Energy management for hybrid building with grid-connected shared DC parking equipped with PV-energy storage system

Shared e-scooter parking regulation: Effects on rider attitudes, perceptions, and use

To address the issue of inefficient parking space utilization resulting from temporal and spatial disparities in parking demand, shared parking emerges as an innovative traffic management paradigm. The core concept of shared parking lies in the strategic allocation of parking spaces. By analyzing the usage patterns of parking areas, the relationship between supply and demand over time can be represented in a binomial form. An integer programming model for parking allocation is formulated, where the objective at each allocation stage is to minimize walking distance and parking costs while maximizing the degree of temporal alignment with overall parking demand in the region. The weights of these three indices are calculated using the entropy weight method, and subsequently, the parking cost matrix is derived through linear weighting. It is proposed to utilize the Hungarian Algorithm as a method for solving the assignment problem to obtain the allocation scheme that minimizes total parking costs. A Python program is developed to execute the phased optimal allocation of parking spaces. At the conclusion of the operation, the results for overall parking demand satisfaction, parking space utilization, and parking costs are generated. The model is applied to parking space management in both real-world instances and simulation experiments. The results demonstrate that the model not only ensures a higher utilization rate of parking spaces but also effectively reduces overall parking costs and the idle rate of parking time compared to the current parking methods. This model is well-suited for the parking allocation processes of smart parking platforms.

Implementing shared micromobility parking: Planning and engineering best practices from around the globe

This study analyzes the effectiveness of a design-led intervention in solving the parking issues of shared Personal Mobility (PM) devices and examines its impact on user behavior. The research methodology involved conducting surveys with users of shared PM devices, performing direct observations in disordered parking zones within the campus, and applying design interventions using feedforward and affordance to monitor and evaluate changes in user behavior. The findings confirm that clearly marked parking zones and intuitive visual elements are effective in changing user behavior, and that subjective norms and feedforward play a crucial role in guiding proper actions. In particular, the visual visibility of parking zones was experimentally proven to be effective in promoting correct parking behaviors. However, physical limitations of parking spaces and time constraints may induce noncompliant behaviors, suggesting the need for flexible design approaches and complementary policy measures. This study provides policy and design recommendations for addressing shared PM parking issues and is expected to serve as foundational research for the development of sustainable urban mobility systems.

Abstract Shared parking effectively optimizes urban parking resources while making full use of private parking spaces and satisfying the growing demand for parking in large cities. However, some owners are unwilling to share private parking spaces and oppose the community in conducting shared parking projects. To promote the sharing of private parking spaces, we use a complex network evolutionary game method to depict the impact of owners’ unfavorable relationships on individuals’ decision processes in real time and explore the impact of management mechanisms on owners’ willingness to share. The results demonstrate that experience-weighted attraction learning strategies that focus on experiential learning and adaptability are more conducive to promoting owner cooperation, whereas neighbor-avoidance conflict costs resulting from interactions between owners restrict cooperative behavior, and a higher number of network owners is detrimental to cooperation. A platform that improves the rejection rate of parking requests and overtime inconvenience cost is conducive to cooperation, but overtime probability and time window conflict cost reduce owners’ willingness to share. The government can lessen these adverse effects by adding compensation to all owners and increasing the public opinion adjustment coefficient to promote cooperative behavior and increase the number of shared parking spaces.

Many cities seek solutions to address public concerns about non-compliant shared electric scooter (e-scooter) parking. One strategy is to provide designated parking areas, called “corrals.” However, it remains unclear how much parking infrastructure is needed to improve compliance, or how this varies by land use. To address this gap, our study investigates: How dense does a network of dedicated shared micromobility parking need to be to increase compliance? How large should parking corrals be to meet demand? And how do these relationships vary by land use? We used e-scooter and built environment data in 12 cities worldwide and conducted descriptive, regression, and non-linear analyses. Results reveal that providing at least 20-30 parking corrals per square kilometer (about 50-80 per square mile or a one-minute walk in gridded areas) dramatically improves parking compliance. The spatial distribution of corrals is particularly important in areas with low corral density, where providing uniform coverage can significantly reduce parking non-compliance rates. Land-use intensity variables are non-linearly associated with parking non-compliance but suggest that parking corrals should have greater capacity in areas with more leisure, mixed-use, office, transit, and tourism destinations compared to places with more commercial or residential destinations. These findings offer direct policy recommendations to improve parking compliance and better match demand.

The pollution and congestion caused by the shortage of parking spaces are threatening the sustainable development of cities. Smart parking platforms are one of the major tools to solve the problem by providing the efficient usage of parking resources. However, current platforms can only realize limited functions, and shared parking is far from being implemented on a large scale. Since off-street parking provides the majority of potential shared parking spaces, this paper takes periodic off-street parking lots as the starting point for opening the shared parking market. Based on data from the Ningbo Yongcheng parking platform, power spectral density (PSD) and the autocorrelation function (ACF) are used to identify periodic parking lots. A Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based method is applied to clustering the occupancy time series. Land use, user type, parking duration, and parking patterns are then analyzed to study shared parking supply characteristics. The results show that (1) 31.3% of off-street parking lots are periodic parking lots, and 90.3% of them have regular users exceeding 50%. (2) Periodic parking lots are classified into four types. Most parking lots show convex flat peak, double peak, or triple peak characteristics. (3) The shared parking spaces demonstrate spatial and temporal imbalances. But in a small area, even considering the concentration of land use and the peak period, there are still enough spaces available. The above research is of significance for the large-scale implementation of shared parking, which can promote the sustainable development of a city.

In urban areas, searching for parking and electric vehicle (EV) charging can result in cruising, congestion, and environmental externalities. Recognizing the business opportunity of offering private parking and charging infrastructure access within multi-unit dwellings (MUDs) during daytime, we model a shared parking and EV charging management system. We maximize the revenue of MUD charging hubs in mixed land use, catering to public demand. Our approach accounts for the objectives of the two stakeholders involved: a demand model is fitted on the choices of EV charging users, and the supply model optimizes the allocation of parking and charging requests in an MUD parking lot. A binary integer linear programming model for the allocation of parking and charging spaces with a rolling horizon is integrated with matching rules that handle both parking and charging requests. In our numerical experiments in a neighborhood of Chicago, Illinois, we estimate the performance of the MUD parking and charging system with metrics that include revenue, number of matchings, and utilization rates. At any given time, MUDs with lower prices attract more charging requests, particularly those of longer duration, resulting in higher revenue and greater charging utilization. Dynamic pricing facilitates a more equitable distribution of requests; as MUD parking lots reach capacity and their fees increase, other MUDs become more competitive, attracting additional requests. Comparing our method against first-come-first-served and optimal-solution benchmarks, we demonstrate our model’s effectiveness in dynamically managing mixed parking and charging demand in MUD charging hubs.

With the continuous increase in the number of vehicles worldwide, parking challenges have become more severe, making it a shared goal for governments to alleviate parking difficulties in urban centers. Shared parking has emerged as an effective solution to address parking problems and has been widely studied in recent years. However, existing research primarily focuses on static or single-period parking matching, often neglecting the conflicts between overdue parking users and subsequent users. Therefore, addressing the impact of overdue parking on shared parking systems is highly important. This study proposes a multi-period dynamic matching decision model (MDMD), which divides the operation period of the shared parking platform into multiple decision points. At each decision point, parking demands are classified into four categories: newly arriving demands, allocated demands with a start time not within the current decision point, overdue demands during the current decision point, and demands affected by overdue parking. Three decision variables are established to determine matching schemes for the first, second, and fourth types of parking demands, facilitating a dynamic decision-making process that effectively mitigates the impact of overdue parking. A corresponding algorithm is designed to solve the model. Since the single-period model is a linear programming model, the CPLEX solver obtains allocation schemes for each decision point. These schemes, along with new parking demands, are used as input for the next decision point, achieving a dynamic matching process. Simulation experiments are conducted to compare the MDMD model with the traditional First-Book-First-Served (FBFS) model based on platform revenue, parking space utilization, and parking demand acceptance rate. The experimental results show that, compared to FBFS, MDMD improves long-term earnings by 83%, actual profits in recent profits by 6.6%, and parking space utilization by 8% while maintaining a similar parking demand acceptance rate. To validate the robustness of the model, additional simulations are performed under various overdue probability scenarios, demonstrating that MDMD maintains stable system performance across different probabilities. These improvements highlight the advantages of the dynamic matching strategy, distinguishing this study from existing methods lacking adaptability. These findings provide valuable insights for the optimization of shared parking systems, contributing to sustainable transportation solutions and efficient urban mobility management.

This paper studies how to set the location of park-and-ride and the pricing of shared parking spaces to achieve the desired goals in the context of autonomous vehicles (AV). Assuming that a linear city does not allow single-occupancy private cars to enter the city center, travelers can only choose to park and ride or take a shared autonomous vehicle to reach the city center. Under the condition of autonomous driving, since the car can park autonomously, there are three ways to park: park the car home, park the car in a free parking space near the transfer point, and park the car in a shared parking space provided by residents near the transfer point. Starting from the two goals of profit maximization and social cost minimization, this paper explores the optimal parameter settings under the two goals respectively. The results show that the optimal park-and-ride point and the optimal shared parking space pricing under the two goals are both obtained when the supply of shared parking spaces is equal to the demand. This study can provide theoretical reference and decision-making basis for the optimal allocation of resources in the era of autonomous driving.

ABSTRACT Shared parking plays a crucial role in alleviating parking pressure, but the heterogeneity of potential suppliers’ intentions was often ignored. This study addresses this gap by adopting an interpretable Machine Learning (ML) framework to investigate parking space sharing intentions, considering individual differences. A survey with 383 respondents from mainland China was conducted, and a Latent Class Model (LCM) identified three distinct groups of potential suppliers. The Light Gradient Boosting Machine (LightGBM), outperforming other ML models, was used to quantify factors influencing sharing behaviors. The SHapley Additive exPlanation (SHAP) approach revealed that influential factors vary across different latent classes. These findings provide insights for shared parking operators to encourage potential suppliers’ participation in shared parking.

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.

Shared parking enables private parking owners to share their parking spaces during idle time. In recent years, increasingly more research investigated people’s intentions to participate in shared parking schemes as well as optimization algorithms to match shared parking supplies and demands. However, little research has investigated the distribution of shared parking use in time and space in implementation. To fill this gap, this study uses the transaction records of 121 shared parking lots in Guangzhou, China, and applies a quasi-Poisson regression model to analyze the influence of a set of explanatory factors on the total number of transactions. The results show that the number of parking transactions is significantly influenced by implemented duration, parking lot capacity, land use of shared facility, number of POIs (point of interest), and transit stations within a range of 750 meters from the shared parking lot. This study also applies a linear regression model to analyze the effect of a set of explanatory variables on the average parking duration at shared parking lots. The results show that the average parking duration is significantly influenced by the land use of a shared facility, number of office buildings within 750 meters from the shared parking lot, and peak time of the shared parking lots.

In this study, a tripartite decision-making parking pricing model was developed based on Game Theory to comprehensively reflect the impact of parking pricing on private car travelers, parking lot operators, and traffic managers. Utility theory is introduced to analyze the behavioral characteristics of the tripartite participants in parking pricing. A parking behavior model for private car travelers, an operating profit model for parking lot operators, and a social negative utility model for traffic managers are established. This article presents an analysis of the mutual influence between them based on a game theory perspective and introduces parking saturation and road saturation as new factors influencing parking pricing to address the interactive relationship among the tripartite participants. A parking pricing model based on tripartite games is established, and a solution algorithm is designed. The results indicate that when the parking fee rates for the two public parking lots in the scenario are 8.5 CNY/h and 9 CNY/h, respectively, the parking demand is 300, and the sum of the total travel costs of private car travelers and the total operating profits are CNY 20,589 and 2187.9, respectively. The parking saturation of the public parking lot and the difference between the expected value is minimized to 0.25, and the road saturation and the difference between the expected value are minimized to 1.48, which is the parking pricing plan that minimizes the conflicts of interest among the tripartite stakeholders in the tripartite game. The parking pricing model of a public parking lot provides a reference for formulating parking fee strategies that comprehensively reflect the needs of the three parties involved in the public parking lot.

In recent years, the problem of "parking difficulty" has led to a large number of illegal parking incidents. The shared parking mode has been considered as an effective way to alleviate the conflict between parking supply and demand and urban traffic pressure. To address the issue of illegal parking and promote the development of shared parking, this paper constructs an evolutionary game model with shared platforms and motor vehicle drivers as the main entities. The study investigates the evolutionary stability strategy of the model, conducts sensitivity analysis on model parameters, and further analyzes the impact of highly sensitive parameters on the evolutionary paths of both players in the game. Finally, numerical simulations are performed on dynamic parking pricing standard. The research findings demonstrate that the sensitivity of discounts received by drivers from shared platforms and the additional revenue gained by the shared platform is higher than that of other parameters. Moderately increasing the penalty for illegal parking and the additional revenue of the shared platform can encourage drivers to choose legal parking and promote the development of shared parking. Under given parameterized and periodic parking pricing standard, finally, according to the particle swarm optimization algorithm, a set of relatively optimal parameter values is derived to enable the model to evolve rapidly into a stable state where drivers choose to park legally and the shared platform selects surrounding parking lots. It can effectively reduce the frequency of illegal parking.

An adjustment method for the initial matching scheme between private idle parking spaces and demanders is proposed in this paper. We consider a shared parking platform to determine the initial matching scheme. However, the demanders may cancel reservations due to the change of destinations or parking time before the matching scheme is executed. Meanwhile, new demanders make reservations. We develop a multi-objective optimization model satisfies adjustment principles to obtain the adjusted matching scheme that mitigates the negative impacts of cancellation reservation. The objective is to maximize the satisfaction of demanders, the satisfaction of private idle parking spaces, and the platform profit. The improved nondominated sorting genetic algorithm II (INSGA-II) is then developed to solve the model and determine the adjusted matching scheme. Our results suggest that the performance of the adjusted matching scheme outperforms that of non-adjusted matching scheme for various experimental sizes.

There has been widespread and growing concern about parking. This paper attempts to provide decision support for a shared parking system to reduce parking difficulty. We study a many-to-many matching problem between shared private idle parking spaces and their demanders. A novelty is that the demanders are allowed to use different parking spaces successively in parking relocation service support. This can further reintegrate the idle time of the parking spaces and improve their utilization rate. A multi-objective optimization model is constructed to maximize the number of matched demanders, the total priority of the parking spaces, and the total priority of the demanders. More importantly, the priorities of the parking spaces and the demanders are innovatively considered. Each of the parking spaces and the demanders is given a priority for the matching and the priority of a parking space or a demander will be increased if the parking space or demander rarely gets matched successfully. This helps reduce the withdrawal of parking spaces and the demanders from the parking platform. In addition, an NSGA-II algorithm is designed to solve the model efficiently. Finally, the feasibility of the proposed method is illustrated via an example.

A bi-level programming model is formulated to determine a government subsidy scheme for shared parking management. The government, as the upper-level decision-maker, seeks to maximize the social benefit by providing subsidy to the shared parking platform, incentivizing the platform to rent more shared parking slots, which can increase the utilization rate of idle parking slots and reduce the curbside parking cruising time of parking demanders. At the lower-level formulation, the shared parking platform, as a reseller, not only matches shared parking slot demanders but also determines which shared parking slots to rent, which is approached by a two-sided decision-making process. A “one-to-many” matching principle is adopted for the platform to maximize its revenue. A modified genetic algorithm is designed to solve the proposed model. Results indicate that the government subsidy has a positive impact on stimulating the shared parking market. Specifically, from the perspective of the government, the subsidy can increase the number of matched shared parking demanders, improve the supply of parking slots, and reduce curbside parking cruising time. From the perspective of the shared parking platform, the subsidy can lead to higher revenue.

Abstract The imbalance between supply and demand in urban settings poses a significant barrier to the sustainable advancement of urban transportation. Shared parking serves as a viable solution to mitigate these challenges. Nevertheless, for its sustained growth, a regulatory mechanism enforced by the government is imperative. To promote shared parking market diffusion, we construct an evolutionary game model that incorporates the government, enterprises and parking demanders. It explores stabilization strategies for these stakeholders and identifies multiple equilibrium states under different parameter conditions. The results show that the rate and stability of these evolutionary strategies are constrained by the mutual benefits derived by the three parties. Furthermore, such stakeholders are reciprocally influenced by their willingness to engage in shared parking to varying degrees. Government subsidies serve as a determining factor for the strategic choices made by both enterprises and demanders, albeit at different evolutionary rates. Demanders who have a higher value of time tend to park on-street, thereby influencing enterprise strategies. To foster the long-term growth of the shared parking market, the government must enact appropriate subsidy policies, maintain consistent regulations and advocate for increased subsidies for parking demanders to reduce the effect of temporal heterogeneity on parking behavioural choices.