Parking Pricing Research Articles

Proactive pricing strategies for on-street parking management with physics-informed neural networks

Effective pricing is important for on-street parking management and proactive parking pricing is an innovative strategy to achieve optimal parking utilization. For proactive parking pricing, accurately predicting parking occupancy and deriving the price elasticity of parking demand are necessary. In recent years, there have been an increasing number of studies applying big data technology for parking-occupancy prediction. However, existing research has not incorporated economic knowledge into modeling, thus preventing application of the price elasticity of parking demand. In this study, proactive pricing strategies are proposed to adjust on-street parking prices which involve a parking-occupancy prediction model and a price-optimization method. Physics-informed neural networks are employed to achieve accurate prediction of parking occupancy and calculation of parking price elasticity. An elasticity-occupancy parking-management strategy is proposed for on-street parking management which leverages parking occupancy and price elasticity to guide pricing interventions. A case study shows that the parking-occupancy prediction model can make accurate predictions and derive the price elasticity of parking demand. Proactive parking pricing enables drivers to plan their trips in advance, allowing parking occupancy within an optimal range.

Assessing Road Users’ Preferences for Various Travel Demand Management Strategies for Adoption in Accra, Ghana

The rise in population density, vehicle ownership, and urban development has significantly influenced travel demand and altered travel patterns, leading to traffic congestion in rapidly growing urban centers such as Accra, Ghana. The traditional approach of expanding roadways to accommodate rising traffic is no longer environmentally sustainable. Instead, the emphasis has shifted toward travel demand management (TDM) strategies as a more sustainable solution. This study aimed to investigate a range of TDM strategies that can be adopted in Accra to improve traffic flow through the lenses of everyday road users. The study employed a questionnaire survey and a stratified sampling technique to gather data from 615 respondents for relative importance index (RII) ranking and Chi-square statistical analysis. The findings revealed that the topmost preferred strategies were mass transit improvements, walking and cycling improvements, and alternative work schedules. Notably, mass transit improvements emerged as the most preferred strategy. Conversely, strategies like efficient parking pricing, congestion pricing, and higher fuel and road taxes for private vehicles ranked lowest, garnering minimal preference. The study also revealed a statistically significant correlation between variables such as age, education level, marital status, income level, and mode of transportation and all the selected TDM preferences. However, no significant relationship was found between gender or car ownership and any of the selected TDM preferences. The study provides valuable insights into road users’ preferences for TDM strategies that can aid in planning future urban mobility systems to proactively manage travel demand, alleviate congestion, and promote sustainable transportation options for the city’s residents.

Analysis of Shared Parking Game Model under Dynamic Parking Pricing

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.

Economic Analysis of On-Street Parking with Urban Delivery

The surge in online shopping has dramatically increased the demand for short-term curb access for package pickups and deliveries, leading to heightened competition for limited curb space. This paper addresses the problem of how the unique parking demand of deliverers, particularly their parking duration for delivery attempts linked to parking space availability, affects the dynamics of urban curb parking systems. We develop continuum models of a curb parking system and perform analytical analyses to understand the dynamics and steady-state properties of the system under the influence of increased urban deliveries. We conduct comparative statics to examine how various curb management measures, such as pricing, parking duration caps, and dedicated delivery bays, influence the equilibrium conditions, followed by comparisons of the theoretical capacity of these measures. We further demonstrate the working mechanism of delivery bays and their role in forestalling specific failures within a hybrid system with both general parkers and deliverers. Finally, we investigate curb management strategies in nonstationary operational contexts and prescribe the optimal strategies therein. Our findings offer valuable insights into the unique properties that deliverers introduce to curb parking dynamics, highlighting the need for a strategic reevaluation of current management practices. We find that pricing strategies for metered parking to general parkers prove to be more efficient and flexible compared with other interventions. Notably, our analysis suggests that when curb parking pricing is optimally calibrated, the necessity for dedicated delivery bays diminishes. Furthermore, we reveal that optimal curb management strategies could diverge in response to surges in demand, depending on whether the increase sources from general parkers or deliverers. To be effective, the sizing of delivery bays must align with the underlying causes of parking scarcity. History: This paper has been accepted for the Transportation Science Special Section on ISTTT25 Conference. Funding: This work was supported by the George Washington University [University Facilitating Fund]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2024.0569 .

Parking preferences of tourists in Sun Moon Lake scenic area

Sun Moon Lake is a famous tourist attraction in Taiwan and around the world. However, as Sun Moon Lake is surrounded by mountains and has limited land to develop, traffic congestion around the lake area is commonplace during peak holiday hours. This study focuses on the parking choices of visitors to Sun Moon Lake and develops a stated preference (SP) instrument with multiple scenarios to evaluate parking preferences under various financial and service level scenarios. We estimate Multinomial Logit (MNL) and Mixed Logit (ML) models, accounting for the panel nature of the data (PDML)to identify preferences for parking choices of visitors to Sun Moon Lake. The focus is on understanding how parking price, travel time, walking time, scenery, and transfers between public transport affect visitors’ parking choices. Unlike the findings of studies in metropolitan areas, which often find that parking price was the deciding factor, visitors’ parking decisions in the tourist area were more concerned with time factors, such as the time to search for places to park and traffic congestion, possibly due to the less frequent use of tourist venues. Although raising parking price can suppress parking demand in the scenic area, other parking management mechanisms work better, such as the construction of new and suitable outer parking lots with transfer buses to relieve the heavily congested traffic in the scenic area. In addition, we find that using the parking space in the area can be improved by beautifying the landscaping between the parking lots and the tourist spots, enhancing the pleasure of traveling along the routes, introducing multiple transfer modes, and providing real-time traffic information to tourists.

Dynamic Pricing and Route Guidance: A Multi-Agent System for Effective Parking and Traffic Management

In a city center that is usually flooded with traffic, finding a parking space is a challenge for most commuters. Searching for parking generates additional urban traffic, increases energy consumption and vehicle emissions, and wastes time. This study examines how parking systems can be used as an effective tool for reducing traffic congestion. It proposes a new, smart approach to parking using real-time dynamic pricing and route guidance in a multi-agent system to better align supply and demand, thereby reducing both congestion and the amount of time spend by drivers searching for parking. To examine the effects of dynamic parking pricing on road traffic, we conducted a multi-agent open-source framework to simulate spatiotemporal distributions of daily individual activity. By extending MATSim, we were able to model parking usage more realistically. We developed several micro-simulation scenarios to evaluate strategy performance for both on-street and off-street parking in the city center of Tunis, Tunisia. The results show that dynamic pricing policies can be used to plan and manage parking areas more efficiently, minimizing the dispersion of parking occupancy lots. Variations in parking prices were shown to increase parking lot vacancies and reduce traffic congestion. Moreover, the system can also generate increased revenue for government and private-sector parking authorities while improving driver satisfaction. Finally, the parking dynamic pricing strategy promises a convenient mobility experience, while increasing efficiency and reducing the negative externalities of urban transportation.

Integrated Departure Time and Parking Location Choices in a Morning Commute Problem under a Partially Automated Environment

This study formulates the joint decisions of commuters on departure time and parking location choices in a morning commute problem where the commuters travel with autonomous vehicles (AVs) or human-driven vehicles (HVs). Under a mixed traffic environment, we aim to explore the impacts of parking capacity and parking pricing on the equilibrium travel pattern and the system performance. We build a dynamic equilibrium model for the morning commute problem by assuming that the parking slots can be grouped into central and peripheral clusters based on the distance between the parking location and the workplace. We first analyze the parking location preferences of commuters towards the two parking clusters under a mixed traffic environment. We then examine the equilibrium conditions and identify all the equilibrium travel patterns. We further analyze the system performance measured by the total travel cost with respect to the parking prices and the capacity of the central cluster. The optimal parking pricing scheme is also derived to minimize the total travel cost. We conduct numerical analysis to demonstrate the change in the total travel cost against the parking capacity of the central cluster and its parking price. Sensitivity analysis is performed to show the impacts of the network configuration on the total travel cost.

Dynamic Parking Pricing: Implementation in North America


 
 
 Dynamic parking prices, which adjust in response to changes in demand, can be a useful tool for managing parking availability and encouraging turnover. However, there are some limitations to this approach. First, it can be difficult to implement and administer, requiring complex pricing algorithms and systems to collect and process data in real-time. Additionally, dynamic pricing may not be well-received by drivers, who may perceive it as unfair or unpredictable. Parking as it stands is a relatively ‘dumb’ system, and introducing any smartness into the system will include a number of added complications that can limit the uptake and the success of the system. This paper will look into the parking problem overall, introduce dynamic parking as a potential solution, and then delve into the example of SF Park. Finally further considerations and alternate solutions will be considered in the context of dynamic parking pricing to provide context and consider further research. 
 
 

Exploring parking choices under the coexistence of autonomous and conventional vehicles

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

Dynamic vehicle parking pricing. A review

Dynamic parking pricing refers to the adjustment of the price of parking to achieve the required occupancy rates. It plays a significant role in parking management systems to minimize traffic congestion and cruising time, as well as to maximize revenue. The optimization of parking pricing and supply through a time-varying pricing strategy is a crucial issue. This paper reviews academic work on approaches to parking pricing, giving emphasis to time-varying pricing strategies. Approaches based on game theory, dynamic and stochastic control, multiobjective and multilevel programming, queuing theory, artificial intelligence, statistics, among others, are reviewed. We categorize these techniques to examine various issues of dynamic parking pricing. The main contributions and methods used are summarized. Furthermore, a brief discussion of the strengths, limitations, and possible future work is given.

Agent-based modeling of residential parking zones in Leipzig

This study introduces a methodology to model parking cost and residential parking zones within the agent-based simulation framework MATSim. An overview of related research and a description of the implementation are given. The results suggest that parking pricing has a larger impact on the share of motorized transport than longer access and egress walks to the activity. Raising parking costs could prove to be an effective tool to reduce motorized transport.

Analyzing Parking Demand Characteristics Using a Bayesian Model Averaging

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

Acceptability of transportation demand management policy packages considering interactions and socio-economic heterogeneity

The Transportation Demand Management (TDM) policies will not be authorized if they have not enough public acceptability. In this study, policy packing is proposed as a way to alleviate the unacceptability of coercive (push) TDM policies that have considerable effectiveness (e.g., cordon and parking pricing) by using some non-coercive (pull) TDM policies (e.g., transit development). Furthermore, in addition to the main effects, the interaction effects of TDM policies on the acceptability of two policy packages are addressed. Policy package (I) includes cordon pricing and reduction of transit access time, and Policy package (II) contains parking pricing and reduction of transit access time. A Random Parameter Ordered Logit (RPOL) model is developed based on a choice experiment designed for car commuters of Tehran, Iran. Results confirm that while reducing transit access time have a significant interaction effect with cordon pricing on the acceptability of Package (I) it has not a significant interaction effect with parking pricing on the acceptability of Package (II). Furthermore, the heterogeneity of respondents' cars value and the presence of free parking at the workplace can significantly affect the acceptability of Package (II). This study also addresses the effect of respondents’ trip and socio-economic characteristics on the acceptability.

Are supply and demand the main key drivers of airport parking prices? The quantitative study

Parking pricing always poses a challenge to airport management due to their impact on revenue. According to previous studies, parking prices have not considered air transportation supply and demand simultaneously until now, which is why challenges have arisen. Also, no model identifies the factors affecting parking prices and expresses their relationship quantitatively. The contribution of the current paper is to develop numeric models for pricing airport parking using supply and demand data. In this regard, the multiple linear regression approach is used due to its simplicity and interoperability. Three time periods for airport parking pricing are considered as dependent variables: 1-h short-term, 24-h short-term, and 24-h long-term. Independent variables also include air transportation supply and demand characteristics along with socioeconomic variables based on previous research. Employing collected data from 60 airports on all continents, the final model for each parking type is selected. The results indicate that parking expense will be affected more by transportation supply than by transportation demand. As a measure of the airport's supply, short-term parking prices have been significantly affected by the average cost of amenities. By affecting passengers' access to the airport, public transportation ticket prices also impact the cost of 24-h long-term parking. These supply-related factors have a more noticeable effect on parking prices than demand variables, such as the number of airport passengers, and this can be taken into account by managers and planners in the future.

Game-Based Demand Feedback Reservation Parking Pricing

Reservation parking is a new type of parking that allows users to obtain a parking space for an appropriate time slot by reservation. This paper pioneers a high degree-of-freedom reservation strategy and rules for commercial districts in which users can arbitrarily select parking time slots. The nested logit is applied to build a parking revenue model by analyzing the cost, demand, and price factors sequentially according to the user’s choice behavior. The parking lot price game in the region is dissected and a nonlinear pricing model is proposed for maximizing regional returns. A model validation of a parking lot in an actual commercial area shows that compared to the traditional parking method, the parking lot revenue increased by 69.63% upon the application of this reservation strategy and pricing scheme, while the parking lot traffic increased by 22.4%. This model can provide strong support for reservation parking pricing in commercial areas. In the comparison of scenarios, the scientific validity of this model using dynamic pricing and regional game competition is verified. In the application, a reasonable price scheme can be formulated based on the parking lot traffic, cost, and surrounding parking strategies, which can result in higher parking revenue while providing users with great parking convenience.

Autonomous valet parking optimization with two-step reservation and pricing strategy

With the development of autonomous vehicle, the autonomous valet parking has been attracting extensive attention, by relieving users’ inconvenience from parking. Compared with shortrange autonomous valet parking, long-range autonomous valet parking extends parking features with bridge of long-range travelling and parking in a user-friendly cycle. However, due to the uncertainty of trip routes, the performance of single reservation mechanism on parking lot status prediction is limited. Besides, unified parking pricing intensifies parking competition and causes local congestion. Therefore, we propose a long-range autonomous valet parking framework based on Two-step Reservation Mechanism. The proposed scheme provides the first reservation service before arriving at Drop-off spots (regular step), so as to anticipate parking demand of parking lot. Then, it provides the second reservation service when arriving at Drop-off spots (extra step) for updating the optimality of parking decision. The future parking lot status can be predicted with the impact of concurrent requests and reservation execution probability. Meanwhile, we propose a heterogeneous pricing strategy, in which the parking lots with higher parking demands would be adjusted with higher prices. Simulation results show that our scheme outperforms literature works on reducing parking cost and balancing parking demand.

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.

The impact of on-street and off-street parking regulations on parking type choice

ABSTRACT In practice, parking regimes for different parking types (on-street, off-street and garage) are introduced independently, leading frequently to inefficient utilization. The aim of the paper is to show that in areas with different public parking types, integrated parking management is needed so as to utilize available capacities evenly. To demonstrate this, we determined the factors impacting parking type choice, and then the importance and intensity of parking measures thereon. Empirical data collected by interviewing on-street and off-street parking users were used to develop a binary logit model showing the most important parking type choice parameters: on-street time limitation, and on-street and off-street parking prices. Reduced time limitation was found to decrease on-street parking probability while increasing off-street parking probability. Also, price increase for one parking type reduced the probability of choosing that particular type and increased opting for alternative type. Moreover, a synergy effect of integrated parking management was demonstrated.

Transportation demand management policy efficiency: An attempt to address the effectiveness and acceptability of policy packages

As private car usage has negative effects, such as traffic congestion and air pollution, the effectiveness of Transportation Demand Management (TDM) policies could be considered as any reduction in car usage. However, the efficiency of TDM policies could be addressed by a combination of their effectiveness and public acceptability. Policy packaging is treated as a way to get more benefits from a set of policies in form of a package. Therefore, to improve the efficiency of a package containing a push TDM policy, which is usually effective but not acceptable, introducing a pull policy that is generally acceptable could be of interest. This research aimed to evaluate the efficiency of two packages of TDM policies containing a push policy (either cordon or parking pricing) and a pull policy (transit system development). Therefore, 535 working-purpose car commuters were interviewed to examine the trade-offs between the acceptability and effectiveness of each package. According to the results, although the transit development policy causes a 1.52% reduction in car usage, it significantly affects the acceptability of cordon pricing and transit development package. Finally, a discussion is provided regarding how to use the policy packages to achieve the highest potential effectiveness at a specific level of acceptability.

Examining the Effect of Weather Conditions on On-Street Parking Variables

In this study, the effect of weather conditions on on-street parking variables is examined. In this context, data sets consist of 17 variables such as parking capacity, employee personnel, off-duty personnel, turn-over rate, first-hour parking price, daily parking price, number of transactions, number of cash transactions, average cash parking duration, 0-15 min parking duration, average parking duration, number of subscription, parking subscription price, daily transactions of subscription, subscription parking duration and income on the on-street parking spaces in the Anatolian Side of Istanbul for two years (2017-2018). In this study, turnover rate, parking duration, cash parking duration, and parking income variables are investigated by multiple regression analysis with dummy variables. Daily general weather data during this period are classified into 11 categories as sunny, cloudy, partly cloudy, light rain, rain, short-term heavy rainfall, heavy rainfall, light snow, light snowfall, snow, and a heavy snowstorm- as dummy variables. Based on the estimation results, it is found that both sunny and light snow + light snowfall + snow + heavy snowstorm weather variables decrease parking duration by 0.528 and 1.293 min, respectively. Moreover, results also indicated that the same variables increase cash parking duration by 1.21 and 3.29 min, respectively. Both sunny and cloudy + partly cloudy weather variables increase parking income by 719 TL and 580 TL, respectively. Light snow + light snowfall + snow + heavy snowstorm weather variable decreases parking income by 1143 TL.