physical Internet (Internet of Things) for Manu-
facturing and Logistics with substantial government
and industrial grants. He has published extensively
including over two hundred refereed journal papers
in addition to over 200 conference papers and ten
monographs. He is currently a Professor and the
Head of the Department of Industrial and Manufacturing Systems Engineering,
The University of Hong Kong, Hong Kong. His research works have been
widely cited in the relevant field.
Prof. Huang is a Chartered Engineer, a Fellow of The American Society of
Mechanical Engineers (ASME), Hong Kong Institution of Engineers (HKIE),
the Institution of Engineering and Technology (IET), and the Chartered
Institute of Logistics and Transport (CILT), and a member of the Institute
of Industrial Engineers (IIE). He serves as an associate editor and editorial
member for several international journals.