Parking Choice Behavior Research Articles

Modeling Parking Choice Behavior Using Cumulative Prospect Theory

In order to capture the driver’s attitude and preference towards risk during the decision-making process, this paper establishes a parking choice model considering driver heterogeneity based on the cumulative prospect theory. This research innovatively considers the influence of the unreliability of cruise time on drivers’ parking choice behavior and adds the psychological cost of drivers’ anxiety caused by lateness into the model. At the same time, according to the driver’s parking preference for parking, the latent category model is used to divide the driver into time-sensitive and money-sensitive categories. This paper analyzes the influence of unreliable cruising time on drivers’ parking choice behavior and finds that drivers have the characteristics of overestimating high-probability events and underestimating low-probability events in the decision-making process. By comparing the parking choice results of rational and irrational drivers, it is found that the model considering the risk attitude of drivers in the decision-making process is more in line with reality.

Planning for bicycle parking: Predicting demand using stated preference and count data

Predicting bicycle parking demand is critical to optimizing parking facilities and thereby promoting cycling. Unfortunately, previous studies have not considered facility type and location when predicting bicycle parking demand, which is critical to meeting user needs, especially in scenarios with multiple parking options, such as on university campuses, as in our case study. The paper presents a predictive model for bicycle parking demand using a synthetic population derived from building space utilization data, a mobility survey, parking facility data, and results from a stated preference experiment on bicycle parking preferences. We evaluate the model’s quality using count data from 2022 and 2023 and the influence of including facility types (front wheel racks, u-racks, bicycle parking stations) and whether they are covered. We also analyze the influence of beeline-based distances to reach a facility and to get from the facility to the destination and examine how to weigh them.Incorporating facility types and coverage substantially improves the model’s predictive accuracy, but only if the model’s sensitivity to walking distances between facilities and buildings is increased. This suggests that stated preference experiments on bicycle parking choice behavior may underestimate cyclists’ sensitivity to walking distances. In contrast, accounting for cycling detours to reach a facility does not contribute to prediction quality. Thus, when cyclists have multiple parking options, it is crucial to consider walking distances for realistic predictions. Furthermore, user-centered planning requires careful consideration of parking facility attributes and the specific preferences of target cyclist groups when determining the size and location of parking facilities.

Parking choice behavior of urban village residents considering parking risk: An integrated modeling approach

With rising car ownership, the demand for parking and land for parking is increasing rapidly. This strains urban land resources and accentuates the performance of urban villages, characterized by limited space, high building density, narrow roadways, and few parking spaces, making parking challenging. To address this, the study focuses on guiding residents' parking choices from a parking management perspective, aiming to reduce the demand for parking spots among village members. In April 2021, a survey was conducted in Shenzhen City, where urban villages are widely dispersed. The survey revealed that residents primarily rely on on-street parking within the village, off-street parking within the village, and on-street parking outside the village. Due to the unquantifiable nature of the latent variable of parking risk and the impact of individual heterogeneity, this paper adopts a discrete selection model that integrates principal component analysis and mixed logit model (PCA-ML). The model demonstrated a good fit, indicating that parking charges, search time, and walking distance to home have negative impacts on parking choices. In addition, street parking outside the village is positively influenced by the parking risk attitude variable, while village parking remains unaffected. This study assists authoritative departments in understanding the factors that influence parking management in urban villages, thereafter, adopting targeted guidance to direct residents in urban villages to park their vehicles, mitigating the current situation of parking difficulties in urban villages. Furthermore, it contributes to achieve equitable and sustainable development of urban villages.

Parking choice behavior analysis of rural residents based on latent variable random forest model

ABSTRACT The imbalance of rural parking supply and demand has a great impact on traffic congestion and environmental pollution, which has attracted the attention of many scholars as well as policymakers. However, most of the current research on parking choice mainly focuses on urban business and residential areas, lacks research on rural parking choice behavior, and focuses on the analysis of observable factors, ignoring the internal relationship with potential variables. Based on this, this study considers the heterogeneity of individuals and uses the random forest algorithm to construct a model of rural residents’ willingness to choose parking with both latent and explicit variables, to explore how much and in what ways individual characteristics and parking characteristics affect rural residents’ parking choices, and to explore parking planning programs and strategies that are truly applicable to rural areas. The results of the study suggest that safety and convenience of the parking environment are key factors influencing the parking choice behavior of rural residents, and can greatly improve the predictive accuracy of the parking willingness model. Upon comparison, it is found that the application of the random forest algorithm is also significantly better than the logit model in terms of prediction effect, indicating that there is a nonlinear effect among the factors influencing the parking choice behavior of rural residents and that the random forest model with the addition of latent variables provides a better explanatory ability for the study of the parking choice behavior of rural residents.

Use of Structural Equation Modelling and Neural Network to Analyse Shared Parking Choice Behaviour

The shared parking mode represents a feasible solution to the persistent problem of parking scarcity in urban areas. This paper aims to examine the shared parking choice behaviours using a combination of structural equation modelling (SEM) and neural network, taking into account both the parking location characteristics and the travellers’ characteristics. Data were collected from a commercial district in Nanjing, China, through an online questionnaire survey covering 11 factors affecting shared parking choice. The method involved two steps: firstly, SEM was applied to examine the influence of these factors on shared parking choice. Following this, the seven factors with the strongest correlation to shared parking choice were used to train a neural network model for shared parking prediction. This SEM-informed model was found to outperform a neural network model trained on all eleven factors across precision, recall, accuracy, F1 and AUC metrics. The research concluded that the selected factors significantly influence shared parking choice, reinforcing the hypothesis regarding the importance of parking location and traveller characteristics. These findings provide valuable insights to support the effective implementation and promotion of shared parking.

Pricing curb parking: Differentiated parking fees or cash rewards?

Parking price adjustment is an effective method for influencing curb parking choices, playing a crucial role in alleviating urban parking shortages and balancing uneven distribution. The government has extensively explored the implementation of differentiated parking fees and cash incentives for curb parking; however, few studies have investigated the effects of these policies on parking balance management. This study utilized a questionnaire survey to construct two multinomial logit models and examine the mechanisms behind drivers' choice behaviors. Taking the curb parking facilities in Yinzhou District, Ningbo as the case study area, the optimal dynamic pricing schemes for the two strategies were established, and their operational effectiveness was evaluated. The results show that parking cost, value of cash rewards, parking occupancy, and surrounding road conditions have a significant impact on curb parking choice behavior. Both strategies can effectively balance the utilization rate of curb parking space-time resources. The cash rewards strategy yields higher earnings compared to the differentiated parking fee strategy, but the latter is more conducive for reducing the occupancy during peak hours. The research findings of this study can provide insights and a theoretical foundation for real-time and proactive management of roadside parking.

Where to park an autonomous vehicle? Results of a stated choice experiment

The future innovation and growing popularity of autonomous vehicles have the potential to significantly impact the spatiotemporal distribution of parking demand. However, little knowledge is gained on how people will choose to park their autonomous cars. In principle, an autonomous vehicle is not necessarily parked close by like traditional vehicles leveraging the automated driving and parking capability, still, the decision made by people is important for policymakers in urban and transportation planning. This study attempts to gain useful insights to understand people’s parking location choices for autonomous vehicles. A stated choice experiment was designed, allowing people to choose a parking location for autonomous vehicles in varied contexts, including time windows, picking-up times, and the requirement for on-time arrival at the next activity. We found that similar to conventional cars people generally prefer cheaper and/or closer parking lots for autonomous vehicles. However, the distance between a parking lot and the activity location is relatively longer in the case of autonomous vehicles. The amount of time an autonomous vehicle spends in congestion while picking up the users influences the choice of parking locations. Moreover, substantial preference heterogeneity between individual people was found in the parking choice behavior. The maximum value of access time for autonomous cars is 34 $/h which is higher than the empirical value of walking time for conventional cars. Results of elasticity indicate that the influence of parking fees is larger than that of access time and congestion time.

Modelling Parking Choice Behaviour Considering Alternative Availability and Systematic and Random Variations in User Tastes

Parking choice models proposed over recent years have been aimed at choosing the relevant variables and pricing policies affecting said choice. In our analyses, we have encountered some gaps to cover; for this reason, we introduce a methodology for addressing user parking choices, including the availability of parking alternatives in each of the studied zones presenting pivoted scenarios. This methodology has been developed based on a stated preferences survey design to define user behaviour when making parking choices through discrete-choice models. Furthermore, the estimated models have considered systematic and random variations in user tastes and the correlation between similar or related parking alternatives. This methodology has made it possible to provide greater realism in generating fictitious parking scenarios. The results show the importance of the fee attribute and the differences in the users’ preferences depending on whether they live inside or outside the study area and their arrival time at the destination. Several scenarios have been simulated, and in these scenarios, it is found that removing free parking areas or restricting free parking in favour of parking fee regulation in congested zones can be an efficient policy measure to promote other parking alternatives and reduce the number of vehicles searching for a parking space.

Analysis of taste heterogeneity in commuters’ travel decisions using joint parking– and mode–choice model: A case from urban India

The concept of transportation demand management (TDM) upholds the development of sustainable mobility through the triumph of optimally balanced transport modal share in cities. The modal split management directly reflects on TDM of each transport subsystem, including parking. In developing countries, the policy-makers have largely focused on supply-side measures, yet demand-side measures have remained unaddressed in policy implications. Ample literature is available presenting responses of TDM strategies, but most studies account mode choice and parking choice behaviour separately rather than considering trade-offs between them. Failing to do so may lead to biased model estimates and impropriety in policy implications. This paper seeks to fill this gap by admitting parking choice as an endogenous decision within the model of mode choice behaviour. This study integrates attitudinal factors and built-environment variables in addition to parking and travel attributes for developing comprehensive estimation results. A mixed logit model with random coefficients is estimated using hierarchical Bayes approach based on the Markov Chain Monte Carlo simulation method. The results reveal significant influence of mode/parking specific attitudes on commuters choice behaviour in addition to the built-environment factors and mode/parking related attributes. It is identified that considerable shift is occurring between parking-types in preference to switching travel mode with hypothetical changes in parking attributes. Besides, study investigates the heterogeneity in the willingness-to-pay through a follow-up regression model, which provides important insights for identifying possible sources of this heterogeneity among respondents. The study provides remarkable results which may be beneficial to planning authorities for improving TDM strategies especially in developing countries.

Modelling parking type choice behavior

• An econometric parking choice model is developed to investigate parking choice behavior. • We derive cruising and walking time valuations. • Drivers are willing to pay more for reducing cruising and walking time. • Trip purpose and turnover policy affects the choice of the type of parking. • Research on the Tunisian drivers’ behavior. Cruising for a parking space represents one of the major sources of urban congestion. Search for Parking generates additional urban traffic, energy consumption, vehicle emissions and time waste. Thus, it is essential to understand how drivers make decisions to select their parking lots. The estimation of models that reproduce parking driver's attitudes is crucial to improve the performance of parking facilities and transport systems. This study aims to understand the different behavioral considerations that rule the choice of drivers to choose one parking type among three alternatives: on-street parking, off-street parking and underground parking. For this purpose, we propose a multinomial logit model by using data obtained with a revealed preference surveys of drivers' parking type choices and associated factors in the city. Our findings provide authorities with additional perspectives for creating an efficient parking policy and associated land use planning. Indeed, to tackle the parking issue we had to pay attention to driver’s preferences regarding future parking supply options.

Time-Domain Characteristics of Residential Parking and SEM-BL Integration Model of Parking Method Choice Behaviour

Correct understanding of the residential parking characteristics and parking behavioral regularities is important to the planning of parking facilities, the selection of parking equipment, and the formulation of parking management measures in residential areas. There are few in-depth reports on the time domain characteristics of the residential parking and the parking choice behavior of developers and drivers. Besides, the influence of developers’ potential demands and drivers’ mental latent variables on the results of parking choice has been ignored. To address the gap, in this paper, we propose the concept of latent variables for parking choice based on the analysis of the time domain characteristics of residential parking in different cities and areas. We construct the SEM-BL integrated model for developers and drivers that considers the combined effect of both manifest and latent variables for the actual residential situation and analyze the factors influencing the choice of stereoscopic parking facilities from the perspective of developers and drivers in residential areas. The results show that the type of residential area, floor space, and plot ratio have a critical impact on parking choice. Residential drivers are most concerned about factors such as walking distance after parking, access convenience, pick-up time, pedestrian access, and environment, while developers choose a stereoscopic parking garage with more concern for economy, ignoring the consideration of drivers for walking distance after parking and convenience of accessing the car. Therefore, the mismatch between the needs of both sides is one of the important factors that make it difficult to solve the problem of parking in residential areas. Based on these results, we put forward a proposal for residential parking planning and equipment selection that takes into account the common needs of both developers and drivers.

Research on parking choice behavior of shared autonomous vehicle services by measuring users’ intention of usage

Shared autonomous vehicles (SAVs) are one of the important development directions of smart and green transportation. However, relevant researches are not sufficient at present. The factors influencing the intention to use SAVs and their parking choice behaviors need to be further analyzed. First, in order to better explain, predict, and improve travelers’ intention to use SAVs, the conceptual framework based on technology acceptance model was developed to establish the relationships between the travelers’ intention to use SAVs, social influence of SAVs, attitude toward behavior of SAVs, perceived risk of SAVs, perceived usefulness of SAVs and perceived ease of these use. Then structural equation model (SEM) was established to analyze the relationship between various variables. The results show that the perceived usefulness, behavior attitude, social influence, perceived ease of use, and perceived risk are the main factors that determine the intention to use SAVs. Through the test of direct effect, indirect effect, and total effect in the model, it is found that perceived usefulness has the largest total impact on intention to use SAVs, with a standardized coefficient of 0.765, followed by behavior attitude (0.732), social influence (0.597), perceived ease of use (0.462) and perceived risk of SAVs (−0.452). In addition, through the study of observed indicator variables ATB2 and BI3, it is found that perceived usefulness, perceived ease of use, social influence, perceived risk, attitude toward behavior, and behavior intention all have an impact on parking behavior. In order to study the specific influencing factors of parking choice behavior, a multinomial logit (MNL) model was established to analyze the relationships between travelers’ parking choice behaviors and the influential factors, which include travelers’ individual characteristics, travel attributes, and parking modes’ attributes by extracting from a questionnaire. The results show that the travel time, travel fees, parking charge, cruising fees, parking time and traffic emission are the main factors that determine travelers’ choices of parking. This paper provides advice for operators of SAVs.

Optimal allocation of shared parking spaces for hospital parkers considering parking choice behavior under bounded rationality

ABSTRACT Lack of parking spaces at downtown hospitals causes urban problems like patient access delays and additional carbon emissions. This paper proposes a cumulative prospect theory-based shared parking space allocation model (CPT-SPSA model) to alleviate the parking difficulties. The CPT-SPSA model aims to maximize the profit of the shared parking platform under time window constraints considering the parking choice behavior of hospital parkers. To determine the parking choice behavior, cumulative prospect theory (CPT) is utilized to model parkers’ evaluation of parking lots considering individual heterogeneity. Numerical experiments indicate that the profit of the platform and the utilization rate of the shared parking spaces are improved significantly and more successful matches of shared parking supply and demand are achieved under the CPT-SPSA model. Those with different parking purposes vary in acceptance of each parking lot. The platform can earn more profit by reasonably adjusting the parking fees of the shared parking spaces.

Research on Parking Choice Behavior of Shared Autonomous Vehicle Services by Measuring Users’ Intention of Usage

Research on Parking Choice Behavior of Shared Autonomous Vehicle Services by Measuring Users’ Intention of Usage

Parking and Ride Induction Methods for Drivers in Commuting Scenes

Parking and ride is a very effective method to improve the traffic condition of commuter channels, and it is necessary to develop effective parking guidance strategies. In this study, considering the travel time, walking distance, parking cruise time, parking fee, and personal attributes of drivers, a probability model of parking and ride selection in commuter scenarios was proposed, and a dynamic price adjustment method based on the equilibrium of parking occupancy in the region was constructed. The parking price was adjusted by determining the target occupancy, thus affecting the parking choice behavior to guide the commuter to park. The example analysis showed that this method adjusted the selection probability of the parking lot by using the dynamic price adjustment method from the perspective of regional parking occupancy equilibrium, solved the model by symmetric duality algorithm and formulated a reasonable parking replacement induction scheme to achieve the goal of occupancy equilibrium. Compared with parking guidance under static pricing, it can avoid the crowding of commuter vehicles into the city center effectively to reduce the congestion of commuter channels.

Early Bird Scheme for Parking Management: How Does Parking Play a Role in the Morning Commute Problem

This paper extended the Vickrey’s point-queue model to study the early bird parking mechanism during morning commute peak hours. We not only investigated how commuters choose departure times in view of morning commute traffic congestion and the discounted early bird parking fee, but also analyzed the conditions which are determined for the existence of the user equilibrium in the analysis model provided in this paper. Moreover, the tendency of the total queuing time and the incremental parking pricing revenue was derived along with the different choice strategy between early bird parkers (ERPs) and regular parkers (RPs). The results showed that the number of commuters was jointly determined by the desired time and the bottleneck capacity for different schedules. Additionally, the method of fare incentive showed a better effect on reducing queue than the initial no-incentive method with the instantaneous travel demand. Most importantly, the incremental parking revenue can be increased by properly adjusting the parking pricing gap between ERPs and RPs. Our research not only provided several important propositions for the early bird parking mechanism but also included the optimal solutions for optimal parking pricing and schedule gap in two groups of parkers. This work is expected to promote the development of early bird parking to mitigate morning commute traffic congestion and motivate the related research of schedule coordination for regulating parking choice behavior in morning peak hours.

Shared Parking Behavioral Analysis Based on SEM

Shared parking has become a widely studied topic among researchers due to a rise in urban expansion. Over the past years, several studies have been conducted to investigate the relationship between the choices of shared parking spaces by urban residents and the different factors influencing these choices. This paper analyzes the different factors that can possibly affect parking choice behavior and builds a structural equation model to determine the most influencing attributes.

Analysis of shared parking demander’ choice behavior based on elaboration likelihood model

The shared parking mode is an effective measure to solve the problem of urban parking difficulties. However, due to insufficient research on the parking choice behavior and its influence factors, the shared parking mode has not been effectively promoted and applied. In order to study the parking choice behavior of the demander in the shared parking mode, this paper establishes a choice intention model of shared parking demander based on Elaboration Likelihood Model (ELM), and divides effect route in the process of promoting shared parking mode into central route and peripheral route. Firstly, we study the external influence factors that correspond to central route and peripheral route. And then we establish the connection between external influence factors and attribute factors of the demander by assuming 20 correlations. Finally the hypotheses in the model are verified by questionnaire investigation and statistical test. The research results indicate that the walking distance and parking time are significantly correlated with the travel purpose of the demander; the parking fee is significantly correlated with the income of the demander; the security risk factor is significantly correlated with the age of the demander. The research results of this paper have certain significance and reference value for platform operators and government departments to issue relevant incentive policies to promote the shared parking mode.

Car Parking User’s Behavior Using News Articles Mining Based Approach

Studying individual’s parking choice behavior can considerably contribute towards evidence-based policing in urban area. This study investigates evidence gathered by mining Finland news article API concerning car parking associated topics in order to comprehend user’s behavior and identify potential unforeseen circumstances that may impact users’ decisions and preferences. The study follows a natural language processing research pipeline, emphasizing word co-occurrence analysis, sentiment score and named-entity monitoring. The results can be exploited by local authorities to develop further evidence based policing in city urban planning.

Analysis of Taste Heterogeneity in Commuters’ Travel Decisions Using Joint Parking– and Mode–Choice Model: A Case From Urban India

The concept of transportation demand management (TDM) upholds the development of sustainable mobility through the triumph of optimally balanced transport modal share in cities. The modal split management directly reflects on TDM of each transport subsystem, including parking. In developing countries, the policy-makers have largely focused on supply-side measures, yet demand-side measures have remained unaddressed in policy implications. Ample literature is available presenting responses of TDM strategies, but most studies account mode choice and parking choice behaviour separately rather than considering trade-offs between them. Failing to do so may lead to biased model estimates and impropriety in policy implications. This paper seeks to fill this gap by admitting parking choice as an endogenous decision within the model of mode choice behaviour. This study integrates attitudinal factors and built-environment variables in addition to parking and travel attributes for developing comprehensive estimation results. A mixed logit model with random coefficients is estimated using hierarchical Bayes approach based on the Markov Chain Monte Carlo simulation method. The results reveal significant influence of mode/parking specific attitudes on commuters choice behaviour in addition to the built-environment factors and mode/parking related attributes. It is identified that considerable shift is occurring between parking-types in preference to switching travel mode with hypothetical changes in parking attributes. Besides, study investigates the heterogeneity in the willingness-to-pay through a follow-up regression model, which provides important insights for identifying possible sources of this heterogeneity among respondents. The study provides remarkable results which may be beneficial to planning authorities for improving TDM strategies especially in developing countries.