Parking Behavior Research Articles

Analysis of the impact of policy measures on parking behavior using interpretable time series models

Analysis of the impact of policy measures on parking behavior using interpretable time series models

A dynamic electric fence planning framework for dockless bike-sharing systems based on inventory prediction

While dockless bike-sharing systems are growing in popularity due to their convenience, indiscriminate parking creates disorder in cities. To address this problem, this paper proposes a novel prediction-based approach for planning dynamic electric fences. These fences can adapt to dynamic usage patterns and efficiently guide parking behavior. First, graded clustering and spatial point integration algorithms are used to determine the locations of electric fence candidates. Second, initial parking and scheduling simulations are developed to calculate the inventory of each candidate. Finally, a spatiotemporal graph neural network is utilized to predict inventory and generate real-time deployment plans for electric fences. Case studies are conducted in two regions in Shanghai, China. Compared to a non-electric fence scenario, extensive experiments show that our framework can satisfy 98.6% of the parking demand and reduce spatial entropy by 15% within a reasonable walking distance. The results contribute to improving urban orderliness and promoting the sustainability of bike-sharing systems.

Park smart or face the music: Understanding users’ orderly parking behavior of dockless shared bikes from the perspective of deterrence theory

The rapid growth of dockless bike-sharing has revolutionized urban transportation. However, the disorderly parking of dockless shared bikes has emerged as a critical challenge, significantly hindering the benefits of this system by causing obstructions, safety hazards, and public nuisance. This study aims to investigate users’ orderly parking behavior of dockless shared bikes through the lens of deterrence theory. Our findings reveal that punishment severity, punishment certainty, personal norm, and descriptive norm have significant positive effects on users’ attitudes towards orderly parking, which in turn positively influences their orderly parking behavior. This study contributes to the literature by extending the application of deterrence theory to the context of dockless bike-sharing and providing empirical evidence on the role of deterrence factors in shaping users’ orderly parking behavior. Our findings offer valuable insights for bike-sharing operators and policymakers to develop effective strategies.

Study on Parking Adaptability in Urban Complexes on Top of Subways Based on Shared Parking Spaces

Urban complexes on top of subways as a function of intensive building groups, including residential, office, business, and other types nature of land use where parking time differences are obvious, can implement shared parking spaces, thereby reducing the index of parking allotment. Currently, the parking space allocation index for complexes is only a simple superposition of different land uses, resulting in an over-allocation of parking allotments, leading to a waste of land resources and a low utilization rate of parking allotments. Considering the factor of shared parking spaces, this paper conducted an in-depth analysis of the parking adaptability of urban complexes on top of subways and selected five urban complexes on top of subway stations in Wuhan to conduct a parking survey to analyze the parking demand characteristics. This study also investigated the parking behavior of parkers and analyzed the characteristics of parking behavior in urban complexes on top of subways as well as the current parking demand prediction methods and models, establishing a parking demand prediction model based on shared parking spaces and conducting an adaptability analysis. Finally, using five urban complexes in Wuhan as examples, the number of parking spaces demanded by urban complexes on top of subways in 2025 was predicted, and Wuhan Golden Harvest Fashion Plaza was used as an example to verify the feasibility and implementation ability of the theoretical and applied research in this paper.

Does Parking Type Preference Behavior Differ According to Whether It Is Paid or Free? A Case Study in Istanbul, Türkiye

Parking behavior depends on drivers’ choice of parking type and willingness to pay for parking. Generally, the parking type refers to off-street and on-street parking facilities. The main factors affecting the preference for parking types are driver, vehicle, travel, and parking characteristics. Understanding drivers’ parking type preference behavior and accurately modeling drivers’ tendencies helps develop sustainable parking management policies. This study examines the parking preferences of drivers in Istanbul with binary logit models according to whether they pay for parking. The results of the models show that the number of factors influencing parking type preference is higher for free parking than for paid parking, including driver, vehicle, travel, and parking characteristics. Moreover, some factors in the models affect drivers’ parking type preferences differently for paid and free parking. Namely, low-income individuals tend to use on-street parking when parking is free and off-street parking when it is paid. Conversely, individuals who drive small-size vehicles prefer off-street parking for free parking and on-street parking for paid parking. Individuals who prefer off-street parking for free parking expect shorter walking distances to the final destination and parking duration. On the contrary, individuals who choose on-street parking for paid parking anticipate shorter walking distances to the final destination and parking duration.

Potential-based dynamic parking navigation for autonomous vehicles: Near-priority vs. distant-priority

We aim to enhance the parking efficiency for a group of autonomous vehicles in a large parking lot during peak hours. Two parking principles, i.e. the near-priority (NP) and distant-priority (DP) principles, are proposed and quantitatively examined. The NP principle characterizes individual parking behavior, where autonomous vehicles tend to select available parking spaces that are closer in proximity. Conversely, the DP principle is proposed from the perspective of the entire parking system, prioritizing the allocation of more distant parking spaces within a certain range around each entrance. Two time indicators, including the overall parking time and the average parking time, are adopted to evaluate the performance of the two principles. A potential-based cellular automata (CA) model is proposed to formulate the dynamic parking process of vehicles in a two-dimensional space, where vehicle navigation is driven by a so-called potential field. Then, two dynamic navigation algorithms are developed for parking navigation under the NP and DP principles. Furthermore, by conducting a set of comparative simulation experiments, we have obtained some management insights into peak parking management in the era of autonomous driving.

GD-YOLO: An improved convolutional neural network architecture for real-time detection of smoking and phone use behaviors

Chemical safety accidents cause significant socio-economic and environmental hazards, and safety accidents caused by unsafe worker behaviors are preventable. Detection of workers' smoking and phone use behaviors in chemical parks can avoid such safety accidents from the root. Aiming at the issues of reduced accuracy in detecting small objects and low arithmetic capability within industrial equipment. This paper proposes a GD-YOLO network based on YOLOv7 for detecting smoking and phone use behaviors. First, an efficient feature extraction module, D-LAN, is designed to solve the problem of difficult extraction of small object features. Second, a lightweight G-LAN module is proposed to reduce the complexity of the model. Third, WIoU and Mish are introduced to improve the model's performance further, creating a more efficient model. The experimental results show that the mean average precision (mAP) and the number of computational parameters increased by 16.80% and decreased by 21.54%, respectively, compared with YOLOv7. GD-YOLO performs better than other existing mainstream methods in precision and complexity.

Unraveling intra-urban freight parking patterns: A data-driven geospatial study of shared logistics sector in Hong Kong

Insufficient urban parking in densely populated cities has led to challenges like traffic congestion and unauthorized parking. While existing literature extensively covers parking behaviors among private vehicles and taxis for commuters, limited research has been proposed centering on urban shared freight activities. It can be problematic to neglect shared freight activities because these logistics activities often require more flexible parking space for goods loading/unloading near urban destinations. This study therefore introduces a geospatial data-driven approach to investigate urban parking behaviors from shared freight activities using GPS trajectories collected in Hong Kong. The analysis involves three main folds: using rank-size distribution and log-odds ratio to comparatively examine spatial heterogeneity of parking, evaluating illegal on-street parking events via illegal parking index, and quantifying relationships between parking behaviors and urban functions using random forest (RF) and SHapley Additive exPlanations (SHAP). The findings reveal significant concentrations of parking events on weekdays/weekends and with different parking durations. Particularly, high illegal on-street parking concentration is reported in Tsuen Wan and Kwai Chung, where the port container terminals and industrial activities are located. We further discover that industrial and dining densities both indicate significantly positive impacts on the relationship to the increases of overall parking frequency and illegal parking index. This study can be of great interest to current parking management and enforcement strategies in cities and relevant practical implications are discussed.

Prioritizing bicycle parking improvements: An application of the logsum approach

Increasing the number and quality of bicycle parking facilities is essential to promoting cycling. However, quantifying the benefits of such improvements is difficult and has not yet been the focus of research. Apart from rule-of-half-based approaches, logsum analysis is a method for estimating the consumer surplus of measures, reflecting actual behavior.This paper presents such an analysis of a set of measures on the RWTH Aachen University campus as a case study. Considering only the direct consumer surplus, the economic efficiency of the analyzed measures diverges significantly. The replacement of inadequate front wheel racks has the best consumer surplus-cost ratio, while the construction of bicycle parking stations has the worst. Taking into account modal shift effects and related changes in externalities and reduced demand for car parking, the cost-benefit ratios of the measures would be much higher. Even though the presented logsum approach ignores these second-order effects and focuses exclusively on economic efficiency, it is still useful in practice for prioritizing potential measures. Apart from that, the results show that groups of cyclists benefit differently from certain types of measures depending on their student status, employee group, and the resale value of their bicycle. Additionally, the analysis emphasizes the importance of including informal parking when modeling bicycle parking behavior.

What Lies Behind Fare Evasion in Curb Parking Behavior? Evidence from Nanjing, China

Frequent incidents of parking fare evasion pose ongoing challenges, placing parking management departments under pressure as a result of reduced parking revenues. The causal mechanisms behind parking fare evasion, as identified in existing studies, remain incompletely explained. To address this gap, our study utilized empirical data from Nanjing, China, employing a random forest methodology to elucidate the intricate and non-linear relationships among parking site attributes, weather conditions, and the built environment concerning curb parking fare evasion behavior. Additionally, we used the Shapley Additive Explanations method to clarify the pertinent variables. Our empirical findings emphasize the significant impact of parking site attributes, including departure time, parking space availability, and occupancy, on evasion behavior. Notably, our results underscore the necessity of strategically managing parking near scenic spots and points of interest (POIs) associated with accommodation during weekends. Conversely, on workdays, there is a need for strategic oversight of parking fees in the vicinity of government institutions, scenic spots, and workplace-related POIs. These research outcomes make valuable contributions to the existing literature on curb parking and provide actionable insights for improving urban parking management.

A Review on Parking Occupancy Prediction and Pattern Analysis

Parking occupancy prediction and pattern analysis is a crucial component of modern urban management systems. Utilizing advanced data analysis techniques, this project aims to develop a predictive model for forecasting parking occupancy levels and analyzing patterns within parking data. By leveraging machine learning algorithms and statistical methods, the project seeks to provide insights into parking behavior and optimize resource allocation in urban areas. The implementation of parking occupancy prediction and pattern analysis contributes to efficient urban planning, improved traffic management, and enhanced user experience. Through the integration of predictive analytics, decision-makers can anticipate parking demand, optimize parking space utilization, and alleviate congestion in urban areas.This project explores the application of data- driven approaches to address challenges in parking management, including predicting peak parking times, identifying trends in parking occupancy, and optimizing parking infrastructure. By harnessing the power of data analysis, the project aims to enhance urban mobility, reduce environmental impact, and improve overall quality of life. Keywords: Parking occupancy prediction, Pattern analysis, Urban management systems, Data analysis techniques, Machine learning algorithms, Traffic management, Urban planning.

Post-Occopancy Evaluation of Hasanuddin University Urban Park

Urban Park is one of the public open spaces that play a role in preserving the environment. Hasanuddin University Urban Park is known as the lungs of Makassar City. The key of a public open space is influenced by a few ideal criterias based on the ideal criteria for public open space according to Kathleen Madden and the Project for Public Space used in this study, such as: (a) Sociability (social interaction that occurs); (b) Uses and activities; (c) Access and Linkages, and (d) Comfort and Image (a comfortable and interesting place to visit). This study aims to (1) Identify the condition of the Hasanuddin University Urban Park based on the ideal criterias for public open space according to Kathleen Madden and Project for Public Space; (2) Describe the influence of visitor behavior refer to the condition of Hasanuddin University Urban Park. This study uses a descriptive analysis methods and place centered mapping analysis methods, the data collection techniques is using data triangulation (observations, interviews, and secondary data). The result and discussion shows that the condition of the Hasanuddin University Urban Park refer to the ideal criteria for public open space according to Kathleen Madden and Project for Public Space, is some aspects of intangibles and aspects of measurements have not met the criteria. Visitor behavior that occurs in urban parks is influenced by the condition of existing park facilities and attributes such as visitor parking behavior, visitor environmental hygiene behavior and visitor alert behavior.
 Keywords: Evaluation of post-occupancy; ideal criteria for public open space; urban parks; visitor behavior.

A Quantum-Inspired Ant Colony Optimization Algorithm for Parking Lot Rental to Shared E-Scooter Services

Electric scooter sharing mobility services have recently spread in major cities all around the world. However, the bad parking behavior of users has become a major source of issues, provoking accidents and compromising urban decorum of public areas. Reducing wild parking habits can be pursued by setting reserved parking spaces. In this work, we consider the problem faced by a municipality that hosts e-scooter sharing services and must choose which locations in its territory may be rented as reserved parking lots to sharing companies, with the aim of maximizing a return on renting and while taking into account spatial consideration and parking needs of local residents. Since this problem may result difficult to solve even for a state-of-the-art optimization software, we propose a hybrid metaheuristic solution algorithm combining a quantum-inspired ant colony optimization algorithm with an exact large neighborhood search. Results of computational tests considering realistic instances referring to the Italian capital city of Rome show the superior performance of the proposed hybrid metaheuristic.

Exploring parking choices under the coexistence of autonomous and conventional vehicles

The introduction of autonomous vehicles (AVs) is likely to alter conventional parking behaviors in urban areas. In particular, AVs would allow travelers to be dropped off at their destination and park at an economic location or keep self-driving, waiting for the travelers to complete their activities. The present study investigates the parking choices under the coexistence of AVs and conventional vehicles (CVs) and compares their travel impact. To this end, we use an agent-based simulation model to consider different parking options for both vehicles. Most notably, for AVs, these choices include returning to origins, parking at a lot, and continuing self-driving. Results show that if CVs and AVs each make up half of the vehicle fleet, the distance traveled by AVs accounts for over 60% of the total vehicle kilometers traveled (VKT) by all vehicles, and the occupant-free kilometers contributes to around 30% of the VKT traveled by AVs. At 100% penetration of AVs, the total VKT by all vehicles is more than 30% greater than that of completely CVs (i.e., 0% AV penetration). At lower vehicle demand, the percentage of different AV parking choices is relatively stable, and total VKT and travel time increase proportionally with the number of demands. In comparison, at a higher vehicle demand, total travel time increases exponentially while total VKT rises modestly over time. The two pricing policies—lowering parking prices and raising travel costs—have comparable effects in motivating more AVs to park in a parking spot instead of returning to the origin. The simulation and subsequent analyses help understand parking choices and their travel consequences when AVs and CVs coexist.

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.

Dysfunctional tourism behaviors in national parks: An exploration of causes, typologies, and consequences in the case of Saharan tourism

This study explores a seldom explored area of inappropriate tourist behavior in African national parks in the Sahara through a case study to support the management and promotion of tourism activities. Using the critical incident technique, 09 frontline employees of the Tassili N'ajjer National Park in Algeria were interviewed to identify tourist behavior that deviates from generally accepted social norms. Based on the survey results, a theoretical framework consisting of seven causes, nine categories of inappropriate behaviors, and three consequence dimensions was constructed. This theoretical framework provides elements for critical discussion of the sustainability of tourism in national parks, as well as a basis for further research. The results also enrich the literature on the subject and provide a basis for the study of North-South tourism dynamics. Theoretical and managerial implications are also discussed. Managerial implicationsThis research study significantly enhances the strategies employed in the management of national parks and protected areas in several ways. Identifying incentives for Jaycustomer behaviorPoor enforcement of regulations, lack of supervision, low prices, lack of knowledge, ambiguity of experience, egocentricity (common tragedy), anonymity and ethnocentrism are the underlying motivations for inappropriate tourist behavior. Expanded Understanding of Jaycustomer BehaviorIn addition to the categories of jaycustomer behavior identified in previous studies, this research unveils tourist behaviors that have been rarely documented in the literature. These include disengagement, dishonesty, pollution, noise, and waste. Influential Situational FactorsThe study finds that the perceived legitimacy of regulations and the level of supervision are the most influential situational factors affecting jaycustomer behavior. The necessary actions include the need for solid regulation, enforced through vigilant control measures and sanctions, and the improvement of guide performance through staff education and training. Impact of Poor Assimilation and Lack of InformationThe research highlights that poor assimilation of park regulations and a lack of information about them exacerbate cognitive biases in judgment formation, shedding light on the importance of effective communication and education strategies.

Factors influencing hikers’ litter behavior in national park in China

The implementation of the litter behavior initiative aims to mitigate the generation of litter by hikers within the hiking trails of China’s national park. The present study employed the extended norm activation model (NAM) to examine the determinants of hikers’ intention to engage in litter behavior within the context of Wuyishan national park. This analysis incorporated three supplementary explanatory variables, namely environmental knowledge, connectedness to nature, and pro-environmental behavior in everyday life. Additionally, three control variables were included, namely gender, age, and education. A survey was conducted by researchers at Wuyishan national park, with a sample size of 466 hikers. The data obtained from the survey was analyzed using the statistical technique known as structural equation modeling. The findings indicate that the NAM effectively captured hikers’ litter behavior in national parks. Factors such as hikers’ awareness of consequences, ascription of responsibility, personal norm, environmental knowledge, connectedness to nature, and pro-environmental behavior in everyday life were found to significantly influence hikers’ intention to engage in litter behavior. The influence of age on hikers’ intention was shown to be significant and positive, suggesting that older hikers exhibited a greater willingness to engage in litter behavior compared to younger hikers. In conclusion, the study provided practical recommendations for improving the management of hiker trash and promoting sustainable development inside national parks.

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.

Analysis of Factors Affecting Improper Parking Behavior

Analysis of Factors Affecting Improper Parking Behavior

Towards Smart Parking Management: Econometric Analysis and Modeling of Public-Parking-Choice Behavior in Three Cities of Binh Duong, Vietnam

In developing cities, newly emerging cities have started facing the problem of insufficient public parking facilities and ineffective regulations. To support the planning, design and management of the public parking system towards a smart and sustainable city vision, it is necessary to study deeply parking behaviors. This paper presents an empirical study on parking-choice behaviors of motorcycle users and car users in the emerging cities of developing countries through a case study of three cities in Binh Duong, Vietnam. To explore the behavioral mechanisms and influential factors, the multinomial logit parking choice models are developed using revealed preference and stated preference data. The users’ overall satisfaction and perceived importance of parking lot design and service aspects are analyzed using order logistic regression. The revealed choices show no trade-off between parking fee and walking distance, as the users are not fully aware of parking locations and service features. However, the stated choice experiments prove a potential existence of the trade-off mechanism and differentiate significant factors in the decision of choices for the two user groups. The results bring insightful implications for the development of a smart public parking system.