Parking Decisions Research Articles

Automated vehicles and the urban parking paradigm: Environmental implications and Citizen preference

This study examines parking decisions in a future populated by automated vehicles (AVs), focusing on their energy implications. Using a multinomial logit model, preferences among cruising, garage parking, sending the AV home, and on-street parking in Santander, Spain, were evaluated. Home sending emerged as the favourite (52%) for its convenience, while garage parking was chosen by 36%, valued for security and environmental benefits. Cruising was least popular (6%), deterred by environmental concerns. Integrating survey data with traffic simulations, the research uncovered that cruising, despite its low preference, accounted for 16% of energy consumption due to empty trips. Surprisingly, sending AVs home, despite being the most popular, led to significant energy use, although it maintained a good consumption ratio. Garage parking, especially when located on city outskirts, was also inefficient. The study highlights the urgent need for strategies to mitigate inefficient parking behaviours, thereby enhancing the sustainability of AV-driven mobility.

Parking preferences of tourists in Sun Moon Lake scenic area

Sun Moon Lake is a famous tourist attraction in Taiwan and around the world. However, as Sun Moon Lake is surrounded by mountains and has limited land to develop, traffic congestion around the lake area is commonplace during peak holiday hours. This study focuses on the parking choices of visitors to Sun Moon Lake and develops a stated preference (SP) instrument with multiple scenarios to evaluate parking preferences under various financial and service level scenarios. We estimate Multinomial Logit (MNL) and Mixed Logit (ML) models, accounting for the panel nature of the data (PDML)to identify preferences for parking choices of visitors to Sun Moon Lake. The focus is on understanding how parking price, travel time, walking time, scenery, and transfers between public transport affect visitors’ parking choices. Unlike the findings of studies in metropolitan areas, which often find that parking price was the deciding factor, visitors’ parking decisions in the tourist area were more concerned with time factors, such as the time to search for places to park and traffic congestion, possibly due to the less frequent use of tourist venues. Although raising parking price can suppress parking demand in the scenic area, other parking management mechanisms work better, such as the construction of new and suitable outer parking lots with transfer buses to relieve the heavily congested traffic in the scenic area. In addition, we find that using the parking space in the area can be improved by beautifying the landscaping between the parking lots and the tourist spots, enhancing the pleasure of traveling along the routes, introducing multiple transfer modes, and providing real-time traffic information to tourists.

A reinforcement learning framework for improving parking decisions in last-mile delivery

This study leverages simulation-optimisation with a Reinforcement Learning (RL) model to analyse the routing behaviour of delivery vehicles (DVs). We conceptualise the system as a stochastic k-armed bandit problem, representing a sequential interaction between a learner (the DV) and its surrounding environment. Each DV is assigned a random number of customers and an initial delivery route. If a loading zone is unavailable, the RL model is used to select a delivery strategy, thereby modifying its route accordingly. The penalty is gauged by the additional trucking and walking time incurred compared to the originally planned route. Our methodology is tested on a simulated network featuring realistic traffic conditions and a fleet of DVs employing four distinct lastmile delivery strategies. The results of our numerical experiments underscore the advantages of providing DVs with an RL-based decision support system for en-route decision-making, yielding benefits to the overall efficiency of the transport network. Highlights Combining simulation and optimisation algorithms with reinforcement learning Model DVs en-route parking decisions with a k-armed bandit algorithm Evaluating the impacts of delivery strategies on traffic congestion and in last-mile delivery efficiency

Modelling autonomous vehicle parking: An agent‐based simulation approach

AbstractAutonomous vehicles (AVs) present a paradigm shift in addressing conventional parking challenges. Unlike human‐driven vehicles, AVs can strategically park or cruise until summoned by users. Utilizing utility theory, the parking decision‐making processes of AVs users are explored, taking into account constraints related to both cost and time. An agent‐based simulation approach is adopted to construct an AV parking model, reflecting the complex dynamics of the parking decision process in the real world, where each user's choice has a ripple effect on traffic conditions, consequently affecting the feasible options for other users. The simulation experiments indicate that 11.50% of AVs gravitate towards parking lots near their destinations, while over 50% of AVs avoid public parking amenities altogether. This trend towards minimizing individual parking costs prompts AVs to undertake extended empty cruising, resulting in a significant increase of 48.18% in total vehicle mileage. Moreover, the pricing structure across various parking facilities and management dictates the parking preferences of AVs, establishing a nuanced trade‐off between parking expenses and proximity for these vehicles.

Modelling parking behaviour of commercial vehicles: a scoping review

ABSTRACT Parking in dense urban areas is a major challenge for last mile logistics. Parking shortage and policies that do not address commercial vehicles’ needs often lead these vehicles to park illegally. This paper conducts a scoping literature review on the parking behaviours of commercial freight and service vehicles, methods used to model these behaviours, and factors that determine their outcomes. Thirty-four studies are included in the review. It is found that commercial vehicles’ parking behaviours mainly comprise parking location and type choices including illegal parking, parking duration, and parking cruising. Methods used to model these behaviours primarily include discrete-choice modelling, regression analysis, survival analysis and simulation. We identify key knowledge gaps and provide insights on research opportunities in modelling more complex parking decisions, investigating parking cruising of commercial vehicles, evaluating the implications of freight demand management, and developing data fusion techniques.

Parking Survey Design Using Gamification

We present a survey methodology to collect parking behavior data. The survey is a game that exposes respondents to hypothetical parking scenarios including parking information and the possibility of illegal parking. We use incentive mechanisms from the gamification literature. The survey overcomes choice set deficiencies of conventional revealed and stated preference surveys (i.e., large or unavailable choice sets). We record detailed travel and parking decisions and use the data to develop choice models. The Mixed logit model demonstrates significance of parking preference heterogeneity. Respondents are more likely to park legally when provided full information about parking spot status.

Mathematical Modeling of a Fuzzy Logic-Based Car Parking System

This paper presents the mathematical modeling of a fuzzy logic-based car parking system. Many people are facing problems parking vehicles in parking lots in most urban cities. The technology of fuzzy-based systems is necessary for all vehicle users to acquire parking slots in cities. A smart parking system helps in obtaining information about available parking spaces, organizing and processing it, and then placing the car in a certain position. The method adopted makes use of a fuzzy-based smart car parking system to develop the usage of small parking spaces for parking lots to allow the parking of vehicles based on their weight. A set of rules was used in the fuzzy logic to control two sets of parking decisions, namely the vehicle entry decision and the inpark decision, by bearing in mind the weight of vehicles and the availability of parking spaces in the parking mall. The results obtained show that the smart car parking system keeps the availability of parking lots in equilibrium, depending on the entry and exit of vehicles. In conclusion, it was discovered that the total number of vehicles granted entry at each time interval depends on the availability of parking lots on each designated floor.

Autonomous valet parking optimization with two-step reservation and pricing strategy

With the development of autonomous vehicle, the autonomous valet parking has been attracting extensive attention, by relieving users’ inconvenience from parking. Compared with shortrange autonomous valet parking, long-range autonomous valet parking extends parking features with bridge of long-range travelling and parking in a user-friendly cycle. However, due to the uncertainty of trip routes, the performance of single reservation mechanism on parking lot status prediction is limited. Besides, unified parking pricing intensifies parking competition and causes local congestion. Therefore, we propose a long-range autonomous valet parking framework based on Two-step Reservation Mechanism. The proposed scheme provides the first reservation service before arriving at Drop-off spots (regular step), so as to anticipate parking demand of parking lot. Then, it provides the second reservation service when arriving at Drop-off spots (extra step) for updating the optimality of parking decision. The future parking lot status can be predicted with the impact of concurrent requests and reservation execution probability. Meanwhile, we propose a heterogeneous pricing strategy, in which the parking lots with higher parking demands would be adjusted with higher prices. Simulation results show that our scheme outperforms literature works on reducing parking cost and balancing parking demand.

Optimization of Shared Autonomous Vehicles Routing Problem: From the View of Parking

Shared autonomous vehicles (SAVs) have great potential for achieving beneficial changes to the society. Although recent studies have explored the traffic safety, economic benefits and environmental impact, parking decisions of SAVs is rarely considered. SAVs need to park to avoid cruising during the interval between services. Appropriate parking decisions can contribute to reduce the vehicle kilometers traveled (VKT). This study synergistically considers routing and parking of SAVs for system optimization. Since the problem is NP-hard, we develop a variable neighborhood search (VNS) heuristic to solve it. The heuristic aims to minimize the VKT, the number of SAVs and the parking cost by systematic changes of neighborhood. A series of experiments based on the Anaheim network prove the high solving efficiency and quality of the heuristic. Results also indicate that the marginal cost of the system decreases with the increase in travel demand and the VKT increases with the increase in parking fees.

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

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.

Influence of Traffic Characteristics on Pavement Performance of Parking Lots

This research aims to identify and interpret the main traffic characteristics affecting the pavement condition index (PCI) of asphalt within parking areas. The study also examines the effect of severity and type of asphalt pavement distress on parking decisions. Previous literature examines the impact of the environment on asphalt pavement deterioration. This paper will focus on the shape of traffic operations within a parking area. The methodology includes applying a multilevel mixed regression model to reveal the hidden hierarchical relationship between traffic activities and the different types of pavement distresses and then discovering their effect on asphalt PCI. The resulting models showed that parking lots are aging faster than the common traveling roads due to the increased impact of axial loads on their surfaces at low and static speeds and some distinctive traffic characteristics. Moreover, some types of pavement distress, such as alligator cracks, potholes and depressions, appear more widely than others on the parking lot surface and significantly influence the parking decision. The conclusion of the research may significantly impact pavement design and maintenance management for parking lots.

A model of household shared parking decisions incorporating equity-seeking household dynamics and leadership personality traits

A model of household shared parking decisions incorporating equity-seeking household dynamics and leadership personality traits

New Parking Lot Selection Approach Based on the Multi-Criteria Decision Making (MCDM) Methods: Health Criteria

To find a parking space, valet parking drivers have to travel a lot, which leads to carbon dioxide (CO2) emissions. In order to reduce these emissions, it is essential to understand a user’s needs and criteria when searching for a parking space. Several selection criteria are considered when allocating a parking space. Recent research on parking space management mentions several parameters that have an impact on the choice of a parking space: namely, the traffic situation, the availability of each parking lot in question, and the cost of parking, etc. In this article, we discuss a new criterion: the physical condition of the driver in the management of parking spaces; the identification of the driver’s bodily fragility. We also propose MCDM as a parking space allocation model that best meets the cost–benefit convention. This reflection leads us to evaluate MCDM methods in the field of intelligent parking management. Therefore, we conducted a comparison between the most recent multi-criteria decision making methods used by researchers, namely, CODA, EDAS, TOPSIS, and WASPAS. The CRITIC method was used in this paper to objectively determine the weight of each criterion. A new approach is proposed to evaluate and select the best MCDM method. Indeed, we propose a method that computes the “average inter-item correlation SW”, a combination of the “average inter-item correlation” and the SW coefficient. This approach allows us to efficiently compute the correlation between a method and the set of methods while favoring the cells with the best ranking. A case study is presented to illustrate the MCDM approach to parking space allocation and evaluation. The proposed system provides drivers with services such as intelligent parking decisions, taking into account the human aspect while reducing energy consumption, driving time, and traffic congestion caused by searching for available parking spaces.

Shared Parking Decision Behavior of Parking Space Owners and Car Travelers Based on Prospect Theory—A Case Study of Nanchang City, China

Shared parking improves the utilization rate of parking spaces by taking advantage of temporal and spatial differences, which is conducive to alleviating parking problems. From the perspective of bounded rationality, this paper studies the factors that influence the decision behavior of parking space owners and car travelers (non-residential drivers who have parking needs near residential areas) in sharing parking spaces. Prospect theory was used to analyze the bounded rational behavior characteristics of parking space owners and car travelers, and a value function model with rental price as the reference point was established. Combined with the survey data of the Xinhuangcheng district in Nanchang City, China, the shared parking space rental price that satisfied both parties was analyzed in this case study. The results of the study show that factors such as personal characteristics and behavioral habits affect the decision behavior of parking space owners and car travelers, and that rental price is a key factor. When the rental price of parking spaces is close to the maximum price desired by the owner, the owner feels the benefit and is willing to share the private parking space, but when the rental price differs greatly from the maximum price desired by the owner, the owner feels the loss and is not willing to share the parking space. From the survey data, it can be concluded that the ideal rental price of shared parking spaces around the survey area is 5 CNY/h. This paper provides a theoretical basis and guidance for the formulation of shared parking policies, which can help solve parking problems.

Aiming Sustainable Transportation Utilizing Social Capital: A Perspective of Mobile Car Park App

The mobile car parking (MCP) app provides users to locate a car park and park his/her car, which is expected to contribute to sustainable transportation. Previous mobile phone-based studies have also advocated that mobile apps create a social network among the users. However, MCP studies have yet not addressed the notion of such social capital which is expected to contribute to the sustainable transportation in a city. MCP, in fact, can also create a communication network among car owners, drivers, and car park owners and offer a unique base for studying social capital and resource utilization opportunities. To reduce the gap in existing studies, in this study, an android-based MCP app was developed as a tool to study the components of social capital. After developing the MCP app, two focus group studies were conducted to explore the components an MCP app can provide. Lastly, we linked the social capital components to the sustainable transportation goals. This research pinpointed several social capital components that are addressable to sustainable transportation, including information exchange, communication, connectedness, time-saving capabilities, mobility, coordination, plan-based journeys, and an opportunity to generate income from the social network. Moreover, very few studies have reported that social capital empirically contributes to time-saving capabilities, mobility, sustainable transportation through improved coordination in car parking management, and improved journey based on car parking decisions which are new contributions to social capital. This research is significant, as most of the mobile-based social capital research concentrated on bonding, bridging, and networking among social network members. However, this research has expanded the components of social capital in many directions. Furthermore, from the research methodological perspective, this research adopts a new approach through a combination of experimental research and focus group study.

Privacy-Aware Fuzzy Skyline Parking Recommendation Using Edge Traffic Facilities

Drivers have always been confronted with real-time parking difficulties when driving on urban roads, especially in crowded downtown or beauty spots. On the other hand, privacy leakage risks on users’ private parking preferences and the sensitive data of parking lots have triggered increasing worries. Some literatures endeavor to improve parking service qualities through multi-consideration parking decision optimization on edge sides or cloud computing based on outsourced data storage. And some other literatures propose a number of privacy-preserving methods, such as cryptography and authentication, but these privacy strategies are at the expense of other qualities of parking services, especially the real-time performance. In this paper, we propose a fuzzy skyline parking recommendation scheme for real-time parking recommendation based on roadside traffic facilities. Linguistic parking information instead of raw parking-related data is used in fuzzy skyline fusion. We evaluated our solution with real-world data sets collected from parking facilities in Wulin downtown, Hangzhou city, China. The evaluation results show that our approaches achieve an average accuracy of parking recommendation over 91%, low communication cost, and quick response time with privacy protection.

Joint Modeling of Access Mode and Parking Choice of Air Travelers Using Revealed Preference Data

Airport ground access mode choice is distinct from everyday mode choice decisions, necessitating context-specific choice model estimation. Understanding airport ground access mode choice decisions is not only important for developing infrastructure planning strategies, but also for assessing the impacts of emerging modes on airport revenues, particularly from parking. However, parking choice is an often-overlooked dimension in airport ground access choice modeling. This paper addresses this gap through the development of a joint model of airport access mode and parking option choice using a passenger survey conducted at Dallas-Fort Worth (DFW) International Airport in 2015. Compared with a traditional conditional logit model that does not consider parking options available at DFW airport, the joint model of mode and parking decisions was found to generate more realistic values of travel time and was shown to have better predictive performance, both of which are critical for obtaining better airport parking revenue estimates and identifying traveler cohorts who may respond more strongly to potential policies targeting curb congestion and parking demand.

Managing campus parking demand through course scheduling – an approach to campus sustainability

Purpose The purpose of this study is to use the optimization modeling method to explore whether there is an ideal arrangement of course enrollments that can yield optimal parking demand and supply on college campuses. Design/methodology/approach Using the University of Louisville as a case study, this study deploys a three-step analytical process to examine the correlation between parking demand and course enrollment, estimate parking demand based on course enrollment with regression analyses and embed this estimated relationship in an optimization model that minimizes on-campus parking demand and supply. Findings The correlation analyses suggest significant correlations between course enrollments and on-campus parking. The correlation patterns are different between students and university employees. The optimization results indicate that coupling parking supply and course scheduling decisions can reduce parking supply by 30%. Originality/value Voluminous studies on sustainable campus transportation have focused on transportation demand management strategies. The relationship between course-scheduling and parking demand was not explicitly accounted for in most studies. This study's results reveal that parking demand on campus depends on the number of courses offered across time. Thus, factoring and optimizing course schedules in campus parking decisions remains a viable and essential option to reduce on-campus parking demand.

Smartphone-based parking guidance algorithm and implementation

To alleviate parking problem and offer convenient and efficient service for drivers in parking selection, a smartphone-based parking guidance algorithm and system implementation architecture are proposed. The algorithm describes parking selection as multi-criteria decision making. Dijkstra algorithm is used to suggest the shortest path for the candidate parking lots. Travel cost criteria for the shortest path and parking cost criteria for the candidate parking lots are integrated to constitute a multi-criteria decision set. Analytic Hierarchy Process (AHP) method is explored to decide criteria weights and further make decision about the most appropriate parking lot for drivers. This work also gives the smartphone-based parking guidance system architecture to implement the proposed algorithm. The system enables drivers to inquire the information such as the optimal parking lot recommended by the proposed algorithm and driving route to the suggested parking lot on smartphone. To illustrate the implementation process of the proposed algorithm, an example is presented. The proposed algorithm and system not only provide drivers with services such as intelligent parking decision, convenient inquiry, and route navigation, but also reduce cruising time, energy consumption, and traffic congestion caused by searching available parking lots.

Analysis of parking cruising behaviour and parking location choice

ABSTRACT This paper explores car drivers’ cruising behaviour and location choice for curb parking in areas with insufficient parking space based on a survey of car drivers in Beijing, China. Preliminary analysis of the data show that car drivers’ cruising behaviour is closely related to their parking duration and parking location. A multinomial probit (MNP) model is used to analyse cruising behaviour and the results show that the closer to the destination car drivers are, the more likely they choose to park on the curb. The adjacent locations are the basis of car drivers’ sequential parking decisions at different locations. The research results provide a better understanding of cruising behaviour for parking and recommendations for reducing cruising for parking. The provision of parking information can help regulate the parking demand distribution.