Parking Behavior Research Articles

Analyzing Parking Demand Characteristics Using a Bayesian Model Averaging

Parking duration analysis is an important aspect of evaluating parking demand. Identifying accurate distribution characteristics of parking duration can not only enhance parking efficiency and parking facility planning, but also provide essential support for parking delicacy management. Previous studies have proposed various statistical distributions to depict parking duration data. However, it is difficult to find a certain type of distribution to describe the characteristics of parking duration in diverse parking facilities, since model uncertainty is caused by stochastic parking behaviors and diverse parking environments. To address the model uncertainty, a Bayesian model averaging (BMA) was applied to integrate the advantages of different statistical distributions to depict parking duration characteristics. The parking dataset was collected from a commercial parking lot in Chengdu, China, and the dataset was categorized into two groups (i.e., temporary users and long-term users) to analyze. A set of statistical distributions was chosen as candidate models, and their corresponding unknown parameters were estimated. The posterior model probability for each candidate model was calculated according to the goodness-of-fit (GOF) metric. The findings of the study illustrate that there is no universally applicable distribution form (e.g., log-normal distribution) to depict the parking duration distribution for both user types, whereas the BMA approach assigns weights to candidate models and always provides an accurate description of the parking duration characteristics. The parking duration analysis is useful for improving parking management strategies and optimizing parking pricing policies.

Discovering stop and parking behaviors of last mile delivery vehicles for urban areas based on not well conditioned GPS traces, expert knowledge and machine learning

Nowadays urban traffic is one of the most serious challenges for local authorities around the world. This challenge implies issues regarding health, energy, safety, environment and quality of life. Achieving a fair traffic outcome is a key priority for city traffic managers while maintaining city’s attractiveness for citizens and travelers and those carrying out commercial activities. As a part of this challenge, delivery and similar companies are relevant stakeholders in optimizing their routes, deliveries, and operational services. It is in this respect that urban traffic and its regulations play a key role.The conditions in which urban traffic takes place thus have to be known and one way to deal with this is based on available and accessible data. In the IoT age, there could be huge amounts of available data generated by countless sensors and systems involved in our lives, cities, infrastructures, etc.... Taking advantage of such data, when the accessibility and quality is good enough, can help us to achieve the desired goals concerning the urban traffic and its consequences. However, in practice, the availability and access to such data is currently a very serious challenge.Following on with this, data generated from the ever-increasing number of sensors on board vehicles could be very useful, not only for checking the vehicle condition, but also to gain a better of the “real-time” traffic situation or to discover traffic behaviors/patterns from the said data. In this work, a real case based study has been carried out gathering basic real GPS information regarding delivery vehicles in a city environment and OpenData to “discover” where, when and how long time delivery vehicles use the regulated parking zones for loading/unloading in the city center. Based on Expert Rules, a stop detection criteria is defined and formulated to be applied to real cases in urban areas, focusing on city centers and Machine Learning techniques to discover stop and park behaviors on last mile deliveries in a real urban area.All this is used to plan traffic strategies and facilities which can permit better and more fluent services. On the other hand, the results provide invaluable knowledge support for the expert knowledge of mobility managers, while also supplying new “findings” about the daily challenges, showing that machine learning techniques and other linked technologies are powerful tools for this challenge.

Parking Prediction in Smart Cities: A Survey

With the growing number of cars in cities, smart parking is gradually becoming a strategic issue in building a smart city. As the precondition in smart parking, accurate parking prediction can reduce the time drivers spend searching for parking spaces and relieve traffic congestion. Meanwhile, VANET and the Internet-of-things (IoT) are the key elements of the current intelligent transportation system. With the IoT devices based on VANET becoming more extensively employed, a large amount of parking data is generated every day, and various methods are proposed for parking prediction, therefore, it is time to systematically summarize the parking prediction issues and the state-of-the-art prediction methods. In this survey, we first provide a comprehensive review of the existing methods used for parking prediction ranging from conventional statistical methods to the latest graph neural network methods. Then, we classify a variety of parking problems such as parking availability prediction, parking behavior prediction, and parking demand prediction. We also compile all the evaluation metrics, open data, and open-source code of the surveyed literature. Finally, we present the challenges and future directions of the parking prediction technique. As far as we know, this is the first survey exploring parking prediction methods, which will be of interest to both researchers and practitioners engaging in intelligent transportation systems (ITS) and smart cities.

Predicting University Campus Parking Demand Using Machine Learning Models

Parking demand at university campuses has been an issue for decades and is gradually increasing each year. With limited capacity, space, and funds to expand parking facilities, there is a dire need to better understand parking behavior on a university campus so that universities can better utilize the limited resources available. One methodology that has been used by Metropolitan Planning Organizations to predict traveler behavior is known as travel demand modeling, where the most common modeling technique is a four-step procedure that utilizes socioeconomic data to predict current and future traffic volumes in a network (e.g., a city). This study focuses primarily on the trip generation step, and The University of Texas at Tyler campus was used as a case study. First, each parking lot on campus was assigned to a parking demand zone (PDZ) based on its proximity to classroom buildings, and the hourly arrival demand for each PDZ was measured using pneumatic tube counters. The course schedule and floor space utilization data for each building on campus were then extracted as input parameters to predict the parking demand at each PDZ. A linear regression model and two artificial neural network (ANN) frameworks were developed. One ANN model was recommended, as it had an R-squared value of 0.846. From the selected ANN model, an equation has been extracted, which has the potential to be used by course schedulers to modify the course schedule to better mitigate parking demand on campus without the need to develop new parking facilities.

Exploring the Influence of Parking Penalties on Bike-Sharing System with Willingness Constraints: A Case Study of Beijing, China

Dockless bike-sharing has experienced explosive growth, establishing itself as an integral component of urban public transport systems. Challenges such as parking violations have spurred operators and users to pursue standardized management. While electronic parking spots are employed to promote standard parking, suboptimal parking layouts can lead to illegal parking. Inadequate post-violation penalties fail to achieve standard parking, while excessive punishment diminishes user engagement. This study combines parking spot density and penalties to incentivize standard parking, and Beijing, China, was selected as the research object. Using an SP questionnaire survey, a binary logistic model analyzes bike-sharing users’ standard parking behavior and willingness to adhere to different rules. Findings reveal that optimal walking distances range from 300 to 450 m for service levels and exceed 400 m for service efficiency. Influential factors include gender, age, occupation, usage behavior, and travel preferences. Users with high-frequency, low-convenience expectations, low travel costs, and flexible travel exhibit strong adherence. Additionally, user acceptance of the maximum distances without penalties follows an exponential distribution, with 80% accepting 400 m and 40% accepting 800 m. Enforcement has a visible effect within 300 m, but diminishes with longer distances. Excessive penalties result in significant user loss.

Model-Based Predictive Control and Reinforcement Learning for Planning Vehicle-Parking Trajectories for Vertical Parking Spaces.

This paper proposes a vehicle-parking trajectory planning method that addresses the issues of a long trajectory planning time and difficult training convergence during automatic parking. The process involves two stages: finding a parking space and parking planning. The first stage uses model predictive control (MPC) for trajectory tracking from the initial position of the vehicle to the starting point of the parking operation. The second stage employs the proximal policy optimization (PPO) algorithm to transform the parking behavior into a reinforcement learning process. A four-dimensional reward function is set to evaluate the strategy based on a formal reward, guiding the adjustment of neural network parameters and reducing the exploration of invalid actions. Finally, a simulation environment is built for the parking scene, and a network framework is designed. The proposed method is compared with the deep deterministic policy gradient and double-delay deep deterministic policy gradient algorithms in the same scene. Results confirm that the MPC controller accurately performs trajectory-tracking control with minimal steering wheel angle changes and smooth, continuous movement. The PPO-based reinforcement learning method achieves shorter learning times, totaling only 30% and 37.5% of the deep deterministic policy gradient (DDPG) and twin-delayed deep deterministic policy gradient (TD3), and the number of iterations to reach convergence for the PPO algorithm with the introduction of the four-dimensional evaluation metrics is 75% and 68% shorter compared to the DDPG and TD3 algorithms, respectively. This study demonstrates the effectiveness of the proposed method in addressing a slow convergence and long training times in parking trajectory planning, improving parking timeliness.

Mobility‐aware optimal trade planning and mean field game‐based decentralized charging control for large‐scale electric vehicles

AbstractThe uncertainty mobility characteristics of electric vehicles (EVs) are a key factor that influence the charging station trade income with the power grid. In this paper, a hierarchical energy management strategy is proposed for charging stations when large‐scale EVs are considered. The traveling and parking mobility characteristics of EVs are first extracted by analyzing historical real‐world traveling data. Then, a day‐ahead power trade planning strategy between the grid and charging station to maximize incomes is developed by solving a mixed‐integer linear programming problem. Furthermore, based on EV mobility characteristics, clusters are generated to classify different parking behaviors, where a mean field game‐based decentralized charging control scheme for individual EVs in each cluster is employed. The target of the proposed optimal charging controller is to guarantee the charging power tracking performance and the power demand of individual EV at the terminal time for subsequent travel. Finally, simulations on MATLAB/Simulink platform are conducted to show the effectiveness of the proposed hierarchical energy management strategy.

Spatial-temporal heterogeneity and built environment nonlinearity in inconsiderate parking of dockless bike-sharing

Although previous studies have shed light on the travel behaviour of dockless bike-sharing (DBS) users, little research focused on their inconsiderate parking behavior. Unlike the travelling behavior, the choice of parking location is closely linked to the different built environments surrounding the parking locations. Therefore, to improve the efficiency of governance, it is vital to explore the parking patterns and heterogeneous influences of the built environment on inconsiderate parking and formulate targeted measures. This paper measures the coordinates of prohibited parking areas in the field to identify inconsiderate parking. Based on big data from Mobike DBS and data on the built environment, the paper empirically analyzes the heterogeneous spatiotemporal distribution patterns of inconsiderate parking with clustering and decision trees. The influencing factors of inconsiderate parking and their nonlinear effects are further analyzed using random forest and partial dependence plots (PDP). The results show that there is significant spatiotemporal heterogeneity in inconsiderate parking, in which different clusters reflect various characteristics of the built environment. Furthermore, marginal effect analysis finds that influencing factors such as riding distance, catering service places, lifestyle services, sports and leisure places, and hotels and hostels have a strong effect on inconsiderate parking behavior, and show nonlinear effects with optimal allocation intervals. Therefore, targeted strategies should be carried out in terms of dynamic temporal adjustment, precise spatial layout, differential management according to time and zone, and cause-assisted administration. The paper’s results provide important decision-making support for inconsiderate parking.

What Causes Curbside Illegal Parking Behavior: A Method Based on Structural Equation Model

Reducing curbside illegal parking is a crucial aspect of urban parking management. However, there is currently a dearth of relevant research. Further analysis is required to determine the factors that influence curbside illegal parking behavior. To this end, a questionnaire survey was conducted in Nanjing, and exploratory factor analysis was used to evaluate the explanatory power of the measurement variables. From this, driver factors, traffic conditions, parking facilities and environment were identified as latent variables. A structural equation model was created to examine the relationship between these variables. The findings indicated that parking facilities and driver factors had a direct and positive correlation with curbside illegal parking behavior, while the environment had a direct and negative correlation with such behavior. Moreover, parking facilities had the greatest impact. The study highlighted the effectiveness of measures such as promoting traffic regulation awareness, optimizing public parking lot charging standards, and strengthening penalties for curbside illegal parking behavior. The results of this research will contribute to a greater comprehension of the phenomenon and provide a theoretical foundation for curbside parking management.

Spatial heterogeneity of urban illegal parking behavior: A geographically weighted Poisson regression approach

Spatial heterogeneity of urban illegal parking behavior: A geographically weighted Poisson regression approach

Authorized Vehicles Only: Police, parking, and pedestrian access in New York City

Sidewalks and crosswalks do not benefit pedestrians if they are blocked by automobiles. In New York City, local media have documented that cars outside of law-enforcement offices routinely park on sidewalks and in crosswalks. This study systematically and longitudinally pursues this topic by tracking the geographic extent of this obstructive parking via in-person observation of the streets surrounding all 77 New York Police Department (NYPD) station houses, across all five boroughs. Combined with review of historical street imagery, this approach reveals widespread and longstanding parking on sidewalks, and to a lesser extent, in crosswalks. Of 77 NYPD station houses, 70 (91%) exhibited parking of at least one of these kinds, with sidewalk parking often extending along the entire block (and not simply in front of station houses), on adjacent blocks, and on both sides of the street. This renders many sidewalks impassable – forcing pedestrians into traffic – and in many cases directly abuts residences and businesses, curtailing access to such destinations. Longitudinally, such obstructive parking was present across 82% of 703 street-imagery data points, indicating this has largely become the default (and not occasional) use of adjacent sidewalks. In-person observation also demonstrated other ways this parking has had negative effects surrounding NYPD station houses, including double parking, obstruction of bus and bike lanes, and blocking of fire hydrants. These findings broaden the study of pedestrian accessibility and safety beyond the quality and design of sidewalks and intersections, to include chronic automotive obstruction, and indicate that parking behavior surrounding public-sector offices, if left unchecked, significantly degrades walkability.

Parking on the integers

Models of parking in which cars are placed randomly and then move according to a deterministic rule have been studied since the work of Konheim and Weiss in the 1960s. Recently, Damron, Gravner, Junge, Lyu, and Sivakoff ((2019) Ann. Appl. Probab. 29 2089–2113) introduced a model in which cars are both placed and move at random. Independently at each point of a Cayley graph G, we place a car with probability p, and otherwise an empty parking space. Each car independently executes a random walk until it finds an empty space in which to park. In this paper we introduce three new techniques for studying the model, namely the space-based parking model, and the strategies for parking and for car removal. These allow us to study the original model by coupling it with models where parking behaviour is easier to control. Applying our methods to the one-dimensional parking problem in Z, we improve on previous work, showing that for p<1/2 the expected journey length of a car is finite, and for p=1/2 the expected journey length by time t grows like t3/4 up to a polylogarithmic factor.

COVID-19 pandemic changes the recreational use of Moscow parks in space and time: Outcomes from crowd-sourcing and machine learning

The limited access to urban green spaces during the COVID-19 pandemic had a negative impact on the human-nature interaction in cities and human well-being. Number of visitors to green areas, initially declined due to imposed restrictions, was restored after they were lifted as established by several studies across the globe However, little is still known about changes in behavior and preferences of park visitors in the post-COVID time. In this study, we investigated spatial-temporal patterns of recreational activities in the three urban parks in Moscow (Russia) prior, during and after the COVID-19 lockdown (in 2019 and 2020). The selected parks represent two different types: a centrally located park with much infrastructure and open landscapes (Gorky Park) and parks located at the outskirts of the city center with a more forested landscape and little infrastructure (Timiryazevski and Sokolniki parks). Recreational activities were identified based on the analysis of social media photos using machine-learning algorithms. As expected, park closures during lockdown resulted in overall decrease in the number of taken photos. After the parks were re-opened, however, the number of photos did not grow immediately. The number of photos only restored after almost three months, and the visiting peak shifted to autumn. Differences between parks were related to the type of the park and its landscape structure. The lowest decrease in the number of photos was observed for the Timiryazevsky park – a semi-natural green area, while the centrally located Gorky Park was the most affected, likely due to the strictest control measures. In comparison to 2019, photos in 2020 were more evenly distributed across the area in all the three parks. Besides, ‘natural’ areas became the main attractors for the visitors - photos under ‘nature observation’ category became the most popular. Spatial distribution of the recreational activities in post-lockdown period was characterized by larger distances between photos, likely corresponding to the social distancing. COVID-19 pandemic highlighted the value of green areas for citizens, but also changed their recreational preferences and overall behavior in parks. The observed shift from high density of visitors around entertainments and attractions in 2019 to a more homogeneous and less dense distribution along the natural zones in 2020 reveals a new pattern in visitors behavior and preference, which shall be considered in spatial planning of the parks. Increasing availability of natural green areas and their integration in urban green infrastructures can become the most relevant policy to consider the crucial role of urban nature as a source of resilience in turbulent times.

Tracking Parking Search and Occupancy in Zurich

This article describes how parking search times and occupancy in a highly attractive area in central Zurich (Switzerland) develop on a weekday morning when a farmer’s market takes place on Bürkliplatz, a central market place inside the study area. Individual vehicles were tracked by bike, drivers were interviewed about their parking behaviour and their route was tracked using GPS. Additionally, the parking occupancy was registered every fifteen minutes during a five-hour time period. A connection between market opening hours and parking dynamics within the perimeter was observed. Drivers usually overestimate their parking search duration.

Developing Nature Interpretation and Alternative Management Strategies to Promote Pro-Environmental Behaviors among Visitors to Thailand's National Parks

The purpose of this study was to recommend nature interpretation and alternative management strategies to enhance pro-environmental behaviors among visitors by studying the factors that influence these behaviors. The samples were 1400 Thai visitors and 20 national park officers. Questionnaire data was analyzed using path analysis and content analysis. Taking into account research findings, the 11 of interpretive messages were created to promote the behaviors in national parks across the country. The message that is deemed most appropriate for encouraging visitor behaviors was: "A national park is a treasure for everyone, and it is important that we all do our part to protect it. If you see any activities that have the potential to destroy natural resources or harm the environment, please inform the park officers immediately." Besides, developing effective nature interpretation and preparing public facilities have been recommended as major alternative management strategies. The most appropriate high-level strategy for developing effective nature interpretation was found to be "utilizing new technologies to support nature interpretation." The survey respondents identified "setting up different colored garbage bins" and "installing fences to preserve fragile areas" as the most effective strategies for preparing public facilities and managing visitor behaviors in Thailand's national parks.&#x0D;

Elderly's intention and use behavior of urban parks: Planned behavior perspective

An urban park is a vital environment that promotes the elderly's physical, social and psychological health. Numerous studies have been conducted to determine the influencing factors that could encourage the elderly's usage of urban parks. However, little attention has been paid to elderly's intention of using urban parks. This study applies the theory of planned behavior (TPB) to understand the relationship between elderly's intentions of using urban park and use behavior. According to the nature of the different activity types elderly people intended to do in urban parks, three sub-models are built based on the original TPB model, which are social, enjoyment and physical activity-based models. These models explore the influencing factors that may affect elderly's use of urban park under the three different intentions. This study employed focus group meetings and questionnaire surveys (N = 537) to collect data and applied structural equation modelling to test the proposed social, enjoyment and physical activity-based models. In addition, this study found that elderly's use behavior of public parks was significantly affected by their intention of using public parks. The use behavior was also directly associated with intrapersonal constraints (degree of liking) and structural constraint variables (available leisure time and physical accessibility). Meanwhile, use behavior is indirectly associated with intrapersonal constraint variables (elderly's perception to urban park) and interpersonal constraints (conflict of use with other groups) through the influence of their intentions of using public parks. Furthermore, physical accessibility may affect elderly's use behavior, but it has no effect on elderly's intention of using urban parks. The findings and proposed models provide theoretical and practical contributions to better understand elderly's use behavior of urban park to encourage healthy ageing in an urban dense city.

Development of a Data-Driven On-Street Parking Information System Using Enhanced Parking Features

On-street parking information (OSPI) systems help reduce congestion in the city by lessening parking search time. However, current systems use features mainly relying on costly manual observations to maintain a high quality. In this paper, on top of traditional location-based features based on spatial, temporal and capacity attributes, vehicle parked-in and parked-out events are employed to fill the quality assurance gap. The parking events (PEs) are used to develop dynamic features to make the system adaptive to changes that impact on-street parking availability. Additionally, a parking behavior change detection (PBCD) model is developed as an OSPI supplementary component to trigger potential parking map updates. The evaluation shows that the developed OSPI availability prediction model is on par with state-of-the-art models, despite having simpler but more enhanced and adaptive features. The foundational temporal and aggregated spatial parking capacity features help the most, while the PE-based features capture variances better and enable adaptivity to disruptions. The PE-based features are advantageous as data are automatically gathered daily. For the PBCD model, impacts by construction events can be detected as validation. The methodology proves that it is possible to create a reliable OSPI system with predominantly PE-based features and aggregated parking capacity features.

A Two-Stage Location and Allocation Framework of Dockless Bike-Sharing System

The dockless bike-sharing system has received widespread attention as a green mode of travel. To improve operational efficiency and promote orderly parking behavior, this article proposes a new two-stage location and allocation model based on the hexagon grids. In the first stage, we build a location model integrating the set-covering part and the flow-capturing part. The model comprehensively considers the walking endurance limit, the point-demand in the grid and the path-demand among grids to determine the optimal candidate grids. In the second stage, we establish two allocation models to calculate the renting and parking demand in each optimal candidate grid and obtain the final optimal grids. In addition, we verify the feasibility of cycling demand transfer by Moran’s index and design the effective spatial distribution of bike-sharing system by grid density function. Empirical results show that the number of the selected grids (8% are newly added grids) is only 53% of the initial grids without cycling demand lost. It means the new framework in this paper can effectively alleviate traffic congestion in crowded urban and ensure that travelers’ demand will not decrease at the same time.

Analysis of user's car parking behaviour through twitter hashtags

AbstractDue to its ease and popularity, social media has recently become an essential source of data for researchers and various stakeholder groups that seek a reliable assessment of their policies based on a comprehensive understanding of users' feedback and inputs, which are reflected in their posts and discussions. In this study, we investigate the issue of users' car parking behaviour through a comprehensive analysis of related hashtags collected from the Twitter social media platform. For this purpose, we adopted a two‐step strategy where in the first stage, a surface‐level analysis of the identified hashtags involving inductive reasoning, sentiment analysis, and user interaction in terms of engagement and diversity scores is performed. In the second phase, a tweet content analysis is performed using sentiment analysis and Empath categorization with respect to the most frequent wordings (assimilating to separate topics), in the same spirit as aspect‐sentiment analysis, to gain further insights regarding the occurrence of negative and positive posts. A quantitative evaluation of the coherence of the Empath categorization indicates that achieved 0.83% coherence score and outperformed both LDA and LSA that had a score of 0.77% and 0.76% respectively. Furthermore, the common word technique assimilated to aspect sentiment compared to the state‐of‐the‐art model for aspect sentiment‐based deberta‐v3‐base model. Besides, the influence of bots or spammers is evaluated using engagement/diversity measures and Botometer API. The results provide valuable insights in terms of discriminating between positive and negative posts and the correlation of surface‐level analysis with content‐based analysis, as well as the impact of various categorizations. The results expect to enable urban planners and policymakers to advance evidence‐based policing in the future design of intelligent parking systems.

Periodic Weather-Aware LSTM With Event Mechanism for Parking Behavior Prediction

There are plenty of parking spaces in big cities, but we often find nowhere to park. For example, New York has 1.4 million cars and 4.4 million on-street parking spaces, but it is still not easy to find a parking place near our destination, especially during peak hours. The reason is the lack of prediction of parking behavior. If we could provide parking behavior in advance, we can ease this parking problem that affects human well-being. We observe that parking lots have periodic parking patterns, which is an important factor for parking behavior prediction. Unfortunately, existing work ignores such periodic parking patterns in parking behavior prediction, and thus incurs low accuracy. To solve this problem, we propose PewLSTM, a novel periodic weather-aware LSTM model that successfully predicts the parking behavior based on historical records, weather, environments, weekdays, and events. PewLSTM includes a periodic weather-aware LSTM prediction module and an event prediction module, for predicting parking behaviors in regular days and events. PewLSTM is extremely useful for drivers and parking lot owners to improve customer experience. For example, the probability of parking space that will be available soon can be provided even if the parking lot is full. Based on 910,477 real parking records in 904 days from 13 parking lots, PewLSTM yields 93.84% parking prediction accuracy, which is about 30% higher than the state-of-the-art parking behavior prediction method. Additionally, we have analyzed parking behaviors in events like holidays and COVID-19. PewLSTM can handle parking behavior prediction in events and reaches 90.68 percent accuracy.