Research on Parking Recommendation Methods Considering Travelers’ Decision Behaviors and Psychological Characteristics

Abstract

Intelligent parking services can provide parking recommendations and reservations for travelers. They are an effective method for solving the cruising for parking problems in big cities. This research conducted a sequential parking decision behavior survey and analyzed travelers’ parking choices and reservation behaviors at different stages of the travel process. Then, a parking recommendation model was established to consider the travelers’ psychological thresholds and the attention to parking factors. The effects of different parking recommendation schemes in different situations were further explored based on parking simulations. It was concluded that travelers were more willing to accept and use the parking recommendation system. A total of 56% of travelers chose to make parking reservations during the travel process. The satisfaction proportion of the psychological threshold for the parking reservation group was higher than that for the non-parking reservation group. A dynamic parking recommendation scheme with a regulation threshold can change the recommendation strategy according to the overall utilization of parking lots. The implementation of the scheme can not only improve travelers’ parking experience, but it can also effectively balance the utilization of parking resources. It can be applied to different parking utilization situations and produce good performance. The research results provide references for the design and application of intelligent parking services in order to solve parking problems.

Full text

Citation: Qin, H.; Xu, N.; Zhang, Y.;
Pang, Q.; Lu, Z. Research on Parking
Recommendation Methods
Considering Travelers’ Decision
Behaviors and Psychological
Characteristics. Sustainability 2023,15,
6808. https://doi.org/10.3390/
su15086808
Academic Editors: El˙
zbieta Macioszek,
Anna Granà, Raffaele Mauro
and Margarida Coelho
Received: 19 February 2023
Revised: 11 April 2023
Accepted: 13 April 2023
Published: 18 April 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sustainability
Article
Research on Parking Recommendation Methods Considering
Travelers’ Decision Behaviors and Psychological Characteristics
Huanmei Qin 1, *, Ning Xu 1, Yonghuan Zhang 1, Qianqian Pang 2and Zhaolin Lu 1
1Beijing Key Lab of Traffic Engineering, Beijing University of Technology, Beijing 100124, China
2School of Transportation, Southeast University, Nanjing 214135, China
*Correspondence: hmqin@bjut.edu.cn
Abstract:
Intelligent parking services can provide parking recommendations and reservations for
travelers. They are an effective method for solving the cruising for parking problems in big cities. This
research conducted a sequential parking decision behavior survey and analyzed travelers’ parking
choices and reservation behaviors at different stages of the travel process. Then, a parking recommen-
dation model was established to consider the travelers’ psychological thresholds and the attention
to parking factors. The effects of different parking recommendation schemes in different situations
were further explored based on parking simulations. It was concluded that travelers were more
willing to accept and use the parking recommendation system. A total of 56% of travelers chose to
make parking reservations during the travel process. The satisfaction proportion of the psychological
threshold for the parking reservation group was higher than that for the non-parking reservation
group. A dynamic parking recommendation scheme with a regulation threshold can change the
recommendation strategy according to the overall utilization of parking lots. The implementation of
the scheme can not only improve travelers’ parking experience, but it can also effectively balance
the utilization of parking resources. It can be applied to different parking utilization situations and
produce good performance. The research results provide references for the design and application of
intelligent parking services in order to solve parking problems.
Keywords:
parking decision behavior; psychological threshold; attention to factors; parking
recommendation
1. Introduction
With the improvement of residents’ consumption levels, the rapid growth of motor
vehicles brings numerous parking problems. In urban central business districts, there is
a sh
ortage of parking supplies, which makes it more difficult for vehicles arriving later to
find a suitable parking space at their destination, even if there is no parking space. It makes
them have to spend more time searching for parking spaces in other areas, which leads to
the phenomenon of cruising for parking. Generally, it takes about 7 to 8 min for people to
find a suitable parking space, which reduces their parking efficiency [
1
]. When the number
of cruise vehicles increases to a certain extent, it will have an impact on the surrounding
road traffic, leading to traffic congestion, causing vehicles to drive at low speeds, generating
more emissions, and increasing environmental pollution [
2
]. The relevant research shows
that 30% of vehicles in the traffic flow cruise for parking [
1
]. Additionally, due to cruising
for parking, the road traffic flow has increased by 25–40% [
3
]. The annual economic loss
caused by cruising for parking in Schwabing, Germany, is estimated at EUR 20 million [
4
].
Intelligent parking services (IPS) can provide travelers with real-time parking occupancy
information by integrating wireless communication technology, mobile terminals, and
GPS. Parking recommendations and reservations can help travelers find a free parking
space faster and easier, thereby reducing their cruising time, improving parking efficiency,
and maximizing parking resources, thus greatly reducing the impact of cruising vehicles
Sustainability 2023,15, 6808. https://doi.org/10.3390/su15086808 https://www.mdpi.com/journal/sustainability

Sustainability 2023,15, 6808 2 of 22
on road traffic [
5
,
6
]. Therefore, IPS is an effective method to solve parking problems in
big cities.
Certain parking reservation systems, such as SpotAngel, can obtain the latest infor-
mation, which includes the location and pricing, as well as details concerning what free
and paid parking spaces are open. It can help travelers find available parking spaces and
parking prices, as well as inform them on the opening hours for parking lots. It can also
filter the available parking spaces by parking price, payment type, etc. Existing intelligent
parking services mostly show the location, number of vacant parking spaces, and parking
prices of parking lots within a certain range of their trip destination to travelers. However,
little research has been conducted on parking choice behavior, specifically considering the
psychological attention and thresholds of individual parking factors. Additionally, it is
also rare to consider recommending parking lots from both the perspectives of travelers
and managers. Based on the survey and analysis of sequential parking decision behaviors,
this research constructs a parking recommendation model and a personal parking decision
model, both of which consider the parking factors regarding the psychological thresholds
and attention of travelers. Different parking recommendation schemes were designed
from the perspectives of travelers and managers, and their applications were analyzed
from the perspectives of the parking utilization rate and some other simulation result
indicators, ultimately obtaining the optimal parking recommendation scheme. The effects
of the optimal recommendation scheme under different initial parking occupancy statuses,
parking reservation proportions, and parking regulation thresholds were also explored. The
research conclusions can provide a reference for the design and application of intelligent
parking systems and help to further reduce a series of urban parking problems.
2. Literature Review
Many scholars have carried out studies on parking choice behavior, as well as parking
recommendations and reservations. The studies concerning parking choice behavior
mainly focus on the influence of personal and travel-related factors [
7
]. Zhang and Zhu [
8
]
analyzed the on-street parking choice behaviors in the city center. They found that travelers
were willing to pay more when choosing an on-street parking space that is closer to the
destination. Khaliq et al. [
9
] used a mixed multinomial logit model to explore on-street
parking choice behaviors and concluded that factors such as parking price, intended
parking duration, and parking convenience all had a strong influence on travelers’ on-street
parking choices. Soto et al. [
10
] established a mixed discrete choice model and found that
the parking price and walking time after parking were two important factors that affect
travelers’ choices for paid and non-paid on-street parking spaces and paid public parking
lots. Tian et al. [
11
] proposed a dynamic parking pricing model to balance the utilization
of parking resources. It was concluded that the parking price and walking distance after
parking were important influencing factors for parking choice [11,12].
Intelligent parking services can provide parking information near the trip destination
for travelers and guide them to park their cars [
13
]. Cao and Menendez [
14
] concluded
that the adoption of intelligent parking services can significantly reduce travelers’ cruising
time for parking. ¸Sengör et al. [
15
] proposed an energy management model for an EV
parking lot (EVPL), based on real-time optimizations using linear programming, which can
maximize the utilization of a parking lot. Dogaroglu and Caliskanelli [
16
,
17
] concluded
that providing parking information, such as walking distance after parking and parking
fees by the intelligent parking guidance system (IPGS), can decrease the individual driving
distance and parking fees, balance the utilization of parking resources, and reduce pollutant
emissions. Khaliq et al. [
18
] proposed a mutual authentication mechanism to solve certain
privacy and security problems in existing intelligent parking systems.
As for intelligent parking recommendations, Huang et al. [
19
] introduced an intelligent
decision support system for the purpose of guiding travelers toward available on-street
parking spaces in urban areas. Li et al. [
20
] proposed a parking occupancy prediction
method to optimize the use of parking spaces. Fu et al. [
21
] considered the types, position,

Sustainability 2023,15, 6808 3 of 22
walking distance, and other such factors of available parking spaces in a parking lot in
order to design the optimal parking space recommendation model. The model could enable
the parking spaces to be used effectively and thus reduce the parking search time. Shin
and Jun [
22
] set fixed weights for the factors of travel time, walking distance after parking,
parking price, and traffic congestion levels in order to recommend the most suitable avail-
able spaces in parking lots. The parking simulation showed that it could improve parking
utilization. Based on the above studies, Safi et al. [
23
] added the factor of the driving safety
level to the parking factors that were considered in the previous document; he found that
the addition of this factor can help with effectively improving the utilization of parking
resources and thus reduce fuel consumption. Shin et al. [
24
] proposed a parking recom-
mendation model based on the neural network predictive control (NNPC) method. The
simulation results showed that the model can alleviate traffic congestion and allow parking
resources to be effectively utilized. Nine criteria for shared parking space allocations and
parking route recommendations were proposed by Zhao et al. [
25
]; they also provided
quantitative models for different conditions. The above studies mainly set certain weights
for parking factors in their respective parking recommendation models, and they were
found to improve the satisfaction of travelers with parking. While travelers usually have
different preferences and psychological needs that affect their parking choice behaviors.
In terms of parking reservations, Sadreddini et al. [
26
] proposed an intelligent reserva-
tion system that considers the behavior, state-of-charge value, parking lot usage history of
electric vehicle users, and parking space availability. The analytical hierarchy process in
multi-criteria decision-making techniques was used in the intelligent reservation system
to alleviate the parking difficulties of travelers’. Mei et al. (2019) [
27
] used genetic algo-
rithms to simulate and optimize the reserve ratio of reserved parking spaces in
a pa
rking
reservation system, and found that parking reservation can effectively improve the parking
lot revenue while ensuring the interests of travelers. In order to reduce the number of
inefficient reservation behaviors, Wang et al. [
28
] proposed a Blockchain-enabled Secure
Framework for Energy-Efficient Smart Parking in Sustainable City Environment, the results
of which show that it can effectively reduce the waste of parking resources. Liu et al. [
29
]
designed a parking guidance system to recommend on-street parking spaces in real time for
travelers to reserve spaces, based on the principle of the shortest distance. The simulation
results showed that the system can significantly reduce travelers’ driving and walking
costs. In order to minimize the total travel cost of all users, He et al. [
30
] proposed a
real-time parking reservation service. They developed a mixed-integer planning model
to efficiently allocate time slots and schedule drivers’ travel plans. Fu et al. [
31
] proposed
a rese
rvation-based parking recommendation model, which can filter parking lots based
on travelers’ demand, and then recommend parking lots, based on the maximum utility of
multiple attributes. The simulation experiment showed that the model can reduce cruising
time for parking and parking costs, and improve the utilization of parking facilities.
Overall, for the studies on parking choice behavior, travel behavior characteristics,
parking price, and walking distance after parking, it was found that these are all important
factors that affect travelers’ parking choices. The relevant studies have analyzed the
effectiveness regarding the application of intelligent parking service systems and discussed
parking recommendations and reservations based on the parking factors. However, few
studies have focused on parking choice behavior based on individual psychological factors
of parking, nor have they considered the interests of travelers and parking managers for
parking recommendations.
In this research, parking recommendation methods are studied based on travelers’
decision behaviors and psychological characteristics. The contents include the following:
(1) Designing a sequential parking behavior survey and obtaining travelers’ decision-
making behavior data at the decision points before a trip, during a trip, and near the
destination. Then, a parking recommendation model and an individual parking decision
process model were both established. (2) Based on a real-time acquisition of travelers’
psychological thresholds and levels of attention to parking factors, taking into account the

Sustainability 2023,15, 6808 4 of 22
benefits of travelers and managers, design static, and dynamic parking recommendation
schemes. (3) Based on parking simulations, the effectiveness of different parking recom-
mendation schemes was also explored. A deep analysis of the performance of the optimal
scheme under different conditions was also carried out. Finally, certain suggestions are
then put forward in order to solve parking problems.
3. Survey of Sequential Parking Decision Behavior under Parking Recommendations
3.1. Design and Implementation of the Survey
In order to analyze travelers’ parking decision behaviors under a parking recom-
mendation system, a stated preference was used to design a parking behavior survey for
shopping trips. First, we asked people if they drive regularly to filter out the groups that
travel by car, and then we conducted a questionnaire. The survey contents consist of the
following parts:
(1)
Personal information of the travelers.
The personal information includes gender, age, occupation, and monthly income.
(2)
Travelers’ attention to factors influencing parking choice.
According to the pilot survey, the influencing factors mainly include walking distance
after parking, parking price, and driving time to the parking lot. The options for the degree
of attention were set from “very unimportant (1)” to “very important (5)”, as per a 5-point
Likert scale.
(3)
The individual psychological thresholds for the factors influencing parking choice.
The personal psychological threshold refers to the upper and lower limit of a person’s
mental endurance or perceptual ability. In this article, when a parking factor in the parking
lot is beyond or less than an individual’s acceptable value, the travelers will give up
choosing the parking lot. For example, when the price of parking exceeds the acceptable
value to the traveler, the traveler will abandon their choice of the parking lot. This paper
obtains the threshold of the walking distance after parking, the threshold of the parking
price, and the threshold of the available vacant parking spaces.
(4)
Stated preference survey on sequential parking decision behaviors.
It is assumed that a shopping travel scenario is presented by a screenshot of the Baidu
map (Baidu Maps is an application that can display the location of the travel start and
end points, travel distance, and travel routes, as well as the distribution of parking lots
and parking information near travel destinations.). Establish shopping parking scenes
for questionnaire survey. The trip origin of the home (i.e., the Century oriental garden
community) and the trip destination of the Xidan Joy City are marked on the map. The
travel distance is about 15 km and the travel activities of shopping last about 3 h. During the
travel process, travelers can check the real-time parking information through the parking
recommendation system and reserve a parking space as required.
The whole shopping travel process is divided into three decision points, including
before the trip (15 km away from the destination), during the trip (7.5 km away from the
destination), and near the destination (2 km away from the destination). Firstly, the traveler
is asked whether they need to view the parking information before the trip. If their answer
is “Yes”, then the real-time information of the parking lots within a certain area near the
destination will be presented, including the distribution of parking lots, the number of
vacant parking spaces, parking price, walking distance after parking, and travel time to the
parking lot. Figure 1shows an example of the distribution and real-time information of
the parking lots. Then, the traveler is asked whether they need to make a decision about
the parking lot based on the information presented. If their answer is “Yes”, they will be
provided with an opportunity to choose a parking lot from among the available parking
lots. At the same time, the parking recommendation system will provide a recommended
parking lot to the traveler. The traveler can then reserve a parking lot of their own choice or
as recommended by the parking recommendation system. If the traveler reserves a parking

Sustainability 2023,15, 6808 5 of 22
lot, the parking decision process will then end. Otherwise, the traveler will continue to
answer the questions at the next decision point.
Sustainability 2023, 14, x FOR PEER REVIEW 5 of 22
area near the destination will be presented, including the distribution of parking lots, the
number of vacant parking spaces, parking price, walking distance after parking, and
travel time to the parking lot. Figure 1 shows an example of the distribution and real-time
information of the parking lots. Then, the traveler is asked whether they need to make a
decision about the parking lot based on the information presented. If their answer is “Yes”,
they will be provided with an opportunity to choose a parking lot from among the
available parking lots. At the same time, the parking recommendation system will provide
a recommended parking lot to the traveler. The traveler can then reserve a parking lot of
their own choice or as recommended by the parking recommendation system. If the
traveler reserves a parking lot, the parking decision process will then end. Otherwise, the
traveler will continue to answer the questions at the next decision point.
Figure 1. Parking lot distribution and real-time information around the trip destination.
If the traveler does not view the parking information or reserve a parking lot at a
decision point, they will be presented with the questions and the updated real-time
parking information at the next decision point until they reach their destination. The
seing of the questions for each decision point is similar. The whole parking decision
process is shown in Figure 2. The recommended parking lot by the parking
recommendation system is obtained through the parking recommendation model in
Section 4.1. For travelers who do not view any of the parking information that is provided
by the parking recommendation system during the whole travel process, they will then
be provided with the choice of parking lots that have available parking spaces, ordered
from the nearest to the furthest, after arriving at their destination.
The survey contents were compiled through the Questionnaire Star platform, which
can perform the question-hopping design for different decision points during the travel
process. The questionnaire was distributed through the internet from December 2020 to
January 2021. Persons who own a car are qualified to answer the questionnaire. A total of
692 samples were returned, and there were 633 valid samples.
Figure 1. Parking lot distribution and real-time information around the trip destination.
If the traveler does not view the parking information or reserve a parking lot at
a dec
ision point, they will be presented with the questions and the updated real-time
parking information at the next decision point until they reach their destination. The setting
of the questions for each decision point is similar. The whole parking decision process is
shown in Figure 2. The recommended parking lot by the parking recommendation system
is obtained through the parking recommendation model in Section 4.1. For travelers who do
not view any of the parking information that is provided by the parking recommendation
system during the whole travel process, they will then be provided with the choice of
parking lots that have available parking spaces, ordered from the nearest to the furthest,
after arriving at their destination.
The survey contents were compiled through the Questionnaire Star platform, which
can perform the question-hopping design for different decision points during the travel
process. The questionnaire was distributed through the internet from December 2020 to
January 2021. Persons who own a car are qualified to answer the questionnaire. A total of
692 samples were returned, and there were 633 valid samples.
3.2. Graphic Characteristics and Differential Statistics of the Survey Data
(1)
Personal information.
The majority of participants in the survey samples were men, accounting for 61%. The
age of the participants in the samples was mainly distributed between 26 and 3
5 yea
rs
old, which accounted for 41%; this was followed by those who were 36~45 years old,
which accounted for 33%. A total of 31% of the participants in the samples were pro-
fessional and technical personnel, followed by 26% and 18% for freelancers and public
institution/enterprise personnel, respectively. The participants’ personal monthly income
mainly ranged from CNY 5000 to 10,000, accounting for 37%, followed by CNY 3000~5000
and CNY 10,000~15,000, accounting for 20% each, respectively.

Sustainability 2023,15, 6808 6 of 22
(2)
Analysis of the travelers’ attention to the factors influencing parking choice.
As shown in Figure 3, most travelers think that the walking distance after parking,
driving time, and parking price are the most important factors for their parking choice with
respect to a shopping trip. The “more important” label accounts for the largest proportion
of the three parking factors, accounting for 53%, 43%, and 38%, respectively. This, therefore,
indicates that travelers pay more attention to the walking distance after parking, driving
time, and parking price for their parking choice.
Sustainability 2023, 14, x FOR PEER REVIEW 6 of 22
Figure 2. The design of a parking decision process for different decision points.
3.2. Graphic Characteristics and Differential Statistics of the Survey Data
(1) Personal information.
The majority of participants in the survey samples were men, accounting for 61%.
The age of the participants in the samples was mainly distributed between 26 and 35 years
old, which accounted for 41%; this was followed by those who were 36~45 years old, which
accounted for 33%. A total of 31% of the participants in the samples were professional and
technical personnel, followed by 26% and 18% for freelancers and public
institution/enterprise personnel, respectively. The participants’ personal monthly income
mainly ranged from CNY 5000 to 10,000, accounting for 37%, followed by CNY 3000~5000
and CNY 10,000~15,000, accounting for 20% each, respectively.
(2) Analysis of the travelers’ aention to the factors influencing parking choice.
As shown in Figure 3, most travelers think that the walking distance after parking,
driving time, and parking price are the most important factors for their parking choice
with respect to a shopping trip. The “more important” label accounts for the largest
proportion of the three parking factors, accounting for 53%, 43%, and 38%, respectively.
This, therefore, indicates that travelers pay more aention to the walking distance after
parking, driving time, and parking price for their parking choice.
(3) Analysis of travelers’ psychological thresholds for the parking factors influencing
parking choice.
Figure 4 shows that 84% of travelers will not consider choosing a parking lot that has
a walking distance of more than 800 m from the destination of their shopping trip. This
indicates that travelers’ acceptable psychological threshold for the walking distance after
parking is, in the main, less than 800 m. In addition, when the parking price for a parking
lot exceeds 15 CNY/h, 86% of travelers will not consider the parking lot. The travelers’
Travelers
Using
p
arking
recommen
d
ation
system
Without
p
arking
recommen
d
ation
system
Reserve a
recommended
p
arking
lot
Reserve the on
e
chosen
b
y your own
Before the trip
Yes
N
o
During the trip Near the destination
View parking
information？
Make a
parking lot
choice?
Parking
choice at
destination
Reserve a
parking lot?
Ye
s
No
No
View parking
information？
Yes
Ye
s
View parking
information？
N
o
Ye
s
Yes
No
No
Parking
choice at
destination
Yes Ye
s
Ye
s
No
N
o
No
Reserve a
parking lot?
Reserve a
parking lot?
or
Make a
parking lot
choice?
Make a
parking lot
choice?
Parking decision process is finished
Figure 2. The design of a parking decision process for different decision points.
(3)
Analysis of travelers’ psychological thresholds for the parking factors influencing
parking choice.
Figure 4shows that 84% of travelers will not consider choosing a parking lot that has
a walking distance of more than 800 m from the destination of their shopping trip. This
indicates that travelers’ acceptable psychological threshold for the walking distance after
parking is, in the main, less than 800 m. In addition, when the parking price for a parking
lot exceeds 15 CNY/h, 86% of travelers will not consider the parking lot. The travelers’
psychological threshold for parking price was found to be, in the main, less than 15 CNY/h.
A total of 88% of travelers would not consider choosing a parking lot with fewer than
tw
o avail
able spaces, which means that the psychological threshold for available parking
spaces is greater than or equal to two. The “Any” represents that they can accept any
one choice.

Sustainability 2023,15, 6808 7 of 22
Sustainability 2023, 14, x FOR PEER REVIEW 7 of 22
psychological threshold for parking price was found to be, in the main, less than 15
CNY/h. A total of 88% of travelers would not consider choosing a parking lot with fewer
than two available spaces, which means that the psychological threshold for available
parking spaces is greater than or equal to two. The “Any” represents that they can accept
any one choice.
Figure 3. Aention to factors influencing parking choice.
Figure 4. Psychological thresholds for the parking factors influencing parking choice.
(4) Choice intention regarding the sequential parking decision process.
It can be seen from Figure 5 that the proportions of travelers who do not use the
parking recommendation system to view information and those who view parking
information without reserving a parking lot before a trip are 36% and 26%, respectively.
Travelers who reserve a parking lot that is recommended by the parking reservation
system and those who choose a parking lot on their own are 35% and 4%, respectively.
Among the 62% of travelers who do not make a parking reservation before a trip, 42% of
111
645
17
24 25
53
43
38
24 28 31
0
10
20
30
40
50
60
Walking distance
after parking
Driving time to the
parking
Parking price
Proportion (%)
Very unimportant Unimportant Generally important
More important Very important
4
14
16
17
31
23
33
33
34
21
17
13
13
5
5
12
36
0% 20% 40% 60% 80% 100%
Number of
available
parking
space
Parking
price
Walking
distance
after
parking
30 20 10 5 2 Any
20 CNY/h 15 CNY/h 10 CNY/h 8 CNY/h Any
1000m 800m 500m 300m200m100mAny
Figure 3. Attention to factors influencing parking choice.
Sustainability 2023, 14, x FOR PEER REVIEW 7 of 22
psychological threshold for parking price was found to be, in the main, less than 15
CNY/h. A total of 88% of travelers would not consider choosing a parking lot with fewer
than two available spaces, which means that the psychological threshold for available
parking spaces is greater than or equal to two. The “Any” represents that they can accept
any one choice.
Figure 3. Aention to factors influencing parking choice.
Figure 4. Psychological thresholds for the parking factors influencing parking choice.
(4) Choice intention regarding the sequential parking decision process.
It can be seen from Figure 5 that the proportions of travelers who do not use the
parking recommendation system to view information and those who view parking
information without reserving a parking lot before a trip are 36% and 26%, respectively.
Travelers who reserve a parking lot that is recommended by the parking reservation
system and those who choose a parking lot on their own are 35% and 4%, respectively.
Among the 62% of travelers who do not make a parking reservation before a trip, 42% of
111
645
17
24 25
53
43
38
24 28 31
0
10
20
30
40
50
60
Walking distance
after parking
Driving time to the
parking
Parking price
Proportion (%)
Very unimportant Unimportant Generally important
More important Very important
4
14
16
17
31
23
33
33
34
21
17
13
13
5
5
12
36
0% 20% 40% 60% 80% 100%
Number of
available
parking
space
Parking
price
Walking
distance
after
parking
30 20 10 5 2 Any
20 CNY/h 15 CNY/h 10 CNY/h 8 CNY/h Any
1000m 800m 500m 300m200m100mAny
Figure 4. Psychological thresholds for the parking factors influencing parking choice.
(4)
Choice intention regarding the sequential parking decision process.
It can be seen from Figure 5that the proportions of travelers who do not use the parking
recommendation system to view information and those who view parking information
without reserving a parking lot before a trip are 36% and 26%, respectively. Travelers who
reserve a parking lot that is recommended by the parking reservation system and those
who choose a parking lot on their own are 35% and 4%, respectively. Among the 62% of
travelers who do not make a parking reservation before a trip, 42% of them do not use the
parking recommendation system, and 13% of them view information without reserving
a park
ing lot, respectively. In addition, only 7% of travelers choose to reserve a parking lot
at this decision point. Overall, 55% of travelers who did not make parking reservations
during the previous two decision points. As travelers drive closer to their destination, 34%
of them still do not use the parking recommendation system, and 11% of them use the

Sustainability 2023,15, 6808 8 of 22
parking recommendation system only to view information without making a reservation.
The proportion of travelers who reserve a parking lot is 10% at the decision point near the
destination. It can be concluded that most travelers choose to make a parking reservation
before the trip, accounting for 39%, which is then followed by those making a reservation
when near the destination, and then those during the trip. Overall, 56% of travelers make
parking reservations at some point during the whole travel process. Furthermore, most of
them, accounting for 48%, choose to reserve the parking lot that is recommended by the
recommendation system. This indicates that travelers are more willing than not to accept
the parking recommendation system.
Sustainability 2023, 14, x FOR PEER REVIEW 8 of 22
them do not use the parking recommendation system, and 13% of them view information
without reserving a parking lot, respectively. In addition, only 7% of travelers choose to
reserve a parking lot at this decision point. Overall, 55% of travelers who did not make
parking reservations during the previous two decision points. As travelers drive closer to
their destination, 34% of them still do not use the parking recommendation system, and
11% of them use the parking recommendation system only to view information without
making a reservation. The proportion of travelers who reserve a parking lot is 10% at the
decision point near the destination. It can be concluded that most travelers choose to make
a parking reservation before the trip, accounting for 39%, which is then followed by those
making a reservation when near the destination, and then those during the trip. Overall,
56% of travelers make parking reservations at some point during the whole travel process.
Furthermore, most of them, accounting for 48%, choose to reserve the parking lot that is
recommended by the recommendation system. This indicates that travelers are more
willing than not to accept the parking recommendation system.
Figure 5. Parking choice proportions at different travel decision points.
4. Parking Recommendation and Individual Parking Decision Process Models
4.1. Parking Recommendation Model
4.1.1. The Parking Recommendation Process, Considering Psychological Thresholds and
Aention
It is assumed that in a parking recommendation system, the starting point and
destination of travelers’ travel can be obtained through a mobile terminal application, and
the aention and psychological threshold of parking factors that affect the parking choices
can be obtained. At the same time, the parking recommendation system can receive real-
time location information from travelers, and then collect real-time information from
nearby parking lots to present it to them.
The parking recommendation process is as follows: Firstly, the candidate parking lots
meeting the travelers’ needs at a decision point are tentatively selected based on the travel
information, individual parking factors psychological threshold, and the real-time
information of the parking lots near the destination. Secondly, the aribute information
of the candidate parking lots is standardized, and the utility of each candidate parking lot
is calculated from the perspective of travelers’ and managers’ interests, respectively.
Finally, comprehensive utility scores of the candidate parking lots are obtained based on
36
42
34
26
13
11
4
2
2
35
5
8
0 20406080100
Before the trip
（100%）
During the trip
（62%）
Near the destination
（55%）
Choice proportion (%)
Do not use the parking recommendation system
View information without reservation
Reserve a parking lot of their own choice
Reserve a parking lot recommended
Figure 5. Parking choice proportions at different travel decision points.
4. Parking Recommendation and Individual Parking Decision Process Models
4.1. Parking Recommendation Model
4.1.1. The Parking Recommendation Process, Considering Psychological Thresholds
and Attention
It is assumed that in a parking recommendation system, the starting point and desti-
nation of travelers’ travel can be obtained through a mobile terminal application, and the
attention and psychological threshold of parking factors that affect the parking choices can
be obtained. At the same time, the parking recommendation system can receive real-time
location information from travelers, and then collect real-time information from nearby
parking lots to present it to them.
The parking recommendation process is as follows: Firstly, the candidate parking
lots meeting the travelers’ needs at a decision point are tentatively selected based on the
travel information, individual parking factors psychological threshold, and the real-time
information of the parking lots near the destination. Secondly, the attribute information
of the candidate parking lots is standardized, and the utility of each candidate parking
lot is calculated from the perspective of travelers’ and managers’ interests, respectively.
Finally, comprehensive utility scores of the candidate parking lots are obtained based on the
adjustment coefficient for the proportions of the two-part utilities. Then, a better parking
lot is obtained, based on the principle of utility maximization, and provided to the travelers.
The recommendation process is divided into three steps, which is detailed below.
Step 1. The determination of candidate parking lots.
According to the information about parking lots at the decision point near the desti-
nation, firstly, the parking lots that meet the individual’s psychological threshold for the

Sustainability 2023,15, 6808 9 of 22
parking factors—specifically for all three factors, including walking distance after parking,
parking price, and number of available parking spaces—are established. If there is no
parking lot that satisfies all of the above screening criteria, select parking lots that meet the
psychological threshold of any of the three factors as candidate parking lots. If there is still
no parking lot that satisfies the above two screening criteria, all of the parking lots with
vacant spaces within a certain range area around the destination can be used as candidate
parking lots. The variable Zrepresents the set of candidate parking lots. Z
tij
is set to 1 if the
parking lot jnear the destination satisfies the screening process for traveler iat decision
time t, otherwise it is 0.
Step 2. Standardization of the attribute information of candidate parking lots.
According to the candidate parking lots in Z
ti
and their real-time information at
a dec
ision point, the influencing factor matrix B
ti
of candidate parking lots for traveler i
at decision time tis obtained. In regard to B
ti
= {L
k
, C
k
, O
tk
}, kis the number of candidate
parking lots, L
k
is the walking distance from the kth parking lot to the destination, C
k
is the
parking price of the kth parking lot, and O
tk
is the number of available parking spaces in
the kth parking lot at time t.
The influencing factors of the candidate parking lots are standardized from the perspec-
tive of travelers’ and managers’ interest, respectively. If the traveler’s benefit is considered,
the standardization method for the factors of the candidate parking lots is shown in For-
mula (1). That is to say that the lower the parking price and the shorter the walking
distance after parking, then the greater the traveler’s benefit. The standardized matrix for
the influencing factors of the candidate parking lots for traveler iis R1
ti ={lk,ck}.
lk=min(Lk)
Lk
ck=min(Ck)
Ck
(k=1, 2, · · · m)(1)
where l
k
and c
k
are the standardized values for the walking distance after parking and
parking price for the kth candidate parking lot, respectively. In addition, mis the number
of candidate parking lots.
From the perspective of parking managers’ benefits, the focus is more on the perfor-
mance of parking lot utilization. In areas with high parking demand, using the parking
recommendation system can guide travelers to park their car in parking lots that have more
vacant spaces, thus reducing cruising for parking and improving the effective utilization of
parking resources. Therefore, the standardization for the factor of the number of available
parking spaces is shown in Formula (2). The standardized matrix for the influencing factors
that relate to parking managers is R2
tg ={otk }.
otk =Otk
max(Otk )(k=1, 2, · · · m)(2)
where o
tk
is the standardized value of the number of the available parking spaces of the kth
candidate parking lot at time t.
Step 3.
The calculation of the utility of candidate parking lots and parking recommen-
dations.
Based on the traveler’s degree of attention to the influencing factors that are received
in real time by the parking recommendation system, the weight vector W
i
= [w
il
,w
ic
] is
the walking distance after parking and the parking price for traveler iis calculated by
Formula (3).
wiq =diq
c
∑
q=l
diq
(q=l,c)(3)
where diq is the degree of attention to influencing factor qfor traveler i.

Sustainability 2023,15, 6808 10 of 22
The utilities of the candidate parking lots for traveler iat decision time tis obtained by
Formula (4). Then, the parking lot with the highest utility is selected and recommended to
traveler i.
Eti =λWiR1
ti + (1−λ)R2
tg (4)
where λis the adjustment coefficient for the two-part utilities for travelers and managers.
4.1.2. Parking Recommendation Schemes That Consider the Interests of Travelers
and Managers
The static and dynamic parking recommendation schemes can be obtained by the
varying adjustment coefficients, as shown in Table 1. When the adjustment coefficient
λ
is set to fixed values of 1, 0, and 0.5, three static parking recommendation schemes
can be obtained. Schemes 1, 2, and 3 show the cases that represent the maximization
of travelers’ benefits, balancing parking resource utilization, and the combined benefits
of travelers and parking managers, respectively. When the adjustment coefficient
λ
is
dynamically changed with the utilization status of the parking lots, two dynamic parking
recommendation Schemes 4 and 5, can be obtained. Scheme 5 includes a threshold to start
parking regulation θ.
Table 1. Parking recommendation schemes.
Schemes Adjustment
Coefficient λDescription
Static parking
recommendation
Scheme 1 λ=1Parking recommended with the goal of maximizing the
benefits of travelers.
Scheme 2 λ=0Parking recommended with the goal of balancing parking
resource utilization.
Scheme 3 λ=0.5 Parking recommended based on the combined benefits of
travelers and parking managers.
Dynamic parking
recommendation
Scheme 4 λ=1−Ptk
Ptk is the average occupancy rate of the parking lots near the
destination at time t. If Ptk is high, then the parking lot is
recommended mainly based on the benefits of parking
managers. If Ptk is low, then the parking recommendation is
made mainly based on maximizing the benefits of travelers.
Scheme 5
λ=1−Ptk,
if Pm
tk >θ;
λ=1,
if Pm
tk ≤θ
Pm
tk is the median of the occupancy rates of the parking lots
near the destination at time t. When Pm
tk is greater than θ, the
parking regulation is triggered, and the parking
recommendation is made based on the benefits of parking
managers. When
Pm
tk
is less than or equal to
θ
, the parking lot
is recommended with the goal of maximizing the benefits of
the travelers.
4.2. Individual Parking Decision Process Model
For the travelers who use the parking recommendation system to view the park-
ing information, they may reserve their own chosen parking lot, or they may not make
a res
ervation during the whole travel process. Their parking decision process is as follows:
Firstly, based on the individual psychological thresholds regarding the parking factors,
as well as the real-time information of the parking lot, the parking lots that satisfy trav-
elers’ needs are screened as the initial acceptable parking lots. The screening process is
similar to Step 1 in Section 4.1.1. If there is only one parking lot screened out, it will be
the final parking choice for the trip during the individual decision process. If there is
more than one parking lot selected through the above screening process, those remaining
acceptable parking lots will be compared according to the traveler’s degree of attention
to the parking factors of walking distance after parking, driving time, and parking price.
From the most concerned to the least concerned factors, the comparison of the remaining

Sustainability 2023,15, 6808 11 of 22
parking lots will continue until only one parking lot is left. If no parking lot is obtained by
filtering on all three parking factors, a randomly selected one from the initial acceptable
parking lots is regarded as the final parking choice. For travelers who do not use the
parking recommendation system, their parking decision process follows the principle of
proximity parking.
5. Parking Simulation and Analysis in Different Parking Recommendation Schemes
5.1. Initial Settings of Parking Simulation
Based on the shopping travel scenario in the survey on sequential parking decision
behaviors, the initial information of five parking lots within 1 km (after the preliminary
investigation, it was found that the acceptable walking distance threshold for people after
parking is mostly within 1 km) around the destination of Xidan Joy City is shown in Table 2.
The number of parking spaces in each parking lot is about 200. Differentiated parking fees
are applied in these parking lots and parking prices gradually decrease with the increasing
distance from the destination.
Table 2. Initial information of the parking lots near the destination.
Parking
Lots
Distance from the Parking
Lot to the Destination,
the Joy City (m)
The Number of
Available Parking Spaces
Parking
Price (CNY/h)
P1 60 20 10
P2 100 80 10
P3 250 80 8
P4 520 100 8
P5 600 100 5
It is assumed that car travelers are randomly generated in the range of 5–30 km around
the shopping center. The trip generation rate follows a Poisson distribution with a mean
value of 23 vehicles per 5 min. Travel distances of less than 5 km are mainly covered
by non-motorized travel modes or public transport. We assume that people make the
same proportion of parking choices before the trip, during the trip, and while near the
destination as the data obtained in the questionnaire. According to the traffic status in
Beijing, the average driving speed of cars is between 20 km/h and 50 km/h. The parking
time distribution of car travelers in the shopping center is set according to the field survey.
The parking time of less than 0.5 h accounts for 4%, 0.5 h to 1 h accounts for 8%, 1 h to 2 h
accounts for 16%, 2 h to 3 h accounts for 32%, 3 h to 4 h accounts for 22%, and more than
4 h
accounts for 18%. The vehicle will leave the parking lot automatically after reaching the
parking time. The initial parked vehicles in the parking lot leave at a rate of three vehicles
per 5 min.
The whole travel process is divided into three decision points, including before the
trip (the location of trip generation), during the trip (midpoint of the trip), and near the
destination (2 km away from the destination). Travelers can view parking information,
and choose or reserve a parking lot at each decision point during the travel process. For
the decision points before the trip, during the trip, and near the destination, the parking
reservation proportions are 35%, 5%, and 8%, respectively, based on the parking decision
behavior survey data in Figure 5. The travelers’ parking factor psychological thresholds
and attentions to parking influencing factors are randomly assigned to the car travelers
through the surveyed sample data. The parking reservation fee is CNY 2 per reservation.
Parking is charged every 15 min, and anything less than 15 min is counted as 15 min.
Based on the parking recommendation model and individual parking decision process
model, the travelers’ parking process for the shopping trip under the parking recommenda-
tion service is dynamically simulated using Python programming. The simulation lasts
about 270 min. The threshold to start parking regulations is taken as 70%. As a whole, the
number of vehicles arriving at the shopping center is greater than that which are leaving.

Sustainability 2023,15, 6808 12 of 22
5.2. Analysis of Parking Simulation in Different Parking Recommendation Schemes
Parking simulation is carried out for the parking recommendation schemes from
o
ne to fiv
e to analyze the dynamic change in parking occupancy status, and to evaluate the
performance of the schemes using multiple indicators.
(1)
The dynamic change in parking occupancy under different parking recommendation
schemes.
As shown in Figure 6a, for the static parking recommendation Scheme 1, that is
λ= 1
,
the parking recommendations are made from the perspective of maximizing travelers’
benefits. The parking lots that are closer to the destination are first recommended to
travelers for their choice, and their occupancy rate increases quickly. When these nearby
parking lots are full, the distant one is recommended for their parking choice and its
occupancy rate begins to increase. Although the walking distance after parking for Park 5
is slightly farther than Park 4, the choice proportion of Park 5 was higher due to its lower
parking price and higher integrated utility. For the static parking recommendation Scheme
2, that is
λ
= 0, parking recommendations are made from the perspective of manager’s
benefits with the goal of balancing the utilization of parking resources. Figure 6b shows
that the occupancy rates of the parking lots can basically be kept at the same level and
present the same trends with the increase or decrease in parking demand after a period of
adjustment. For the static Scheme 3, that is
λ
= 0.5, the interests of travelers and managers
are considered at the same time for the purposes of parking recommendation. Figure 6c
demonstrates that the changing trends in parking occupancy rates are the combined result
of the parking recommendations of Scheme 1 and Scheme 2. Therefore, the smaller the
adjustment coefficient
λ
, then the larger the proportion for the utility of the parking
manager and the easier it is to achieve balanced utilization among the parking lots for
a short period of time.
The dynamic parking recommendation Scheme 4 and Scheme 5 can change the recom-
mendation mechanism according to the overall utilization of parking lots. As for Scheme
4, due to the relatively high mean value of the initial parking occupancy rate of 62% for
these five parking lots, the parking recommendation is mainly made from the perspective
of maximizing traveler benefits and balancing parking resource utilization. Figure 6d
shows that the occupancy rates of parking lots near the destination increase along with the
parking demand. In addition, the adjustment coefficient
λ
accordingly decreases gradually.
As a result, the changing trend of the occupancy rate of each parking lot under Scheme 4
is essentially similar to that of Scheme 2. Parking recommendations under Scheme 5 are
based on the median of the occupancy rates of parking lots, combined with the threshold
in terms of starting parking regulations. Figure 6e shows that in the initial stage, the
median occupancy rates of the parking lots near the destination is 62% and less than the
threshold to start parking regulation of 70%. At this time, parking recommendations are
made for the maximization of travelers’ benefits. Overall, the occupancy rate of parking
lots gradually increases sequentially from the nearest to the farthest from the destination.
When the simulation time approaches around 65 min, the median parking occupancy rates
reaches the regulation threshold of 70%. It is then time to start the parking regulation by
the parking recommendation system with the goal of balancing the utilization of parking
resources while ensuring the interests of travelers. As a result, the trends in the occupancy
rates of parking lots slowly moves closer to equilibrium over time.
(2)
Evaluation of simulation results in different parking recommendation schemes.
In order to evaluate the effects of the implementation of different parking recom-
mendation schemes, multiple indicators are analyzed from the perspective of travelers
and parking managers, as shown in Table 3. For the travelers’ benefits, the proportion
of meeting the psychological threshold of the three parking factors is calculated, as well
as the proportion of psychological thresholds that are primarily concerned with parking
factors and the proportion of psychological thresholds that are secondarily concerned with
parking factors. The three factors that affect the parking choice include the walking dis-

Sustainability 2023,15, 6808 13 of 22
tance after parking, parking price, and the number of available parking spaces. Meanwhile,
the evaluation indicators for travelers also consist of the average walking distance after
parking and the average parking fees for their final chosen parking lot. The statistics are
displayed in three groups. One is a parking reservation group, who make a reservation for
a parking lot as recommended by the parking reservation system or by choosing on their
own. The next is the non-parking reservation group, who do not make a reservation or use
the parking recommendation system. Then, the last group includes all the travelers. The
cumulative parking revenue is also given from the parking manager’s perspective.
Sustainability 2023, 14, x FOR PEER REVIEW 13 of 22
initial stage, the median occupancy rates of the parking lots near the destination is 62%
and less than the threshold to start parking regulation of 70%. At this time, parking
recommendations are made for the maximization of travelers’ benefits. Overall, the
occupancy rate of parking lots gradually increases sequentially from the nearest to the
farthest from the destination. When the simulation time approaches around 65 min, the
median parking occupancy rates reaches the regulation threshold of 70%. It is then time
to start the parking regulation by the parking recommendation system with the goal of
balancing the utilization of parking resources while ensuring the interests of travelers. As
a result, the trends in the occupancy rates of parking lots slowly moves closer to
equilibrium over time.
(a)
(b)
(c)
(d)
(e)
Figure 6. Dynamic change in the parking occupancy rate for different parking recommendation
schemes: (a) Scheme 1; (b) Scheme 2; (c) Scheme 3; (d) Scheme 4; and (e) Scheme 5.
Figure 6.
Dynamic change in the parking occupancy rate for different parking recommendation
schemes: (a) Scheme 1; (b) Scheme 2; (c) Scheme 3; (d) Scheme 4; and (e) Scheme 5.

Sustainability 2023,15, 6808 14 of 22
Table 3. The simulation results in the different parking recommendation schemes.
Schemes
Indicators Related to Travelers
(All/Parking Reservation Group/Non-Parking Reservation Group)
Indicators Related to
Parking Manager
Satisfaction of Psychological Needs (%) Average Walking
Distance after
Parking (m/Person)
Average Parking
Fee (Including
Reservation Fee)
(CNY/Person)
Cumulative
Parking Revenue
(CNY)
All Three
Factors
Primary
Concerned
Factor
Secondary
Concerned
Factor
Scheme 1 71/74/66 84/85/83 82/84/80 267/262/273 24/24/24 28,961
Scheme 2 97/98/96 92/95/89 91/92/89 298/362/211 24/23/23 28,264
Scheme 3 97/98/96 92/95/89 91/92/89 294/359/209 24/23/24 28,309
Scheme 4 97/98/96 92/95/89 91/92/89 297/362/208 24/23/25 28,239
Scheme 5 92/92/91 91/92/87 89/90/88 287/331/227 24/23/24 28,499
Table 3shows that there are about 90% or more travelers whose parking lot choice
satisfies their psychological thresholds for all three factors as well as primary and secondary
concerned factors under the parking recommended Schemes 2, 3, 4, and 5, while the
satisfaction proportions for the parking recommendation of Scheme 1 are lower, which are
71%, 84%, and 82%, respectively. However, the average walking distance after parking for
Scheme 1 is the shortest, with a value of 267 m/person. However, the average parking fee is
approximately the same for all schemes. There are no significant differences for cumulative
parking revenue among the schemes, with a maximum of CNY 28,961 in Scheme 1.
The satisfaction proportions of the psychological needs for the parking reservation
group are higher than those for the non-parking reservation group under different parking
recommendation schemes. Meanwhile, the average walking distance after parking for
the parking reservation group is significantly higher than that of non-parking reservation
group under the Schemes 2, 3, 4, and 5. The average parking fee for the non-parking
reservation group was found to be slightly higher, which is because they do not view
the parking information and mainly choose the closer parking lot to the destination with
a higher parking price. This indicates that using the parking recommendation system
can improve individual parking experience and satisfaction, although it may significantly
increase the walking distance after parking.
In summary, the static parking recommendation Scheme 1, which recommends the
parking lot from the perspective of travelers’ benefits, presents an imbalanced utilization of
the parking lots due to the fact that the nearby parking lots are saturated while the distant
ones are empty. The average walking distance after parking is relatively short, but the
satisfaction proportion of psychological needs is also relatively low. Therefore, parking
recommendation Scheme 1 is more suitable for the situations where the parking demand
and the utilization of parking lots are relatively low.
For the recommended parking Schemes 2, 3, and 4, the changing trends of parking
occupancy rates and the evaluation indicators are roughly the same. All these schemes can
effectively balance the utilization of the parking lot resources. Meanwhile, the smaller the
adjustment coefficient, the faster the speed of reaching the balanced parking utilization.
Since the parking regulation by the parking recommendation system was launched from
the start of the parking simulation, the average walking distance after parking is higher for
the parking reservation group. The satisfaction proportions of the psychological thresholds
of the parking factors were also relatively high. Therefore, Schemes 2, 3, and 4 were more
suitable for situations where the parking demand is high and where the utilization of
parking lots continues to be kept at a high level.
Dynamic parking recommendation Scheme 5 takes the interests of both travelers and
parking managers into account. At the stage of low occupancy for the parking lots, the
scheme can recommend the best parking lot for the maximization of the travelers’ benefits.
When the parking demand increases and the occupancy of the parking lots reaches a high
level, parking regulation is triggered by the parking recommendation system. The parking

Sustainability 2023,15, 6808 15 of 22
recommendation can make the utilization of parking resources more balanced and keep
a cert
ain proportion of available parking spaces for each parking lot, thus reducing cruising
for parking. Based on the comprehensive analysis, the effect of parking recommendation
Scheme 5 is the best, and thus further exploration of its performance under different
conditions is conducted in the following chapters.
5.3. Analysis of Simulation Results in Different Initial Parking Utilization States
In order to analyze the effect of the dynamic parking recommendation Scheme 5 under
different initial parking utilization states, two different travel scenarios are given based
on the settings detailed in Section 5.1. One scenario represents the insufficient parking
resources, whereby the initial number of available parking spaces for Park 1, Park 2, Park
3, Park 4, and Park 5 are set to 20, 30, 40, 50, and 80, respectively. The other scenario
represents sufficient parking resources, whereby the initial number of available parking
spaces for Park 1, Park 2, Park 3, Park 4, and Park 5 are set to 100, 120, 150, 160, and 180,
respectively. The simulation is performed under these two scenarios to evaluate the results
of the operation.
(1)
Dynamic change in parking occupancy in different initial parking utilization.
Compared with the changing trends of parking occupancy rates in Figure 6e, Figure 7a
shows that the utilization of parking lot resources can be balanced quickly by parking
recommendation Scheme 5 when there are a few vacant parking spaces, at first. The
parking occupancy rate of each parking lot is close to an essentially balanced state at
around
130 mi
n. When there are sufficient initial parking spaces, as shown in Figure 7b,
at the initial stage, parking recommendations are made mainly based on travelers’ utility
maximization. Overall, the occupancy rates of the parking lots that are closer to the
destination gradually increase and will be closer to saturation. At this time, the median
of the parking occupancy rates of the parking lots reach the regulation threshold of 70%
at about 135 min. Thus, the parking adjustment is then started from the perspective of
balancing the utilization of parking resources while ensuring the interests of travelers. The
farthest parking lot will then be recommended to travelers. Since implementing this, the
occupancy rates of Park 4 and Park 5 have increased quickly.
Sustainability 2023, 14, x FOR PEER REVIEW 16 of 22
(a)
(b)
Figure 7. The dynamic change in parking occupancy rates for different initial parking utilizations:
(a) insufficient scarce parking spaces and (b) sufficient parking spaces.
(2) Simulation results in different initial parking utilizations.
Table 4 shows that when the initial parking space is sufficient, the satisfaction ratio
of the travelers to the psychological threshold of the three concerned parking factors, the
satisfaction ratio of the psychological threshold of the primary parking concern factors,
and the satisfaction ratio of the secondary parking concern factors are all slightly lower
than those under the condition of insufficient initial parking space. The reason is that
when there are sufficient parking spaces in the parking lot near the destination,
recommendations are made with the goal of maximizing the benefits of travelers. In this
situation, the parking lots that are closer to the destination are first recommended and
then gradually saturated. Furthermore, subsequent travelers can only park their cars in
the distant parking lot. Under the situation of scarce initial parking spaces, a balanced
adjustment will be started at the beginning, which will not cause the over-saturated
occupancy of the parking lots that are closer to the destination but will instead bring a
higher average walking distance after parking.
Table 4. Simulation results in different initial parking utilizations.
Scenario
Indicators Related to Travelers
(All/Parking Reservation Group/Non-Parking Reservation Group)
Indicators Related to
Parking Manager
Satisfaction of Psychological Needs (%) Average Walking
Distance after
Parking
(m/Person)
Average Parking Fee
(Including
Reservation Fee)
(CNY/Person)
Cumulative
Parking Revenue
(CNY)
All Three
Factors
Primary
Concerned
Factors
Secondary
Concerned
Factors
Initial
seings 92/92/91 91/92/87 89/90/88 287/331/227 24/23/24 28,499
Insufficient
parking spaces 93/94/91 91/94/87 90/91/88 300/364/213 24/23/24 28,170
Sufficient
parking spaces 87/90/84 89/91/87 88/89/86 261/276/239 24/25/24 29,034
The average parking fee and the cumulative parking revenue have no significant
difference in the different initial parking utilization states. Compared with the non-
parking reservation group, the satisfaction proportions of the psychological threshold for
the parking factors of the parking reservation group are higher under different initial
parking utilization states, while their average walking distance after parking is also
higher. Therefore, parking recommendation Scheme 5 can be applied to different initial
Figure 7.
The dynamic change in parking occupancy rates for different initial parking utilizations:
(a) insufficient scarce parking spaces and (b) sufficient parking spaces.
(2)
Simulation results in different initial parking utilizations.
Table 4shows that when the initial parking space is sufficient, the satisfaction ratio
of the travelers to the psychological threshold of the three concerned parking factors, the
satisfaction ratio of the psychological threshold of the primary parking concern factors, and
the satisfaction ratio of the secondary parking concern factors are all slightly lower than

Sustainability 2023,15, 6808 16 of 22
those under the condition of insufficient initial parking space. The reason is that when there
are sufficient parking spaces in the parking lot near the destination, recommendations are
made with the goal of maximizing the benefits of travelers. In this situation, the parking
lots that are closer to the destination are first recommended and then gradually saturated.
Furthermore, subsequent travelers can only park their cars in the distant parking lot. Under
the situation of scarce initial parking spaces, a balanced adjustment will be started at the
beginning, which will not cause the over-saturated occupancy of the parking lots that
are closer to the destination but will instead bring a higher average walking distance
after parking.
Table 4. Simulation results in different initial parking utilizations.
Scenario
Indicators Related to Travelers
(All/Parking Reservation Group/Non-Parking Reservation Group)
Indicators Related
to Parking
Manager
Satisfaction of Psychological Needs (%) Average Walking
Distance after
Parking
(m/Person)
Average Parking
Fee (Including
Reservation Fee)
(CNY/Person)
Cumulative
Parking Revenue
(CNY)
All Three
Factors
Primary
Concerned
Factors
Secondary
Concerned
Factors
Initial
settings 92/92/91 91/92/87 89/90/88 287/331/227 24/23/24 28,499
Insufficient
parking
spaces
93/94/91 91/94/87 90/91/88 300/364/213 24/23/24 28,170
Sufficient
parking
spaces
87/90/84 89/91/87 88/89/86 261/276/239 24/25/24 29,034
The average parking fee and the cumulative parking revenue have no significant
difference in the different initial parking utilization states. Compared with the non-parking
reservation group, the satisfaction proportions of the psychological threshold for the park-
ing factors of the parking reservation group are higher under different initial parking
utilization states, while their average walking distance after parking is also higher. There-
fore, parking recommendation Scheme 5 can be applied to different initial parking supply
and demand situations and can play a good role in regulating parking demand.
5.4. Analysis of Simulation Results for Different Parking Reservation Proportions
According to the survey data, the parking reservation proportions are 35%, 5%, and
8% for the decision-making points of before the trip, during the trip, and near the shopping
destination. These reservation proportions of the different decision points are changed
in order to obtain two scenarios while keeping the other conditions unchanged. For
reservation Scenario 1, the reservation proportions for the three sequential decision points
are set to 5%, 35%, and 8%, respectively. For reservation Scenario 2, the reservation
proportions are 8%, 5%, and 35%, respectively.
(1)
Dynamic change in the parking occupancy in different parking reservation proportions.
Under the initial reservation scenario, Figure 6e shows that the median parking
occupancy rates reach the regulation threshold of 70% at around 65 min. At this time,
the parking regulation is triggered by parking recommendation Scheme 5. It can be seen
from Figure 8that, while under reservation, Scenarios 1 and 2 possess higher reservation
proportions during the trip and near the destination; however, the parking regulations
have a late start at around 70 min and 75 min, respectively. At this moment, it is more
difficult for the parking regulation to achieve a balanced utilization of parking lots because
Park 1, Park 2, and Park 3 reach a relatively high and nearly saturated parking occupancy.
When the reservation proportion before the trip is higher, the performance of balancing

Sustainability 2023,15, 6808 17 of 22
the utilization of parking resources in Figure 6e is better than that of reservation Scenarios
1 and 2. Therefore, the earlier the parking reservation is made during the whole travel
process, the earlier the time of starting parking regulation and the easier it is to make the
parking resources reach a balanced utilization.
Sustainability 2023, 14, x FOR PEER REVIEW 17 of 22
parking supply and demand situations and can play a good role in regulating parking
demand.
5.4. Analysis of Simulation Results for Different Parking Reservation Proportions
According to the survey data, the parking reservation proportions are 35%, 5%, and
8% for the decision-making points of before the trip, during the trip, and near the
shopping destination. These reservation proportions of the different decision points are
changed in order to obtain two scenarios while keeping the other conditions unchanged.
For reservation Scenario 1, the reservation proportions for the three sequential decision
points are set to 5%, 35%, and 8%, respectively. For reservation Scenario 2, the reservation
proportions are 8%, 5%, and 35%, respectively.
(1) Dynamic change in the parking occupancy in different parking reservation
proportions.
Under the initial reservation scenario, Figure 6e shows that the median parking
occupancy rates reach the regulation threshold of 70% at around 65 min. At this time, the
parking regulation is triggered by parking recommendation Scheme 5. It can be seen from
Figure 8 that, while under reservation, Scenarios 1 and 2 possess higher reservation
proportions during the trip and near the destination; however, the parking regulations
have a late start at around 70 min and 75 min, respectively. At this moment, it is more
difficult for the parking regulation to achieve a balanced utilization of parking lots because
Park 1, Park 2, and Park 3 reach a relatively high and nearly saturated parking occupancy.
When the reservation proportion before the trip is higher, the performance of balancing
the utilization of parking resources in Figure 6e is beer than that of reservation Scenarios
1 and 2. Therefore, the earlier the parking reservation is made during the whole travel
process, the earlier the time of starting parking regulation and the easier it is to make the
parking resources reach a balanced utilization.
(a)
(b)
Figure 8. The dynamic change in parking occupancy rates for different parking reservation
proportions: (a) reservation Scenario 1; (b) reservation Scenario 2.
(2) Simulation results in different parking reservation proportions.
It can be seen from Table 5 that the satisfaction proportions of travelers’ psychological
threshold for the parking factors for their chosen or reserved parking lots are higher under
different parking reservation proportions, while keeping the whole parking reservations
unchanged. At the initial seing (high proportion of pre travel parking reservations), the
satisfaction proportions of the travelers’ psychological threshold for parking factors are
slightly higher. Meanwhile, the average walking distance after parking is the shortest, at
around 287 m/person for all travelers, 331 m/person for the parking reservation group,
and 227 m/person for the non-parking reservation group, which was found to be lower
Figure 8.
The dynamic change in parking occupancy rates for different parking reservation propor-
tions: (a) reservation Scenario 1; (b) reservation Scenario 2.
(2)
Simulation results in different parking reservation proportions.
It can be seen from Table 5that the satisfaction proportions of travelers’ psychological
threshold for the parking factors for their chosen or reserved parking lots are higher under
different parking reservation proportions, while keeping the whole parking reservations
unchanged. At the initial setting (high proportion of pre travel parking reservations), the
satisfaction proportions of the travelers’ psychological threshold for parking factors are
slightly higher. Meanwhile, the average walking distance after parking is the shortest, at
around 287 m/person for all travelers, 331 m/person for the parking reservation group,
and 227 m/person for the non-parking reservation group, which was found to be lower
than that of the reservation Scenarios 1 and 2. In addition, the cumulative parking revenue
for managers was also found to be higher. There was no significant difference found in the
average parking fee under different parking reservation scenarios.
Table 5. Simulation results in different parking reservation proportions.
Scenario
Indicators Related to Travelers
(All/Parking Reservation Group/Non-Parking Reservation Group)
Indicators Related
to Parking
Manager
Satisfaction of Psychological Needs (%) Average Walking
Distance after
Parking
(m/Person)
Average Parking
Fee (Including
Reservation Fee)
(CNY/Person)
Cumulative
Parking Revenue
(CNY)
All Three
Factors
Primary
Concerned
Factors
Secondary
Concerned
Factors
Initial
settings 92/92/91 91/92/87 89/90/88 287/331/227 24/23/24 28,499
Reservation
Scenario 1 90/92/87 90/93/87 88/90/88 296/342/235 24/23/24 27,880
Reservation
Scenario 2 89/92/85 89/93/85 88/90/86 292/340/231 24/23/24 27,513
In general, the parking recommendation Scheme 5 can achieve the effect of balancing
the utilization of parking resources according to parking demand and thus can present
a high satisfaction proportion of travelers’ psychological thresholds for parking factors

Sustainability 2023,15, 6808 18 of 22
under different parking reservation proportions at different stages of the travel process.
Therefore, making parking reservations earlier, the system regulates the start time ear-
lier, and the balance of parking lot resources is better. Meanwhile, the average walking
distance after parking was lower and the parking revenue was higher. Therefore, pro-
viding sufficient parking information to promote travelers’ parking reservations before
the trip can effectively reduce the cruising for parking and thus enhance the utilization of
parking resources.
5.5. Analysis of Simulation Results in Different Parking Regulation Thresholds
In order to analyze the impact of regulation thresholds on the performance of parking
utilization, two changed thresholds of 50% and 90% were given based on the initial setting
of 70%.
(1)
Dynamic change in parking occupancy in different parking regulation thresholds.
Compared with the performance under the parking regulation threshold of 70% in
Figure 6e, Figure 9a shows that the parking regulation is triggered in order to balance
the resource utilization at the start due to the higher occupancy rates of the parking lots
under the lower regulation threshold of 50%. Additionally, the occupancy rates in parking
lots reach a basically balanced state at around 210 min. When the parking regulation
threshold is set to 90%, as shown in Figure 9b, the time to start the parking regulations
is delayed to 90 min, compared with the initial setting of 70%. Due to the high threshold
that is required to start parking regulations, certain parking lots are thus already nearly
saturated, and the parking regulation is thus frequently started at different periods of time.
When the median parking occupancy rates of the parking lots are less than 90% again, the
parking recommendation will be made to maximize the benefits of travelers, which leads to
an increase in the occupancy of car parks close to peoples destinations. Eventually, it
is difficult for the adjustment to make parking resource utilizations reach the overall
equilibrium state.
Sustainability 2023, 14, x FOR PEER REVIEW 19 of 22
(a) (b)
Figure 9. The dynamic change in parking occupancy rates for different parking regulation
thresholds: (a) parking regulation threshold of 50% and (b) parking regulation threshold of 90%.
(2) Simulation results in different parking regulation thresholds.
Table 6 shows that when the threshold to start parking regulation changes from 50%
to 90%, then the satisfaction proportions of travelers’ psychological threshold for parking
factors gradually decrease. Meanwhile, the average walking distance after parking for all
travelers and the parking reservation group also decreases; moreover, the cumulative
parking revenue thus increases. Meanwhile, the satisfaction proportions of the travelers’
psychological threshold for parking factors for the parking reservation group were higher
than those for the non-parking reservation group under different parking regulation
thresholds. However, the average walking distance for the parking reservation group was
found to be relatively longer.
Table 6. Simulation results in different parking regulation thresholds.
Threshold
to Start
Parking
Regulation
Indicators Related to Travelers
(All/Parking Reservation Group/Non-Parking Reservation Group)
Indicators Related to
Parking Manager
Satisfaction of Psychological Needs (%) Average Walking
Distance after
Parking
(m/Person)
Average Parking
Fee (Including
Reservation Fee)
(CNY/Person)
Cumulative
Parking Revenue
(CNY)
All Three
Factors
Primary
Concerned
Factors
Secondary
Concerned
Factors
Initial
seing of
70%
92/92/91 91/92/87 89/90/88 287/331/227 24/23/24 28,499
50% 97/98/96 92/95/89 91/92/89 297/362/208 24/22/25 28
,
239
90% 85/88/81 88/91/84 87/89/84 276/307/235 24/24/24 28,679
Overall, the higher the regulation threshold is, the later the time to start the parking
regulation and the more difficult it is to achieve a balanced utilization of parking lots.
When the threshold to start parking regulation is set at 70%, the satisfaction proportions
of the travelers’ psychological threshold for parking factors and the cumulative parking
revenue are higher. In addition, the average walking distance after parking is also
relatively shorter. Therefore, the threshold to start parking regulation should be set
neither too high nor too low, both from the perspective of improving travelers’ travel
experiences and also the utilization of parking resources.
Figure 9.
The dynamic change in parking occupancy rates for different parking regulation thresholds:
(a) parking regulation threshold of 50% and (b) parking regulation threshold of 90%.
(2)
Simulation results in different parking regulation thresholds.
Table 6shows that when the threshold to start parking regulation changes from 50%
to 90%, then the satisfaction proportions of travelers’ psychological threshold for parking
factors gradually decrease. Meanwhile, the average walking distance after parking for
all travelers and the parking reservation group also decreases; moreover, the cumulative
parking revenue thus increases. Meanwhile, the satisfaction proportions of the travelers’
psychological threshold for parking factors for the parking reservation group were higher

Sustainability 2023,15, 6808 19 of 22
than those for the non-parking reservation group under different parking regulation thresh-
olds. However, the average walking distance for the parking reservation group was found
to be relatively longer.
Table 6. Simulation results in different parking regulation thresholds.
Threshold to
Start Parking
Regulation
Indicators Related to Travelers
(All/Parking Reservation Group/Non-Parking Reservation Group)
Indicators Related
to Parking
Manager
Satisfaction of Psychological Needs (%) Average Walking
Distance after Parking
(m/Person)
Average Parking
Fee (Including
Reservation Fee)
(CNY/Person)
Cumulative
Parking Revenue
(CNY)
All Three
Factors
Primary
Concerned
Factors
Secondary
Concerned
Factors
Initial setting of
70% 92/92/91 91/92/87 89/90/88 287/331/227 24/23/24 28,499
50% 97/98/96 92/95/89 91/92/89 297/362/208 24/22/25 28,239
90% 85/88/81 88/91/84 87/89/84 276/307/235 24/24/24 28,679
Overall, the higher the regulation threshold is, the later the time to start the parking
regulation and the more difficult it is to achieve a balanced utilization of parking lots. When
the threshold to start parking regulation is set at 70%, the satisfaction proportions of the
travelers’ psychological threshold for parking factors and the cumulative parking revenue
are higher. In addition, the average walking distance after parking is also relatively shorter.
Therefore, the threshold to start parking regulation should be set neither too high nor too
low, both from the perspective of improving travelers’ travel experiences and also the
utilization of parking resources.
6. Conclusions
Intelligent parking services can provide travelers with real-time parking information,
as well as help reduce cruising time and balance the utilization of parking resources by
using parking recommendations and reservations. It is an effective method for solving
the parking problems in big cities. Based on a parking survey, this research analyzed
sequential parking decision behaviors at different stages of the travel process. Then,
the parking recommendation model and the individual parking decision model, which
considered travelers’ psychological characteristics, were established in order to analyze the
applicability of different parking recommendation schemes. Finally, the optimal dynamic
parking recommendation scheme is selected and its applicability in different situations is
explored. The conclusions are as follows:
Based on the survey of sequential parking decision behavior, it was found that travelers
pay more attention to walking distance after parking, driving time, and parking price when
making their parking choice. Travelers’ acceptable psychological thresholds for parking
factors with respect to their parking lot choice were found to be, in the main, less than
800 m for walking distance after parking, less than 15 CNY/h for parking price, and more
than two for available parking spaces. In general, travelers were more willing to accept
and use the parking recommendation system. The proportion of parking reservations
was 56%, with the highest value of 39% at the stage before the trip, followed by near the
destination, and then during the trip. Most travelers, accounting for 48%, reserve the
parking lot recommended by the recommendation system.
The parking recommendation model and the individual parking decision model, con-
sidering the travelers’ psychological thresholds and attention to parking factors—which
were received in real time—were established. The static and dynamic parking recom-
mendation schemes, based on a changing adjustment coefficient, were designed. The
simulation results show that the satisfaction proportion of the psychological threshold for
parking factors for the parking reservation group was found to be, under different parking

Sustainability 2023,15, 6808 20 of 22
recommendation schemes, higher than that for the non-parking reservation group. Using
the parking recommendation system, individual parking experiences and satisfaction can
be improved, although the system may slightly increase the walking distance after parking.
The static parking recommendation Scheme 1 presents the phenomenon of an unbal-
anced utilization of parking spaces for nearby and distant parking lots. Since it recommends
the parking lot from the perspective of maximizing travelers’ benefits, the average walking
distance after parking was relatively short, but the satisfaction proportions of psychological
needs for the parking factors were relatively low. Scheme 1 is more suitable for situations
where the utilization of parking lots is relatively low. The recommended parking Schemes 2,
3, and 4 have similar, better operational performances in terms of balancing the utilization
of parking resources. The satisfaction proportions of the psychological needs for parking
factors are relatively high, while the average walking distance after parking is also higher.
These schemes are more suitable for situations where the utilization of parking lots is kept
at a high level. The dynamic parking recommendation Scheme 5 can take the interests
of both travelers and parking managers into account. At a stage with a low occupancy
rate for parking lots, this scheme can recommend the parking lot from the perspective of
maximizing travelers’ benefits. When the occupancy of the parking lots is high enough,
parking regulations will be triggered to make parking resource utilization more balanced.
Therefore, parking recommendation Scheme 5 is better.
Based on the simulation of parking recommendation Scheme 5 under different condi-
tions, it is concluded that dynamic parking recommendation Scheme 5 can quickly balance
the utilization of parking resources in the case of a shortage of parking spaces. When the
initial parking space is sufficient, parking recommendations can be carried out, at first,
based on the benefits to travelers. Then, the parking regulation can start when the parking
occupancy reaches a high level. Parking recommendation Scheme 5 can be applied to dif-
ferent parking utilization conditions and plays a good role in regulating parking demand
distribution. Making parking reservations before the trip has a better effect in terms of
balancing the utilization of parking resources. Meanwhile, the average walking distance
after parking is lower and the parking revenue is higher. Therefore, providing sufficient
parking information to promote travelers’ parking reservations before the trip is beneficial
for reducing cruising for parking and in terms of optimizing parking resource utilization.
For the dynamic parking recommendation, the threshold to start parking regulation should
be set neither too high nor too low from the perspectives of increasing travelers’ parking
experience and in balancing the utilization of parking resources.
This research explored parking recommendation methods considering travelers’ psy-
chological characteristics. This article also has some limitations. For example, only shop-
ping travel scenarios are discussed. In the future, we should explore the applicability
of more parking and travel scenarios, such as office or work trips. Another limitation is
that the simulations in this paper assume that people’s parking choices are the same as
those obtained from the questionnaire. We should investigate and obtain more parking
reservation data under travel scenarios. The survey data can be enriched to improve the
decision models for parking choice and reservation for different groups during the travel
process; in other words, it will be better to verify the effects of implementing the parking
recommendation models and schemes through practical examples.
Author Contributions:
Methodology, data curation, and writing original draft, N.X.; conceptualiza-
tion, funding acquisition, and writing—review and editing, H.Q.; supervision, Y.Z., Q.P. and Z.L. All
authors have read and agreed to the published version of the manuscript.
Funding:
This research was supported by the Beijing Natural Science Foundation (8212002) and the
National Natural Science Foundation (71971005).
Institutional Review Board Statement:
Ethical review and approval were waived for this study, as
no personal identity was involved or reported.
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the study.

Sustainability 2023,15, 6808 21 of 22
Data Availability Statement:
The data used to support the findings of this study are available from
the corresponding authors upon request.
Acknowledgments:
The authors would like to thank anonymous reviewers for their valuable com-
ments and suggestions.
Conflicts of Interest:
The authors declare that there are no conflicts of interest regarding the publica-
tion of this paper.
References
1. Shoup, D.C. Cruising for parking. Transp. Policy 2006,13, 479–486. [CrossRef]
2.
Zhu, Y.; Ye, X.; Chen, J.; Yan, X.; Wang, T. Impact of Cruising for Parking on Travel Time of Traffic Flow. Sustainability
2020
,12, 3079.
[CrossRef]
3.
Giuffrè, T.; Siniscalchi, S.; Tesoriere, G. A novel architecture of parking management for smart cities. Procedia–Soc. Behav.
2012
,53,
16–28. [CrossRef]
4.
Caliskan, M.; Barthels, A.; Scheuermann, B.; Mauve, M. Predicting Parking Lot Occupancy in Vehicular Ad Hoc Networks. In
Proceedings of the IEEE 65th Conference on Vehicular Technology, Dublin, Ireland, 22–25 April 2007.
5. Paul, B. On-Street Parking Management; Deutsche Gesellschaft Für International Zusammenarbeit: Berlin, Germany, 2016.
6.
Arnott, R.; Inci, E. An integrated model of downtown parking and traffic congestion. J. Urban Econ.
2005
,60, 418–442. [CrossRef]
7.
Van der Waerden, P.; Timmermans, H.; da Silva, A.N.R. The influence of personal and trip characteristics on habitual parking
behavior. Case Stud. Transp. Policy 2015,3, 33–36. [CrossRef]
8. Zhang, R.; Zhu, L. Curbside parking pricing in a city centre using a threshold. Transp. Policy 2016,52, 16–27. [CrossRef]
9.
Khaliq, A.; van der Waerden, P.; Janssens, D.; Wets, G. A conceptual framework for forecasting car driver’s on-street parking
decisions. Transp. Res. Procedia 2019,37, 131–138. [CrossRef]
10.
Soto, J.J.; Márquez, L.; Macea, L.F. Accounting for attitudes on parking choice: An integrated choice and latent variable approach.
Transp. Res. Part A Policy Pract. 2018,111, 65–77. [CrossRef]
11.
Tian, Q.; Yang, L.; Wang, C.L.; Huang, H.-J. Dynamic pricing for reservation-based parking system: A revenue management
method. Transp. Policy 2018,71, 36–44. [CrossRef]
12.
Lei, C.; Ouyang, Y.F. Dynamic pricing and reservation for intelligent urban parking management. Transp. Res. Part C Emerg.
Technol. 2017,77, 226–244. [CrossRef]
13.
Fahim, A.; Hasan, M.; Chowdhury, M.A. Smart parking systems: Comprehensive review based on various aspects. Heliyon
2021
,
7, e07050. [CrossRef] [PubMed]
14.
Cao, J.; Menendez, M. Quantification of potential cruising time savings through intelligent parking services. Transp. Res. Part A
Policy Pract. 2018,116, 151–165. [CrossRef]
15.
¸Sengör, ˙
I.; Güner, S.; Erdinç, O. Real-time algorithm based intelligent EV parking lot charging management strategy providing
PLL type demand response program. IEEE Trans. Sustain. Energy 2020,12, 1256–1264. [CrossRef]
16.
Dogaroglu, B.; Caliskanelli, S.P. Investigation of car park preference by intelligent system guidance. Res. Transp. Bus. Manag.
2020
,
37, 100567. [CrossRef]
17.
Dogaroglu, B.; Caliskanelli, S.P.; Tanyel, S. Comparison of intelligent parking guidance system and conventional system with
regard to capacity utilization. Sustain. Cities Soc. 2021,74, 103152. [CrossRef]
18.
Khaliq, A.A.; Anjum, A.; Ajmal, A.B.; Webber, J.L.; Mehbodniya, A.; Khan, S. A secure and privacy preserved parking recom-
mender system using elliptic curve cryptography and local differential privacy. IEEE Access 2022,10, 56410–56426. [CrossRef]
19.
Huang, Y.H.; Hsieh, C.H. A decision support system for available parking slots on the roadsides in urban areas. Expert Syst. Appl.
2022,205, 117668. [CrossRef]
20.
Li, J.; Qu, H.; You, L. An Integrated Approach for the Near Real-Time Parking Occupancy Prediction. IEEE Trans. Intell. Transp.
Syst. 2022,24, 3769–3778. [CrossRef]
21.
Fu, J.; Chen, Z.; Sun, R. Research on intelligent terminal oriented optimal parking space recommendation model. In Proceedings
of the 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), Qingdao, China, 8–11 October 2014;
pp. 2373–2378.
22.
Shin, J.H.; Jun, H.B. A study on smart parking guidance algorithm. Transp. Res. Part C Emerg. Technol.
2014
,44, 299–317.
[CrossRef]
23.
Safi, Q.G.K.; Luo, S.L.; Pan, L.M.; Liu, W.; Hussain, R.; Bouk, S.H. SVPS: Cloud-based smart vehicle parking system over
ubiquitous VANETs. Comput. Netw. 2018,138, 18–30. [CrossRef]
24.
Shin, J.H.; Jun, H.B.; Kim, J.G. Dynamic control of intelligent parking guidance using neural network predictive control. Comput.
Ind. Eng. 2018,120, 15–30. [CrossRef]
25.
Zhao, P.; Wei, L.; Pan, D.; Dong, S. Multicriteria Model for Shared Parking and Parking Route Recommender Systems. J. Adv.
Transp. 2022,2022, 8369362. [CrossRef]
26.
Sadreddini, Z.; Guner, S.; Erdinc, O. Design of a decision-based multicriteria reservation system for the EV parking lot. IEEE
Trans. Transp. Electrif. 2021,7, 2429–2438. [CrossRef]

Sustainability 2023,15, 6808 22 of 22
27.
Mei, Z.Y.; Zhang, W.; Zhang, L.H.; Wang, D. Optimization of reservation parking space configurations in city centers through
an agent-based simulation. Simul. Model. Pract. Theory 2020,99, 102020. [CrossRef]
28.
Wang, J.; Zhu, C.; Miao, C.; Zhu, R.; Zhang, X.; Tang, Y.; Huang, H.; Gao, C. BPR: Blockchain-Enabled Efficient and Secure Parking
Reservation Framework with Block Size Dynamic Adjustment Method. IEEE Trans. Intell. Transp. Syst.
2022
,24, 3555–3570.
[CrossRef]
29.
Liu, K.S.; Gao, J.; Wu, X.; Lin, S. On-street parking guidance with real-time sensing data for smart cities. In Proceedings of the
2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), Hong Kong, China,
11–13 June 2018; pp. 1–9.
30.
He, H.; Zhang, Z.; Yan, P. A real-time reservation service for smart parking system. In Proceedings of the 2018 15th international
conference on service systems and service management (ICSSSM), Hangzhou, China, 21–22 July 2018; pp. 1–6.
31.
Fu, J.; Chen, Z.; Sun, R.; Yang, B. Reservation based optimal parking lot recommendation model in Internet of Vehicle environment.
China Commun. 2014,11, 38–48. [CrossRef]
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