Environmental and Social Benefits of Urban Parking Space Shortages Mitigation Management Model: A System Dynamics and Nudge Approach

Abstract

With the growth of the urban population and economic level, the issue of urban parking space shortages (UPSSs) has assumed growing prominence. This persistent issue not only exacerbates traffic congestion but also contributes to environmental pollution, highlighting the need for system-oriented mitigation strategies. First, an algorithm for mitigating UPSSs based on nudge theory was constructed, in order to determine how the nudge strategies work. Second, nudge tools, including gain disclosure, salience, and outcome notification, were integrated to construct a mitigation model for UPSSs, which synthesizes nudge theory, the model of self-regulatory processes involved in behavioral change, and system dynamics (NT-SPBC-SD theory). Finally, four scenarios of natural development, guide adjustment, balanced regulation, and enhanced change were simulated. The findings of this study are as follows: (1) The UPSS mitigation had multiple overlapping effects and critical point effects, and the nudge strategy gradually decayed or even rebounded over time. (2) Under the enhanced change scenario, the degree of UPSSs, the amount of illegal parking, and CO₂ emissions from civil vehicles decreased by 21.2%, 6.93%, and 14.54%, respectively. (3) After quantitative comparisons, the balanced regulation scenario with lower implementation costs instead demonstrated superior overall performance. The results support subsequent research and guide the enhancement of urban parking management policies to advance urban sustainability.

1. Introduction

The Global Mobility Report 2022 released by Sustainable Mobility for All (SuM4All) delves into the concept of sustainable mobility, emphasizing the necessity of reducing reliance on private vehicles to address urban challenges, such as parking shortages. Given the limited urban space, the contradiction between the supply and demand for parking spaces has gradually intensified, particularly in large and medium-sized cities, where the shortage of parking spaces has emerged as a critical challenge in urban management that requires urgent attention [1]. Scholars have done more research related to parking management in urban transportation, specifically including the supply and demand of urban parking spaces, the technical management of urban parking problems, and the policy management of the urban parking dilemma.

In terms of urban parking space supply (SPS) and demand (DPS), the SPS is divided into basic parking supply and travel parking supply [2]. The SPS is non-storable and non-transportable [3]. However, the spatial and temporal distribution of DPS is uneven, leading to an oversupply in some areas and severe shortages in others. This imbalance makes it difficult to spatially regulate SPS. Several researchers have suggested that the imbalance between the SPS and DPS is the primary conflict in parking space management [4]. DPS is influenced by multiple factors, including transportation policies [5] and residents’ employment levels [6]. Some scholars believe that the influencing factors of parking demand intensity include the nature of land development, the level of economic development, the level of transportation facility supply, and the level of attracting vehicle trips [7].

In terms of technological management of the UPSS, researchers have developed an IoT-assisted smart parking system based on a cloud platform [8]. IoT-based methods lock the parking spaces and allow vehicles to enter according to the queue number. In this way, online queuing and booking are realized to effectively improve the queuing in front of the parking lot [9]. After an in-depth study of the shared parking system, researchers have found that there is a significant positive correlation between drivers’ willingness to use shared parking spaces and the convenience of parking, the length of parking time, and the frequency of car use [10]. This suggests that improving the convenience and service level of parking is able to promote the utilization rate of shared parking spaces. In addition, based on a double auction model, it establishes a bidding mechanism for shared parking space participants and effectively mitigates user default issues [11].

In terms of policy governance of the UPSS, scholars address urban parking problems from the perspective of administrative intervention. Scholars have found that parking permit systems and public transport subsidies can regulate vehicle trips and alleviate traffic congestion [12,13]. In their study of the influencing factors of parking price, researchers have found that excessively high pricing can lead to an increase in supply and crowd out some demand. Moreover, implementing a public transport priority policy helps optimize travel modes, thereby reducing parking demand [14]. Therefore, setting parking pricing standards requires careful consideration of the price level. Gradually reducing parking incentives can encourage more efficient parking behavior and promote the choice of public transportation [15]. Notably, scholars have explored the contribution of parking charges to reducing energy consumption and CO₂ emissions [16]. It was found that parking fees can reduce cruising behavior, thereby significantly contributing to fuel savings and reducing pollutant emissions. However, there is a phenomenon that poor air quality drives consumers to purchase more plug-in hybrid electric (PHEV) and electric (EV) vehicles [17], leading to an increase in vehicle ownership and exacerbating UPSSs.

In terms of the nudge strategies within the transportation field, scholars have employed various nudge tools to conduct behavioral experiments. In several studies, researchers have utilized social-norm-based nudge strategy to influence travelers’ decisions and mitigate specific transportation issues [18,19,20]. Other studies have focused on the impact of information disclosure [18], finding that presenting relevant travel information can significantly shape individual travel behavior. Additionally, experiments have examined the role of reminders in guiding the behavior of car-sharing users [21], with encouraging results. Notably, some scholars have integrated principles from behavioral economics and goal-framing theory to explore the broader effects of different nudge strategies. Their findings suggest that all nudge interventions generally have a positive influence on the likelihood of choosing public transportation. Among these, gain disclosure—those that emphasize health benefits—demonstrated the most pronounced effect [22].

The above research results have important reference value for the study of UPSS mitigation. While previous studies have examined urban parking using system dynamics or behavioral nudges separately, few have combined these approaches into an integrated mitigation management model. Moreover, existing research rarely addresses the impact of psychological factors on traffic patterns. To this end, the operational modes of nudge strategies are first analyzed, and an algorithm to mitigate UPSSs is developed, grounded in nudge theory. Secondly, building upon existing research, innovative governance strategies centered around nudge theory are proposed in this paper. Three nudge tools (gain disclosure, salience, and outcome notification) are introduced. Utilizing system dynamics modeling, the dynamic changes and governance pathways for addressing UPSS are explored from a system perspective. Finally, based on the performance of UPSS mitigation across various scenarios, the strategy with a relatively optimal comprehensive performance is identified. Additionally, latent effects and the mechanisms that have not been thoroughly explored in existing literature are uncovered, and corresponding policy recommendations are formulated. The possible contributions of this study are as follows: (1) Combining nudge theory (NT), the model of self-regulatory processes involved in behavioral change (SPBC), and system dynamics (SD), we propose a UPSS mitigation algorithm based on NT-SPBC-SD theory. (2) The multiple overlapping effects, critical point effect, and paradoxical effect of nudge strategy on UPSS mitigation are revealed through dynamic simulation. Based on this, corresponding optimization suggestions are proposed.

2. Methods

System dynamics is a quantitative research method used to analyze complex social and economic systems. It is based on feedback control theory and computer simulation techniques [23]. SD has been extensively applied in various fields, including transportation emission reduction [24], ecosystem management [25], and sustainable development [26]. The study of UPSSs is a complex system problem involving environmental, social, and energy aspects, and the SD theory provides support for this paper. Nudge theory aims to influence individuals’ decisions in a specific direction while preserving their freedom of choice. It is characterized by simplicity, low cost, and a non-confrontational nature [27]. There are various tools of nudge, such as salience, disclosure, reminder, outcome notification, and default option [28]. SPBC explains how behavioral interventions affect goal-directed thinking via automatic associative activation systems, both of which influence behavior independently. The automatic associative activation system consists of the impulse subsystem and the habit subsystem [29]. NT and SPBC are significant theories in behavioral economics research and provide a theoretical basis for exploring the mechanism of NT to mitigate UPSSs.

Within the integrated framework, SD defines the system boundary and simulates dynamic responses, SPBC explains the behavioral change process, and NT provides psychologically grounded policy levers to influence public behavior. By combining these approaches, the model creates a closed feedback loop that captures the complete behavioral response process: behavioral intervention–psychological change–system response–behavioral feedback. This loop allows for a more accurate and actionable understanding of how policy measures translate into real-world behavioral and system-level outcomes.

2.1. Analysis of Parking Decision Behavior Based on NT-SPBC-SD Theory

Figure 1 shows the UPSS mitigation algorithm based on NT-SPBC-SD theory, and the detailed process was analyzed as follows.

Step 1: Introduce nudge strategies.

Nudge strategies (η) were introduced to optimize the parking turnover rate and public travel behavior. Three nudge tools were selected: gain disclosure, salience, and outcome notification. Gain disclosure refers to the practice of visually informing the public about the potential benefits and costs associated with travel and parking behaviors, while enhancing their awareness of the limited availability of parking resources. It is aimed at altering their parking and travel behavior choices. Salience involves increasing the public’s attention to key information through visual, verbal, or other forms. Outcome notification indicates communicating to the public the potential impacts or consequences of their choices, thereby changing their subconscious perceptions of certain behaviors [28].

Gain disclosure highlights the personal and societal benefits of using public transport and complying with parking regulations, which motivates individuals to adopt more efficient travel behaviors. Salience draws attention to key information, such as limited parking durations or nearby transit options, helping to disrupt habitual car use. Outcome notification provides timely feedback—for example, on reduced congestion or improved parking availability—reinforcing behavior change and supporting long-term adaptation. These tools are well-suited to the NT-SPBC-SD theory and have practical value in shaping more sustainable urban mobility choices. These nudge tools act through an automatic associative activation system in the behavioral decision process [30]. In the model, they act through the influence coefficient ${\omega }_i$, and $0<{\omega }_i\le 1$, which can be determined by calculating the findings of Reference [22] and satisfying ${\displaystyle \sum _{i=1}^3{\omega }_i=1}$. Therefore,

$$\eta ={\omega }_1\times \mathrm{Gain} \mathrm{Disclosure}+{\omega }_2\times \mathrm{Salience}+{\omega }_3\times \mathrm{Outcome} \mathrm{Notification}.$$

(1)

Step 2: Introduce the level of automatic associative activation.

Based on the SPBC, the level of automatic associative activation was introduced. This level measures the extent to which nudge strategies trigger public selection behavior. The higher the intensity of η, the greater the level of automatic associative activation (A), indicating a positive correlation between the two. Consequently, the relationship can be expressed as A = 1 − exp(−k·η), where k is an arbitrary real number serving as a sensitivity parameter for the model, controlling the rate of change in the activation level. When η is relatively low, A increases gradually. As η intensifies, A approaches saturation.

Step 3: Introduce the level of goal-directed thinking.

The level of goal-directed thinking (B) was introduced based on SPBC to measure the effect of nudge strategy. The higher the level of automatic associative activation (A), the lower the public’s ability to engage in deep thinking about systemic goals, indicating a negative correlation between the two. Therefore, let B = 1 − A. When A is excessively high, the public may rely on intuitive decisions, whereas when A is relatively low, the public may tend to engage in deep thinking. Within the NT-SPBC-SD theory, this means that as B increases, the effectiveness of nudge strategies tends to decline, leading to weaker behavioral change and lower improvements in parking space turnover rates.

Step 4: Calculate the parking turnover rate (PTR).

The PTR refers to the average number of vehicles parked in each parking space within a certain period, reflecting the spatial utilization efficiency of parking facilities. It measures the efficiency of parking resource use and is influenced by a combination of η, A, and B. By incorporating the SPBC theory to determine the mode of action of nudge strategies, it is observed that a higher intensity of η implementation leads to a higher A, which in turn results in a higher parking turnover rate. However, when B is high, the increase in PTR may be somewhat constrained. Let the graphical function of PTR in the city’s natural state be denoted as δ, with i representing the adjustment coefficient for nudge effectiveness, and $m,n\in R$, representing the adjustment coefficients for factors A and B. Therefore,

$$\mathrm{Parking} \mathrm{Turnover} \mathrm{Rate} \left(\mathrm{PTR}\right)=\delta +\ln \left(1+n\cdot A\right)+{\displaystyle \frac{1}{1+e^{m\cdot B}}}+i.$$

(2)

Step 5: Calculate the propensity of public transportation travel choice (PPTTC).

The PPTTC positively correlates with nudge strategies. The higher the degree of automatic associative activation is, the more likely the public is to be induced to opt for public transportation. Enhancing the attractiveness of public transportation can mitigate parking pressure while optimizing urban transportation resources. Similarly, the manner in which nudge strategies operate is determined by integrating the SPBC theory. Let i represent the moderation coefficient of the nudge effect. Letters g and h denote the adjustment coefficients for A and B ($g,h\in R$). Then, the calculation of PPTTC can be derived as shown in Equation (3). To test the rationality of the model parameters and equations, a reality test was conducted on PTR and PPTTC, assessing the degree of agreement between simulated and actual conditions. Following this test, if satisfactory, proceed to the calculation in Step 6. Otherwise, revert to Step 1.

$$\mathrm{Propensity} \mathrm{of} \mathrm{Public} \mathrm{Transportation} \mathrm{Travel} \mathrm{Choice} \left(\mathrm{PPTTC}\right)=\ln \left(1+g\cdot A\right)+{\displaystyle \frac{1}{1+e^{h\cdot B}}}+i.$$

(3)

Step 6: Calculate the degree of parking convenience.

The measure of the degree of parking convenience (Γ) reflects the public’s perception of the ease or difficulty of parking, influenced by PTR and φ. Higher values of PTR and φ indicate a higher degree of parking convenience. Based on Reference [31] with adjustments, the formula for calculating the degree of parking convenience is Γ = 1/1 + exp(α × (1 − PTR × φ)). In this formula, $0<\phi \le 1$ and α represent tolerance parameters related to the parkers’ perception of parking ease or difficulty.

Step 7: Calculate the degree of parking space demand.

The degree of urban rail transit impact (E) and PPTTC are negatively correlated with the public’s demand for parking spaces. Based on the graphical function of parking space demand (θ), the degree of parking space demand (D) can be expressed as D = θ × (1 − E) ×(1 − t × ln(1 + PPTTC)), where t is the adjustment parameter for the impact of public transportation choice on public parking demand ($t\in R$).

Step 8: Calculate the degree of parking space shortages.

The degree of parking space shortages (P) is affected by the comprehensive impact of E and Γ. The P is positively correlated with the parking demand but negatively correlated with parking convenience. Let λ be the adjustment coefficient of parking space shortages, $0<\lambda \le 1$. Then,

$$P=D\times \left(1-\lambda \times \mathrm{\Gamma }\right)=\theta \times \left(1-E\right)\times \left(1-t\times \ln \left(1+\mathrm{PPTTC}\right)\right)\times \left(1-\lambda \times {\displaystyle \frac{1}{1+e^{\alpha \left(1-\mathrm{PTR}\cdot \phi \right)}}}\right)$$

(4)

Next, the model is tested again. If it passes the test, then output the results. If it fails the test, then return to step 6. The algorithm integrates the nudge theory and behavioral economics model. Through multi-level calculations, the impact of the nudge strategy on PTR and PPTTC is analyzed. Finally, the degree of parking space shortages is affected by parking convenience and parking demand. This approach addresses limitations of nudge theory in calculating PTR and public transport travel behavior and provides both theoretical and computational support for future practical applications.

2.2. Causal Analysis

By determining the interactions between various variables, a causal loop diagram of the formation mechanism of the urban parking space shortages dilemma was constructed (Figure 2). To analyze the formation mechanism of UPSSs and the role of various policies in mitigating UPSSs, six feedback loops were constructed in this study.

Loop 1: Degree of parking space shortages → + Rate of vehicles illegally parking → + Degree of traffic congestion → + Rate of traffic accidents → + Strength of governance → + Transportation investment → + Level of public transportation services → Number of vehicle trips → + CO₂ Emissions from civil vehicles → + Degree of air pollution → - Attractiveness degree of the city → + Urban population → + Demand for land → + Degree of land development → - Available land → - Difficulty of parking lot construction → - Supply of parking spaces → -Degree of parking space shortages.

Loop 2: Degree of parking space shortages → + Rate of vehicles illegally parking → + Degree of traffic congestion → + Rate of traffic accidents → + Strength of governance → + Driving restriction policy → + Demand for car purchases → + Number of vehicles → + Demand for parking spaces → + Degree of parking space shortages.

Loop 3: Degree of parking space shortages → + Rate of vehicles illegally parking → + Degree of traffic congestion → + Rate of traffic accidents → + Strength of governance → + Transportation investment → + Level of parking facilities → + Parking efficiency → + Parking turnover rate → - Degree of parking space shortages.

Loop 4: Degree of parking space shortages → + Rate of vehicles illegally parking → + Degree of traffic congestion → + Rate of traffic accidents → + Strength of governance → + Parking fee policy → + Travel cost → - Number of vehicle trips → - Parking efficiency → + Parking turnover rate → - Degree of parking space shortages.

Loop 5: Degree of parking space shortages → + Rate of vehicles illegally parking → + Degree of traffic congestion → + Rate of traffic accidents → + Strength of governance → + Shared parking policy → - Degree of parking space privatization → - Rate of parking space utilization → - Degree of parking space shortages.

Loop 6: Degree of parking space shortages → + Rate of vehicles illegally parking → + Degree of traffic congestion → + Rate of traffic accidents → + Strength of governance → + Driving restriction policy → + Demand for car purchases → + Number of vehicles → + Number of vehicle trips → + CO₂ Emissions from civil vehicles → + Degree of air pollution → - Attractiveness degree of the city → + Urban population → + Demand for land → + Degree of land development → - Available land → - Difficulty of parking lot construction → - Supply of parking spaces → - Degree of parking space shortages.

Loop 1 shows that as parking spaces become increasingly scarce, the phenomenon of vehicles parking indiscriminately increases, which in turn exacerbates traffic congestion and accident rates. In response to these problems, the government has stepped up governance efforts and promoted transportation investment to improve the level of public transportation services, aiming to reduce the number of vehicle trips. However, although the reduction in the number of vehicle trips can help reduce CO₂ emissions and air pollution, it may increase the population and land demand due to the increase in urban attractiveness. These will increase the degree of land development, reduce available land, and increase the difficulty of parking lot construction, leading to insufficient parking space supply, and ultimately exacerbating parking space shortages again. Loop 1 is a positive feedback loop. After a series of cyclical effects, the degree of parking space shortages is exacerbated.

Loop 2 is also a positive feedback loop. Due to the increasing shortage of parking spaces, illegal parking becomes more frequent, leading to an increase in the degree of traffic congestion and accident rates. This has prompted the government to strengthen the implementation of driving restriction policy to control vehicle use, but this has stimulated an increase in demand for car purchases, resulting in a growing number of vehicles. Therefore, the demand for parking spaces has further increased, and ultimately the shortage of parking spaces has increased. After the driving restriction policy was strengthened, considering the increase in the number of vehicles, this in turn increased the shortage of parking spaces, resulting in a paradoxical phenomenon.

Loops 3–5 are negative feedback loops. Specifically, when parking spaces become increasingly scarce, the incidence of illegal parking rises, leading to heightened traffic congestion and accident rates, prompting the government to enhance regulatory interventions. These measures include increasing investment in transportation infrastructure to improve parking facility capacity and efficiency (Loop 3), implementing parking fee policy to raise travel costs and reduce vehicle trips (Loop 4), and promoting shared parking to reduce privatization and increase utilization of parking spaces (Loop 5). Loops 3 and 4 improve parking turnover rates, while Loop 5 enhances parking utilization rates. These loops ultimately achieve the goal of mitigating parking space shortages. Initially, the increasing scarcity of parking spaces triggers a series of effects, which are subsequently suppressed.

Loop 6 is also a negative feedback loop. The escalating issues of parking space shortages and traffic congestion have prompted governments to implement driving restriction policy as a countermeasure. The policy, in turn, leads to an increase in vehicle ownership. However, recognizing that this surge could exacerbate air pollution, it results in a subsequent decline in the urban population. Consequently, the demand for land and the difficulty of constructing parking facilities are reduced. Finally, this increases the supply of parking spaces, thereby mitigating the degree of parking space shortages. By incorporating air pollution, this loop mitigates the degree of parking space shortages through a series of variables.

2.3. System Dynamics Model for Mitigating UPSSs Based on Nudge Theory

Based on the formation mechanism of UPSSs and the manner in which nudge strategies influence parking behavior decisions, a system dynamics model for mitigating UPSSs was established, grounded in nudge theory (Figure 3). The year 2016 marked the beginning of China’s 13th Five-Year Plan, a period that brought significant advancements in urban transportation [32]. The year 2030, being the target year for carbon peaking, holds special significance for urban transportation. Consequently, the simulation period was set from 2016 to 2030, with a time step of 1.

The hypotheses in the model are as follows:

Hypothesis 1.

Public transportation only includes bus, taxi, and urban rail transit. The civil vehicles involved in this study only include civil vehicles with new energy and fuel drive modes.

Hypothesis 2.

The model is based on data from Zhengzhou. In addition to the policies studied in the model, the possible impact of other policies in Zhengzhou on the model is not considered.

Hypothesis 3.

Pollution emissions from civil new energy vehicles only come from CO₂ emissions generated by thermal power generation, and civil new energy vehicles that consume other energy, such as hydrogen, are not considered.

The SD model of UPSS mitigation can be decomposed into the traffic subsystem, environmental subsystem, economic subsystem, and policy subsystem. The traffic subsystem is complex and includes factors such as infrastructure and transportation demand. This study mainly discusses the degree of parking space shortages and traffic congestion. In the environmental and economic subsystems, environmental performance and economic performance are, respectively, expressed by changes in civil vehicle CO₂ emissions and variations in parking fee revenue. In the policy subsystem, the model introduces three nudge tools to increase the parking turnover rate and public transportation travel preference, thereby improving parking convenience and reducing parking space demand, ultimately mitigating the problem of UPSSs.

The data mainly come from official data, including official data such as the Henan Statistical Yearbook (2016–2023) and Zhengzhou Statistical Yearbook (2016–2023), as well as existing reports and literature, such as the China Motor Vehicle Environmental Management Annual Report (2016–2018), China Mobile Source Environmental Management Annual Report (2019–2023), and Reference [33]. The main parameters and initial values in the model are shown in

Table 1. Main parameters and initial values (in 2016).

Variable	Value	Unit
GDP	8.1309 × 1011	yuan
Urban population	1.1194 × 107	person
Number of trips per capita	2.73	times/person
Number of parking spaces	1.192 × 106	space
Urban road area	5.1255 × 107	m2
Number of taxis	10,908	vehicle
Number of buses	6230	vehicle
Energy consumption of civil new energy vehicles	0.1595	kW·h/km
Energy consumption of civil fuel vehicles	0.1	L/km
Conversion factor of electricity CO2 emissions of civil new energy vehicles	0.00058	ton/kW·h
Conversion factor of gasoline CO2 emissions of civil fuel vehicles	0.002925	ton/L
Number of civil new energy vehicles	14,549	vehicle
Number of civil fuel vehicles	2.72416 × 106	vehicle

, while the key equations and sources are shown in

Table 2. Main equations and sources.

Variable	Unit	Equation	Source
Urban population	person	INTEG (Increase in urban population + Net inward migration − Number of deaths, 1.1194 × 107)	Zhengzhou Statistical Yearbook
Urban road area	m2	INTEG (Increase in urban road area, 5.1255 × 107)	Zhengzhou Statistical Yearbook
Level of road traffic tolerance	−	WITH LOOKUP (Average road area per vehicle, (((25, 0)–(200, 1)), (30, 0.05), (34.143, 0.1), (34.369, 0.12), (34.443, 0.13), (34.489, 0.14), (34.586, 0.15), (35.778, 0.2), (38.377, 0.25), (45.887, 0.35), (51.547, 0.45), (52.593, 0.5), (56.083, 0.6), (60, 0.65), (80, 0.8), (100, 0.9), (140, 0.95)))	Reference [33] + Amendments
Graphical function of civil fuel vehicle ownership growth rate	−	WITH LOOKUP (Time, (((2016, 0)–(2030, 1)), (2016, 0.1485), (2017, 0.1115), (2018, 0.109), (2019, 0.0626), (2020, 0.0851), (2021, 0.0584), (2022, 0.0972), (2023, 0.073), (2024, 0.0735), (2025, 0.0788), (2026, 0.0767), (2027, 0.0786), (2028, 0.0741), (2029, 0.077), (2030, 0.0767)))	Zhengzhou Statistical Yearbook + GM (1, 1) metabolic model [34]
Degree of parking convenience	−	1/(1 + EXP(1 × (1 − Rate of parking space utilization × Parking turnover rate)))	NT-SPBC-SD algorithm
Growth rate of civil new energy vehicle ownership	−	0.48 × Attractiveness of the growth in civil new energy vehicle ownership^3 + 0.52 × Graphical function of civil new energy vehicle ownership growth rate^2	Reference [33] + Amendments
Number of parking space requirements	space	(Number of civil new energy vehicles + Number of civil fuel vehicles) × 1.3	Reference [35]
Growth rate of urban road area	−	WITH LOOKUP (Time, (((2016, 0)–(2030, 1)), (2016, 0.135675), (2017, 0.0509543), (2018, 0.0293421), (2019, 0.0960775), (2020, 0.10171), (2021, 0.0366912), (2022, 0.0727215), (2023, 0.0740984), (2024, 0.0744387), (2025, 0.064661), (2026, 0.0656281), (2027, 0.0735039), (2028, 0.0683946), (2029, 0.0677855), (2030, 0.0679314)))	Zhengzhou Statistical Yearbook + GM (1, 1) metabolic model [34]
Degree of parking space demand	−	Graphical function of parking space demand × (1 − Level of road traffic tolerance) × (1 − 0.1 × LN (1 + Propensity of public transportation travel choice))	NT-SPBC-SD algorithm
Propensity of civil vehicle travel choice	−	0.6 × (1 + Level of road traffic tolerance) + 0.4 × (1 − Propensity of public transportation travel choice)	Reference [33] + Amendments

.

2.4. Model Validation

The GDP and number of parking spaces were used to test the historical validity of the model. Simulated data from 2016 to 2022 were compared with historical records. The two indicators reflect both macro-level urban development trends and the direct evolution of parking infrastructure. The validation results are shown in

Table 3. Model validation.

Time	GDP (yuan)			Number of Parking Spaces (Space)
	Actual Value	Simulation Value	Relative Error	Actual Value	Simulation Value	Relative Error
2016	8.1309 × 1011	8.1309 × 1011	0.00%	1.1920 × 106	1.1920 × 106	0.00%
2017	9.3017 × 1011	9.3913 × 1011	0.96%	1.2470 × 106	1.2466 × 106	0.03%
2018	1.0670 × 1012	1.0565 × 1012	0.98%	1.3000 × 106	1.3018 × 106	0.14%
2019	1.1586 × 1012	1.1303 × 1012	2.45%	1.3570 × 106	1.3615 × 106	0.33%
2020	1.1850 × 1012	1.1660 × 1012	1.61%	1.4190 × 106	1.4228 × 106	0.26%
2021	1.2538 × 1012	1.2222 × 1012	2.52%	1.4775 × 106	1.5138 × 106	2.46%
2022	1.2935 × 1012	1.2698 × 1012	1.83%	1.6083 × 106	1.6137 × 106	0.33%

. The relative error of the tested variables was within 5% each year. Therefore, the simulation effect of the model had certain rationality and effectiveness.

3. Results

3.1. Dynamic Evolution of Parking System Under Natural Development Scenario

Based on the average parking duration [36] and the parking fee standard in Zhengzhou, the actual parking fee was set at 6.17 yuan/time. A natural development scenario (baseline scenario) was set. The simulation results of the main variables are shown in Figure 4.

In Figure 4, the degree of parking space shortages exhibits a consistent upward trend over the years (curve 1). In particular, the shortage intensifies significantly after 2019, reflecting a growing imbalance between parking supply and demand. This upward trajectory is intimately linked to the surge in civil vehicle travel (curve 5 in Figure 4) and the acceleration of urbanization. This underscores the potential for the parking issue to worsen considerably in the absence of effective management strategies.

Regarding the degree of parking convenience (curve 2), it remained between 0.3 and 0.4 throughout the simulation period, with a slight overall increase. This stability, in conjunction with the escalating number of civil vehicle trips and the heightened parking scarcity, may further raise both time and financial costs incurred by drivers.

The number of vehicles illegally parking demonstrated a persistent growth trajectory under natural development conditions (curve 3). A peculiar aspect of this trend is the abrupt decline observed in 2020 due to the COVID-19 pandemic, which was swiftly followed by a resurgence at an even faster pace. This reflects the immediate consequence of inadequate parking resource provision, which forces drivers to park illegally to meet their needs. This practice not only intensifies traffic congestion but also exerts adverse impacts on urban governance and the environment.

From Figure 4 (curve 4), traffic congestion remained relatively stable with slight fluctuations before 2020. Subsequently, due to the impact of the COVID-19 pandemic, the traffic congestion level dropped significantly. However, a sharp increase was observed during the period from approximately 2022 to 2024, likely attributable to the overall relaxation of pandemic-related restrictions. In the later stage of the simulation (2024–2030), traffic congestion will show signs of easing. This can be explained by two factors. First, although the volume of private vehicle travel continues to grow, its growth rate gradually slows over time. Meanwhile, urban road area increases at a steady pace, leading to a moderate rise in the average road area per vehicle. Second, the model retains some baseline urban traffic management mechanisms, which continue to exert influence even under the natural scenario. As a result, the overall degree of congestion shows a modest decline.

Excluding the three years influenced by the pandemic (2020–2022), civil vehicle travel demand increased steadily throughout the simulation period (curve 5), with growth accelerating in the later years. This reflects a heightened dependence on civil vehicles amid economic development and urbanization, a trend that is expected to further strain parking spaces and transportation infrastructure.

The CO₂ emissions from civil vehicles (curve 6) increased alongside the rise in travel demand. They later stabilized, which may support the achievement of the carbon peak target by 2030. This highlights the significant indirect impact of parking issues on environmental pollution, underscoring the need for further attention to this challenge.

In the scenario of natural development, the results showed a parallel increase in parking shortages and illegal parking. A clear negative correlation was observed between parking convenience and traffic congestion. Furthermore, the increase in civil vehicle travel exacerbated parking problems and had adverse environmental impacts. These findings reveal that UPSS issues will continue to deteriorate under the scenario of natural development. Therefore, targeted policy interventions are urgently needed to mitigate the degree of parking space shortages and reduce traffic congestion and environmental pollution.

3.2. Improved Strategies Based on Nudge Theory

In response to the continued aggravation of urban parking system problems, nudge strategies, such as gain disclosure, salience, and outcome notification, were introduced. They were set together at three policy implementation levels: low, medium, and high, corresponding to the development scenarios of guide adjustment, balanced regulation, and enhanced change. The comparative analysis results are shown in Figure 5 and

Table 4. Impact of different scenarios on main variables (in 2030).

Scenario	Degree of Parking Space Shortages	Degree of Traffic Congestion	Number of Civil Vehicle Trips (ton)	CO2 Emissions from Civil Vehicles (ton)
Natural development	0.527011	0.225057	1.7623 × 106	9.2900 × 106
Variation	-	-	-	-
Guide adjustment	0.482919	0.214207	1.7300 × 106	8.6526 × 106
Variation	−8.37%	−4.82%	−1.83%	−6.86%
Balanced regulation	0.415281	0.229264	1.7758 × 106	7.8826 × 106
Variation	−21.20%	1.87%	0.77%	−15.15%
Enhanced change	0.390452	0.261059	1.8802 × 106	7.9388 × 106
Variation	−25.91%	16.00%	6.69%	−14.54%

.

In terms of mitigating parking space constraints (Figure 5a), curve 2 (guide adjustment scenario) followed a similar upward trend to the natural development scenario, showing only a minor effect. Curves 3 and 4 exhibited a slight decline during the initial stages of the simulation, followed by a stable trend of development. However, around the year 2026, both curves rebounded and maintain an upward trajectory, with a growth rate significantly higher than the natural development scenario. Despite this, the effectiveness remains notable. According to Table 4, the three nudge scenarios, respectively, reduced the degree of parking space shortages by 8.37%, 21.20%, and 25.91%.

In terms of traffic congestion mitigation (Figure 5b), the enhanced change and balanced regulation scenarios initially show higher congestion levels than the natural development scenario. This may be due to the early intensity of nudge strategies, which were overly frequent or complex. The resulting information overload caused confusion or anxiety among the public, leading to increased travel frequency. As civil vehicle trips plummeted during the pandemic (Figure 5c), a significant reduction in traffic congestion was realized, and the effects of the three scenarios were similar during this period. By 2024, congestion levels in all scenarios rebounded to between 0.21 and 0.28. Curves 1 and 2 exhibited a slight downward trend over time, and the guide adjustment scenario achieved a 4.82% reduction in congestion. Curves 3 and 4, on the other hand, rebounded later in the simulation, and the enhanced change scenario even increased congestion by 16%. The reason for this may be that under the strong nudge strategy, the degree of parking shortages dropped rapidly in the later simulation period. This improvement in parking availability enhances perceived convenience, making civil vehicle use more attractive to the public. As a result, both the willingness to use and to purchase civil vehicles increases, leading to a rise in the number of civil vehicle trips. This behavioral shift ultimately contributes to increased traffic congestion.

In terms of civil vehicle travel suppression (Figure 5c), the strategies initially showed side effects similar to those observed in Figure 5b. Improved measures in the middle of the simulation produced certain effects, and the effects of the enhanced change and balanced regulation scenarios were similar. In particular, in the late stage of the simulation, curve 4 exhibited a sharp growth, increasing civil vehicle trips by 6.69%. Meanwhile, the travel volume under the balanced regulation scenario remained nearly identical to that of the natural development scenario. In contrast, curve 2 (guide adjustment scenario) reduced it by 1.83%. The reason may be that the significant reduction in the degree of parking space shortages has stimulated more people to choose civil vehicle travel, resulting in an inducing effect on the demand for civil vehicle travel.

Regarding carbon reduction (Figure 5d), the balanced regulation and enhanced change scenarios proved more effective. They reduced motor vehicle CO₂ emissions by 15.15% and 14.54%, respectively, with the balanced regulation scenario performing slightly better. In contrast, the guide adjustment scenario achieved a 6.86% reduction. Notably, curves 2 and 3 indicated a carbon peaking trend in the later simulation stage. This suggests that although high-intensity nudge strategies can reduce carbon emissions, they are also associated with the risk of increased vehicle trips. In addition, looking at the images vertically, the spacing of curves 2 and 3 is significantly larger than that of curves 3 and 4, which suggests that there is a diminishing marginal effect of nudge strategies (the same pattern exists in Figure 5a–c).

Nudge strategies are effective in alleviating urban parking constraints and improving environmental outcomes. However, excessive intensity may lead to unintended consequences, such as traffic congestion and a rebound in travel demand. In future policy design, it is recommended to give priority to the balanced regulation scenario and combine it with demand management to achieve more comprehensive transportation and environmental benefits.

3.3. Impact of Different Scenarios on UPSSs

In order to further explore the effect of the nudge strategies on the degree of parking space shortages and the internal connection of related variables, a comparative analysis of mitigating UPSSs under different scenarios was conducted. The results are shown in Figure 6 and

Table 5. Effects comparison of different scenarios (in 2030).

Variable	Natural Development Scenario	Guide Adjustment Scenario	Balanced Regulation Scenario	Enhanced Change Scenario	Variation
Degree of parking space shortages	0.527011	0.482919	0.415281	0.390452	−25.91%
Number of vehicles illegally parking (vehicles)	1.3413 × 106	1.2599 × 106	1.2087 × 106	1.2484 × 106	−6.93%
Degree of parking convenience	0.402463	0.416335	0.463181	0.497724	23.67%

.

As shown in Figure 6b, the guide adjustment scenario was improved compared to the natural development scenario. However, due to the low nudge intensity, the effect of mitigating the degree of parking space shortages was limited and failed to significantly solve the system’s difficulties. The balanced regulation scenario (Figure 6c) showed more significant improvements in reducing parking shortages, illegal parking, and enhancing parking convenience. Especially in the suppression of illegal parking, it dropped to about 1.2087 × 10⁶ vehicles at the end of the simulation. The effect in the balanced regulation scenario was even significantly higher than that of the enhanced change scenario (Table 5), which is a better combination of nudge strategies. The enhanced change scenario ultimately reduced parking shortages and illegal parking by 25.91% and 6.93%, respectively. It also increased parking convenience by 23.67% compared to the natural development scenario, driven by high-intensity nudge strategies. The enhanced change scenario had the most significant effect in mitigating UPSSs, but it also had side effects that had limited performance on the number of vehicles illegally parking.

The trends illustrated in Figure 6a–d indicate that nudge strategies tended to have a relatively weak impact in the initial stage of strategy implementation. However, they gradually manifested over the medium- to long-term development process (after 2024), exhibiting a lagged effect. This may be related to the cumulative impact of strategy execution and the adaptability of drivers’ behaviors. Furthermore, a comparison of curves 2 and 3 in Figure 6a–d revealed that as parking convenience increased, its correlation with congestion shifted from negative to positive. This change suggests that the system underwent a critical transition at a certain point, exhibiting a counterintuitive dynamic feedback mechanism.

4. Discussion

4.1. Multiple Overlapping Effects

In terms of mitigating UPSSs, there are induced demand effects, rebound effects, and amplification effects of social and economic growth in the system. Multiple effects are superimposed, resulting in a significant increase in civil vehicle travel in the later stage of the simulation.

The reduction in the degree of parking space shortages can trigger an induced demand effect. In the short term, improved parking convenience decreases the degree of parking space shortages by reducing the costs incurred by the public in searching for parking spaces (curves 1 and 3 in Figure 6a–d). However, this short-term benefit may induce more people to choose civil vehicles (Figure 5c, curve 4), increasing vehicle purchase demand. Coupled with government subsidies for purchasing new energy vehicles, the goal of carbon peaking, the smart technologies of automobile production [37], and intensifying price competition among different brands [38], these factors lead to a decline in the price of vehicles, which may stimulate a rapid increase in the number of civil vehicles in the long run [39]. Consequently, the increase in civil vehicle ownership further intensifies competition for limited parking resources, resulting in a rebound increase in parking space shortages as system demand exceeds supply capacity over time (Figure 5a). As time progresses, economic development and urbanization often lead to more ample economic conditions and an increase in travel demand [22]. This, combined with the decline in vehicle prices, indirectly accelerates the growth in the number of civil vehicles. This amplification effect, superimposed on the short-term improvements brought about by parking optimization, ultimately leads to a rebound in civil vehicle travel. Influenced by the substantial increase in civil vehicle travel, the degree of traffic congestion also experiences a rebound phenomenon in the later stage under the enhanced change scenario (Figure 5b).

The effect of the nudge strategy is swallowed up by the multiple overlapping effects. Especially in the late stage of the simulation, the phenomenon of reverse development was obvious. Therefore, when implementing the nudge strategy to solve the problem of UPSSs, attention should be paid to the possible problems of its long-term implementation.

4.2. Paradoxical Effect of Mitigating Traffic Congestion

The nudge strategy is effective in mitigating the degree of parking space shortages and reducing CO₂ emissions from civil vehicles. However, in the management of civil vehicle trips and traffic congestion, the nudge strategy even produces a negative effect, forming the paradox of traffic congestion mitigation.

The changes illustrated in Figure 5 and Table 4 indicate two key points. First, significant changes in the transportation system can occur rapidly if the necessary political will is present [40]. Second, as the intensity of implementation increases, civil vehicle travel demand reverses in the later simulation stage. Specifically, in the balanced regulation scenario for 2030, the number of civil vehicle trips increased by approximately 1.3490 × 10⁴ vehicles, while in the enhanced change scenario, the increase reached approximately 1.1791 × 10⁵ vehicles. Meanwhile, the degree of traffic congestion increased by 0.004207 in the balanced regulation scenario and by 0.036002 in the enhanced change scenario (its increase reached 16%).

These results suggest that, in the long term, nudge strategies may have unintended side effects [28], not only increasing civil vehicle travel demand but also exacerbating traffic congestion. Furthermore, as indicated by curve 4 in Figure 5a,d, these side effects indirectly undermine the effectiveness of nudge strategies in mitigating traffic congestion and reducing carbon emissions [41]. The effectiveness of the enhanced change scenario was significantly diminished in the later stage of the simulation.

4.3. Critical Point Effect

There is a critical point effect in the relationship between the degree of parking convenience and the degree of parking space shortages. When parking convenience is at a low level, improving convenience can significantly mitigate parking space shortages. However, when parking convenience reaches a certain critical point (about 0.45), its effect is reversed. The shortage of parking spaces and the number of illegal parking have suddenly increased, causing a qualitative change. As shown in Figure 6c, after the degree of parking convenience (curve 3) exceeds 0.45, which is projected to occur around 2027, there is a significant increase in the rate of growth of parking space shortages (curve 1). According to Figure 6d, it is projected that the parking convenience will reach 0.45 around 2024. Compared to Figure 6c, the degree of parking space shortages will suddenly increase about three years earlier.

On the one hand, the shortage of parking spaces and the increase in the number of illegal parking will cause drivers to spend more time looking for suitable parking spaces, thereby exacerbating pollutant emissions and traffic accident rates [42,43]. On the other hand, they will reduce the efficiency of urban road traffic, which will further aggravate traffic congestion [44], forming a vicious circle. Meanwhile, emergency passages, such as fire rescue, may be blocked, which will pose a huge threat to public life and property safety [45,46]. In addition, the above phenomenon will lead to chaos in the city, which may affect the image and reputation of the city and is not conducive to the sustainable development of transportation [47].

4.4. Model Framework Evaluation

The integrated framework provides a dynamic and behaviorally informed approach to understanding and managing urban parking challenges. By combining system dynamics with behavioral constructs from nudge theory and self-regulatory processes of behavioral change, the model captures the nonlinear feedback mechanisms between interventions, psychological processes, and system-level outcomes. Simulation results demonstrated internal logical consistency, and model tests indicated that the model’s outputs responded reasonably to parameter variation, reinforcing its structural robustness. While this study is empirically grounded in data from Zhengzhou, the overall structure and logic of the framework can be readily adapted to other cities facing similar challenges related to parking shortages and traffic congestion. The core behavioral mechanisms are not specific to any single geographic context; rather, they reflect more universal patterns of behavior change in urban transportation systems. Therefore, the results may serve as a valuable reference for cities seeking to develop more effective and behaviorally informed transportation governance strategies.

5. Conclusions and Recommendations

5.1. Main Conclusions

From the perspective of nudge theory, this paper established the UPSS mitigation algorithm based on NT-SPBC-SD theory to represent the urban parking convenience and parking space shortages. In addition, the SD model of UPSS mitigation system based on nudge theory was constructed, and the conclusions obtained through the simulation and analysis of different scenarios are as follows:

• Nudge strategies effectively mitigated parking pressure and demonstrated significant environmental benefits. Compared to the natural development scenario, the guide adjustment, balanced regulation, and enhanced change scenarios reduced parking pressure by 8.37%, 21.2%, and 25.91%, respectively, and decreased illegal parking by 6.07%, 9.89%, and 6.93%, respectively. Among these, the balanced regulation scenario exhibited the best overall performance. In addition, CO₂ emissions from civil vehicles decreased by 6.86%, 15.15%, and 14.54% under the three scenarios, demonstrating considerable carbon reduction potential. However, while the enhanced change scenario effectively mitigated parking issues in the early and middle stages, its effectiveness declined significantly in the later stage, even resulting in an adverse effect.
• The proliferation of civil vehicle travel was exacerbated by multiple overlapping effects. Consequently, this exacerbated the severity of traffic congestion, thereby impeding the effectiveness of measures aimed at mitigating parking space shortages and reducing CO₂ emissions. Furthermore, it may give rise to issues such as resource overutilization and environmental burdens.
• Nudge strategies have the potential to enhance parking convenience, but they may readily induce a policy fatigue effect beyond a critical point. Simulation results revealed that as the implementation intensity of nudge strategies intensified, the degree of parking convenience was elevated. Nevertheless, to a certain extent, the enhancement of parking convenience levels can effectively mitigate the degree of parking space shortages. Conversely, once convenience surpasses a specific critical point, it may stimulate an increase in the demand for civil vehicle travel, thereby leading to a rebound increase in the degree of parking space shortages.

5.2. Recommendations

Based on the above conclusions, the following three suggestions are put forward:

• Adopt a dynamic level of nudge interventions. This involves clearly communicating the potential benefits of behavioral change to the public, enhancing message salience to help individuals quickly recognize problems and make timely adjustments, and providing continuous feedback on behavioral outcomes. These nudge strategies can continue to mitigate parking space shortages. Meanwhile, a dynamic monitoring and feedback mechanism should be established to track the effects of the interventions. This allows timely adjustments to the level of implementation in response to system uncertainties and avoids paying a higher cost for implementing the strategy while achieving a less-than-expected improvement in traffic.
• To tackle the negative impacts arising from multiple overlapping effects, this study focused on two aspects: demand management and the promotion of green travel. On the one hand, to improve parking resource allocation and balance the supply–demand relationship, a time-based dynamic pricing system was adopted to address the temporal and spatial differences in parking demand across various regions. By increasing parking costs during peak hours and reducing fees during off-peak times, a staggered parking pattern is encouraged. Furthermore, the supply of parking spaces in core areas should be restricted, and the addition of new parking spaces in city centers should be gradually reduced, thereby curbing unnecessary demand for civil vehicle use from the supply side. On the other hand, to guide behavioral changes and reduce dependency on civil vehicles, investments should be made in constructing a safer and more convenient public transportation system, expanding its coverage, and improving service quality, while also increasing the frequency of public transportation subsidies. Meanwhile, a quantity control measure should be implemented for civil vehicle ownership, such as limiting the issuance of vehicle license plates. In addition, public awareness and education should be strengthened to make people recognize the impact of civil vehicle travel on the environment and resources, and behavioral guidance should be provided to reduce reliance on personal vehicles.
• To address the critical point effect, recommendations are proposed from the perspectives of integrated management, dynamic feedback, and technological enhancement. First, implement regional parking pricing strategies by increasing parking fees in high-demand areas, such as business districts and areas near major public transportation hubs, while moderately reducing fees in low-demand regions to help redistribute parking pressure. Second, enhance the technological level of parking facilities. Prioritize the development of multi-story parking structures and P + R facilities to establish a decentralized parking network, avoiding the concentration of parking demand at single locations. Third, strengthen penalties for illegal parking. Expand the application of intelligent monitoring systems to detect and penalize illegal parking in real time. Increase the cost of illegal parking through hefty fines and credit point deductions. Additionally, designate temporary parking spaces or shared parking spots in areas with severe parking space shortages to mitigate the pressure from illegal parking. Finally, promote public feedback and data sharing. Utilize parking management or map applications to collect public feedback on parking convenience and traffic congestion. Leverage big data analytics to ensure convenience remains within a manageable range, thereby avoiding surpassing the critical point.
• From a practical governance perspective, the selection between the balanced regulation and the enhanced change may depend on local constraints and policy environments. The balanced regulation may be more suitable for cities with limited financial resources, gradual implementation preferences, or high sensitivity to public resistance, as it emphasizes incremental change with relatively lower risk. In contrast, the enhanced change strategy, while potentially achieving faster and more substantial improvements in parking efficiency, may require greater upfront investment, stronger administrative capacity, and higher political support to implement successfully. Therefore, decision-makers may consider factors such as budget availability, implementation capacity, and public acceptability when selecting an appropriate strategy.

As urban development continues to reshape mobility patterns, future research could enhance the system dynamics model by incorporating variables related to land use, urban expansion, and long-term infrastructure planning. Notably, traffic demand is a fundamental driver of individual travel behavior. This study did not include demand management strategies, such as congestion pricing and low-emission zones, which warrant further exploration in future research.