Parking Pricing Research Articles

Dynamic Parking Allocation Model in a Multidestination Multiple Parking Lot System

In a multidestination multiple parking lot system, multiple parking lots are available near a destination, and multiple destinations can be served by the same parking lot. The allocation of the parking demand of each destination to each parking lot is called the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">parking allocation problem</i> . The factors that affect the driver’s parking choice are analyzed, including parking fees, parking space search time, and walking time. Considering the dynamic parking demand and drivers’ probabilistic choice of each parking lot and that the parking amount allocation during each time period must meet the capacity constraints of each parking lot, an equivalent mathematical programming model was established. The solution algorithm for the proposed model was proposed. A numerical example verified the proposed model, and the influences of dynamic parking charge on drivers’ parking choice and the balance of utilization among different parking lots were discussed. The proposed model can provide a basis for parking facility location optimization and dynamic parking pricing strategy.

Development of Transportation Models Based on Students’ Interest in a Parking Charging System at Universiti Malaysia Sabah (UMS)

Transportation management and sustainable transportation planning were critical. A well-planned transportation system is extremely beneficial in terms of efficiency and environmental friendliness. To that end, parking charging was one of the transportation management topics covered in this study. A parking charging system is one in which a user can leave their vehicle at a particular place and pay a price based on the amount of time it was left unattended. Given the rising use of private vehicles, which has resulted in an increase in congestion and air pollution, it is believed that a parking fee system can be implemented to alleviate the situation. The primary purpose of this research is to develop a transportation model based on the parking price factor in Ringgit Malaysia (RM). At the completion of the study, a transportation model based on parking rates will be developed, and it is projected that once implemented, the percentage of private vehicles that use public transportation will increase. This model is deemed necessary in order to mitigate the harmful effect of an excessive number of private vehicles at UMS. The State Preference Survey (SPS) method was used. A questionnaire form was developed and distributed online to 300 respondents among the students of the Faculty of Engineering at UMS, in order to collect the required data. The data collected was then analyzed using linear regression to develop several transportation logistic models. The transportation models that have been developed in the form of a logistic model that can reflect the willingness of UMS students to shift from private vehicles to public transport. These models predict that when the parking price increases, the percentage shift of private vehicles to public transport will increase linearly. It is also found that 100% of drivers are willing to shift from private vehicles to public transport if the parking price per hour is RM 4.00. Shifting private vehicle users to public transportation may assist lower the number of private vehicles on the road and thus indirectly help mitigate the negative consequences of an excess of private automobiles.

University campus parking: It’s all the rage

Transportation planners, engineers, and researchers have long lamented the highly emotional public responses generated by changes to parking policies. We know that reducing the supply and increasing the price for parking—while intended to advance sustainability and other important community goals—seems to fuel an angry response, but this knowledge is often vague and anecdotal. This study combines qualitative coding of open-ended survey responses with quantitative analyses of sociodemographic and commute characteristics using descriptive statistics and binary logistic regression models to reveal a strong correlation between parking and anger among University of Wisconsin-Milwaukee (UWM) campus users. Higher probabilities of anger are also positively associated with annual household incomes below $50,000, bus pass holders, and residential locations outside of the immediate UWM neighborhood. Qualitative themes from angry comments include frustrations about parking price, supply, and duration; questions about the motivations for university parking policies; and a sense of entitlement among campus users to free and inexpensive parking options. The study interprets these variables and themes together to provide insights into the complicated relationship between parking and anger and the importance of analyzing angry feedback to inform future policies.

Modeling the Effect of Congestion Charge and Parking Pricing on Urban Traffic: Example of Jerusalem

Abstract. Transportation Network Companies (TNC), like Uber, Lyft, and VIA, started their activities a decade ago with a far-reaching hope that Mobility-On-Demand (MOD) transportation services would decelerate or even stop the ever-growing congestion. However, it didn't happen; the negative incentives, like congestion charges and higher parking prices, seem to be the only policy tools for influencing congestion and associated negative externalities like pollution and noise. The question is whether we can establish socially acceptable congestion charges and parking prices that will effectively reduce the arrivals and traffic in highly congested areas and become the background for the future MOD arrangement? We employ the MATSim agent-based simulation model (Horni et al., 2016) of multi-modal traffic in Jerusalem Metropolitan Area (JMA) to address this problem. We investigate whether the combination of congestion and parking prices can force drivers to use Public Transport (PT), thus reducing arrivals with the private cars into the center of the city. The model study demonstrates that a reasonable charge of 7–12€ for entering the city center could decrease arrivals by 25%. From the transport policy point of view, the effects of congestion charges and parking prices are different – the increase in the congestion charges decreases arrivals. In contrast, the increase in parking prices decreases the dwell time. We discuss the policy consequences of employing each of the two mechanisms.

Repositioning of driverless cars: Is return to home rather than downtown parking economically viable?

As of today, a considerable amount of urban land is devoted to parking, especially in downtown areas. Fully autonomous vehicles (FAVs) – also referred to as self-driving car or driverless car – can self-park in less-expensive areas or even have no need to park at all and so can free city centers from parking lots and thereby relieve downtown land for more valuable purposes such as housing, production, recreation. This paper analyzes whether the technological capability of FAVs to reposition could also be economically viable under a wide range of differentiated constellations, paying particular attention to the heterogeneity of (downtown) parking pricing schemes (hourly parking vs. daily parking vs. employer-paid parking). We develop an economic speed choice model and calculate the utility maximizing (cost minimizing) driving speed of an empty electric FAV returning home. It is shown that compared to human driven cars, individual welfare losses when deviating from the privately optimal speed are rather small, implying that congestion effects would hardly affect economic viability of ‘return home’, as opposed to feasibility. We estimate feasible ‘return home’ options of an empty FAV depending on stylized parameters such as the average duration of stay of the traveler and distance between origin (home) and destination (e.g. work). To identify economic viability, we then juxtapose the net benefit of vehicle repositioning with the cost of downtown parking. Our analysis suggests that vehicle repositioning in the form of ‘return home’ may be economically viable for a wide range of constellations, in particular when account is taken of the benefits of intra-household car-sharing made possible by vehicle relocation. Our findings highlight the potential of FAVs to revolutionize the world’s transportation system and urban structure and stress the need to carefully fine-tune transportation policies to tackle the potential adverse societal effects of induced vehicle miles traveled owing to driverless vehicle repositioning.

Равновесие Нэша при выделении площадей для организации платных и бесплатных парковок с учетом интересов автомобилистов, городских властей и владельцев парковок

The problem of optimizаtion the distribution of urban territory near a center of gravity (airport, railway station, mall, etc.) between two types of parking lots: paid and free is considered. Obviously, under otherwise equal conditions the car user will choose free parking, but this will lead to an increase in the time to find a parking space. Therefore, paying for parking is beneficial for people who have a higher their value of time. Paid parking operator determine the size of the parking fee - if the price is low enough, all parking spaces will be occupied and there will be problems with parking, in addition, there will be lost profits. Lost profits will also occur if the price is high enough, because if the price goes up, fewer car users will be ready to use paid parking services. The city authorities' task is to determine part of the land which should be allocated for paid parking on the understanding that the rest of the land goes to free parking. The paid parking owners' task is to determine the optimal price in order to maximize the profit. Car users choose parking based on their value of time. A mathematical model was built with three types of participants: car users, municipal authorities and parking operators. The strategy of the passenger is the choice of parking, the goal is to minimize the cost and time. The strategy of the municipality is the allocation of territory for parking, the goal is similar to the goals of passengers. Parking operator with the strategy "parking price" achieves to maximize profit. The interaction of the participants is described within the framework of game theory, the objective functions of the players are considered, and the existence of the Nash equilibrium is proved. Numerical examples are considered, options for the development of the presented model are described. The dependence of characteristics on the total allocated number of parking spaces in two types of parking lots, the price of parking and the intensity of the car flow to the center of gravity is presented.

PADaaV: Blockchain-Based Parking Price Prediction Scheme for Sustainable Traffic Management

In most countries, traffic congestion has reached a level where managing traffic is tedious for regulatory bodies. The traffic management faced many issues such as route routing based on congestion, delivery of messages/emails to end-users, and real-time allocation of parking slots. There have been many works on predicting parking prices for traffic management, but most favor users or owners and are not secure. To address these issues, a blockchain and Interplanetary File System (IPFS)-based parking price prediction scheme (PADaaV) is proposed to facilitate the users to reserve a parking slot securely and efficiently. It mainly focuses on ensuring security, privacy, and transparency for parking slot owners and users. Furthermore, we employ a second price auction model to optimize the parking price for users, and parking slot owners can also get benefit from it. The performance of the PADaaV has been simulated for 100 users with 40 parking slots based on different auction models. The various performance parameters considered are profit for users, profit for parking slot owners, overall revenue of the system, scalability, computation time, and data storage cost. The performance results show that the PADaaV is secure and beneficial for users and parking slot owners.

Analysis of the Effect of Demand-Driven Dynamic Parking Pricing on on-Street Parking Demand

The rapid increase in the number of cars has caused many problems such as &#x201C;cruising for parking&#x201D; and &#x201C;parking illegally&#x201D;. Demand-driven dynamic parking pricing mechanism is an effective method to dynamically regulate parking demand, thus reducing the parking problems. In this research, we conducted a large number of on-street parking surveys in the business districts in Beijing. A parking location choice model and the decision rules for cruising process were established. The multi-agent based on-street parking simulation was constructed to explore the effect of time-varying parking price on parking demand. It is concluded that demand-driven dynamic parking pricing can effectively regulate the distribution of parking demand and make the utilization of parking facilities into the desirable range in the business districts. Lower desirable range and higher price change for the price adjustment can cause larger fluctuations of parking demand, less time intervals within the desirable range and more price adjustment times. Higher desirable range and higher price change can bring longer cruising and driving time and lower driving speed. Considering the effectiveness and the operating costs of the pricing schemes, it is recommended that the suitable desirable rang for parking occupancy rate is 60%&#x007E;80% and price change is 2 Yuan/hour. The threshold of price adjustment can be set according to the scale of the regional road network. The proposed dynamic parking pricing strategy can balance the distribution of parking demand and then reduce the parking and traffic problems. The research conclusions can also give references for the formulation of dynamic parking pricing strategies.

An Evaluation of Temporal- and Spatial-Based Dynamic Parking Pricing for Commercial Establishments

All smart parking management systems (SPMS) have incorporated the dynamic pricing into its features and capabilities. The collection of parking fees on a corresponding spot has been dependent on either its time- or space-value, with most SPMS utilizing temporal-based parking fee collection. However, little to no study has been conducted to assess or evaluate these pricing schemes according to its friendliness and economics towards both parkers and business operators. In this work, we evaluate two current temporal- and three proposed spatiotemporal-based dynamic parking pricing methods by computing their social optimum range and economic effects to both users and parking management entities. Our extensive analysis utilizing both empirical mobility traces and driver parking duration behavior provide important insights in the current pricing setups and present necessary adjustments in improving parking fee collection that contribute to societal benefits.

Bangkok’s locked-in traffic jam: Price congestion or regulate parking?

The current state of inner city car transport is highly inefficient, producing congestion, air and noise pollution, accidents, and contributing to global warming. We evaluate the environmental and social costs of motorized transport in Bangkok that turned in the last 25 years from a bus and motorcycle to a car-traffic dominated city, one of the world’s most congested. We find that total external costs burden Bangkok’s economy by about 7% – 10.8% of the GRP, almost half originating in congestion. We evaluate optimal Pigouvian taxation of driving a vehicle in Bangkok and calculate the costs of on-street parking in the inner city. Optimal congestion charging is about $2.3, optimal road toll is about $2.7, and optimal inner city parking is about $3.2 per hour. Our results imply that urban mobility would profit most from a combination of congestion and parking pricing, maintaining most of the flexibility of transport, e.g., by incentivizing shared pooled mobility, while also improving urban quality of life. Better parking management and inner city tolls would benefit most cities in rapidly urbanizing and motorizing Southeast Asia.

Parking violations as an economic gamble for public space

Parking fees and fines represent contested issues in cities, as they conflict with vehicle owners' opinion over ‘rights’ to public space. As cities have moved to regulate vehicle density by introducing parking fees, a share of drivers may feel compelled to ‘cheat’, weighing the price of parking fees against the cost of fines, or to simply park illegally outside designated areas. To better understand these interrelationships, this paper considers negotiations of public space as an economic gamble that can be assessed on the basis of parking violations. The study is situated in Freiburg, Germany, and relies on a mixed-methods approach that investigates the views of the regulatory agency and its enforcement officers, as well as registered parking violations (n = 181,735, in 2019). Time-spatial probabilities of being fined when parking illegally are calculated as risks, defined as detection probability per “hexagon-hour”, an indicator integrating space and time. Results confirm that in the inner city, it is economically rational not to pay parking fees during 86.1% of hexagon-hours, and in surrounding areas during 99.9% of the time. Findings confirm that urban space can be negotiated in an equilibrium of fees, fines and controls. To make vehicle ownership and use less attractive, the price of parking needs to be high. Fines need to be high in relation to fees, and control densities need to constitute significant detection risks.

Pricing curb parking

Where traffic is congested and all the curb spaces are occupied, some drivers are probably cruising for parking. Cities can eliminate this cruising by charging demand-based prices for curb parking to ensure one or two open spaces on every block. Drivers will then usually find convenient places to park within a short walk of their destinations. But if cities charge demand-based prices for curb parking, how will drivers choose where to park, and what will they pay? To answer these questions, I have examined how four variables—parking duration, number of persons in the car, walking speed, and value of saving time spent walking—determine parking choices when prices increase as drivers approach their destinations. Short-term parkers, carpools, slow walkers, and drivers with a high value of saving time will park closer to their destinations. Long-term parkers, solo drivers, fast walkers, and drivers with a low value of saving time will park farther away. This spontaneous, self-organizing pattern of parking choices responding to demand-based parking prices will minimize the collective cost of the time drivers spend walking to and from their destinations. Demand-priced curb parking will also reduce uncertainty about travel times. Drivers delayed by traffic congestion can save time at the end of their trips by paying a higher price to park closer to their destinations. Demand-priced curb parking at the end of a trip can serve as a buffer allowing late-arriving drivers to buy time when they need it most.

Citywide parking policy and traffic: Evidence from Amsterdam

We examine the effect of citywide parking policy on parking and traffic demand. Using a large increase in on-street parking prices for the city of Amsterdam, we show that the policy caused a substantial drop in on-street parking demand, which is not offset by an increase in off-street demand. The overall reduction in parking demand implies a 2% – 3% reduction in traffic, which is confirmed with traffic flow data. The decline in traffic is larger during the evening peak, which indicates that parking prices are effective in reducing congestion in the evening peak but less so in the morning peak.

Parking and Ride Induction Methods for Drivers in Commuting Scenes

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

Parking design and pricing for regular and autonomous vehicles: a morning commute problem

Autonomous vehicles can profoundly change parking behaviour in the future. Instead of searching for parking, the occupants alight at their final destination and send their occupant-free cars to a parking spot. This paper studies the impact of parking in a morning commute problem with autonomous and regular vehicles. We simplify the complex problem with distinct cost functions to a classic bi-class problem that can be analytically solved. To optimize the system, we develop temporal and spatial parking pricing strategies and a new parking supply design scheme, as practical alternatives for the conventional dynamic congestion pricing.

Development of Joint Stated-Preference-off-Revealed-Preference Model for Shanghai Commute Mode Choices in Response to Parking Fee Management

Facing challenges in parking demand-and-supply imbalance and severe road traffic congestion during peak periods in Shanghai, in this paper we develop an SP-off-RP (stated-preference-off-revealed-preference) choice model to analyze relations between parking fee and commute mode choices based on survey data collected there. The survey questionnaire collects information about travelers’ daily commute, travel choices in the SP context, and personal socioeconomic and demographic attributes. The road network and public transportation network data are also used for model development. The model includes three main travel modes: car, public transit, and non-motorized mode. Variables that significantly influence mode choice and the reasons behind it are discussed, including the parking fee, the level-of-service (LOS) of the three modes, and socioeconomic and demographic variables. In the process of model development, a random sample of full-mode commute trips in Shanghai is integrated to improve model precision. The study reveals that the new random disturbance in the SP context is relatively large. The direct elasticity of the parking fee is estimated at −0.85, which means that when the parking fee increases by 10%, the average probability of choosing a private car for the commute will decrease by 8.5%. It is also found that transit LOS improvements have potential to reduce auto use in Shanghai. The study provides references on parking pricing as an alternative policy for travel demand management in Shanghai.

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

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

Impact of pricing policy change on on-street parking demand and user satisfaction: A case study in Nanning, China

Understanding the pricing policy effect is important for on-street parking management. This study investigates the impact of the on-street parking price adjustment with a case study in Nanning. Parking meter data and survey data are used to analyze the impact of the policy intervention on parking demand and user satisfaction, respectively. Regression discontinuity design (RDD), structural equation model (SEM), and binary logistic regression (BLR) models are implemented. Results show that the increase in parking pricing significantly decreases the parking volume (by around 20%) and parking duration (by around 10%). Drivers’ parking costs also increased. The influence varies across different sub-areas, days of the week, and trip purposes. After the policy adjustment, people feel that the parking price is higher, the distance of parking lots to the final destination is closer, and the parking lots are more vacant. On-street parking users after the policy intervention have higher incomes. As they are less sensitive to the price but more on quality, the overall parking satisfaction is increased.

A Contemporary Review of Residential Parking Lessons for Indian Cities

ABSTRACT Given India’s urbanization rate, economic growth, and population size, a rise in private vehicle ownership appears inevitable. Residential parking in particular remains a sizable by-product with far-ranging consequences for land consumption, mobility choices, and housing affordability. To counter such undesirable externalities cities should equip themselves with strategies that are well-grounded in strong evidence. This article derives from literature extracted from the Web of Science, as well as development codes of select cities worldwide. The literature is reviewed and presented to understand the varied aspects of residential parking, to develop a comprehensive repository of attempted policy strategies, and to explore key takeaways for Indian cities. The review is organized into three sections—demand, supply, and pricing—borrowed from market economics. Our discussion in the section dealing with parking supply sheds light on alternate mechanisms—including shared parking (an appropriate tool for cities in rapidly urbanizing developing economies). Inferring from the literature review, we discuss our recommendations on how to formulate city residential parking policy using the case of Bengaluru, a bustling metropolis in southern India. We intend this article to contribute to the wider discourse pertaining to building bylaws, politics of parking pricing, and housing policy.

Optimization of On-Street Parking Charges Based on Price Elasticity of the Expected Perceived Parking Cost

Price discrimination is widely employed to regulate on-street parking behaviors to provide better service to users, and the prices are usually set according to the occupancy of parking spaces without direct consideration of user perception. A binary logit-style choice model is built to describe the parking choice between on-street parking and off-street parking. A new index, named the price elasticity of expected perceived parking cost, is proposed to evaluate users’ response to parking charge. Based on the theory of second-degree price discrimination, three user types are defined according to the parking duration, namely, the preferred users, the neutral users, and the non-preferred users. The optimized parking prices are calculated by the proposed index. A case study of Guangzhou’s on-street parking is presented. It is found that the current pricing scheme for Type-I Zones (High Demand Zones) is reasonable, while the pricing scheme for the Type-II Zones (Low Demand Zones) does not achieve the objectives of usage optimization of on-street parking spaces. An optimized price scheme for the Type-II Zones is proposed to achieve the usage optimization of on-street parking spaces for short-term parking.