Parking Pricing Research Articles

Admission and pricing optimization of on-street parking with delivery bays

We analyze a parking lot, modeled as a loss queue, with passenger and delivery vehicles. The arrival process of delivery vehicles is exogenous, while that of passenger vehicles is a function of the parking price rate and accessibility. The aim of the parking operator is to maximize the revenue generated from passenger vehicles while providing a sufficient service level for delivery vehicles, in terms of their probability to find an available parking spot. Two levels of control are exercised: pricing and admission. From a Markov decision process approach, we prove that the optimal policy is a state-dependent reservation threshold policy that randomizes in at most one state. When some parking spots should be reserved for delivery vehicles, the price rate is selected to saturate the service level constraint, whereas when it is optimal not to restrict the parking lot accessibility, the price can also be selected as the unique local maximum of the revenue or to incentivize all potential passenger vehicles to arrive. Pricing should be used as a primary tool to control the flow of passenger vehicles. In complement, admission control is exercised with a limited use of reservation only when the service level guarantee for delivery vehicles is high.

Walkability, Adaptability, Accessibility and Parking in Rapidly Growing Cities: The Case of Kigali City

Walking, accessibility, and parking spaces are the major pressing challenges today in rapidly growing cities in the global south. Despite the fact that automobiles in these cities increase daily, many cities still have not coped with these changes due to various factors, such as a lack of appropriate technology to react to this. Therefore, this study aims to examine how walkability, accessibility, and parking spaces are taken into consideration in rapidly growing cities, using Kigali City as a specific case. Primary data were collected through observations and interviews that were conducted through the use of structured questions addressed to one administrative official in charge of road and bridge construction and maintenance in the infrastructure management unit at the district level. Secondary data were gathered from existing literature similar to this study. Moreover, 75 city residents from three districts (i.e., 25 residents from each district) were chosen randomly. Findings have revealed that pedestrian walkways are used 24/7 by people going to work, hospitals, buying commodities, and playing various games. In addition, the study has shown that the CHIC building parking yard can accommodate more than 400 cars when fully utilized, and the parking price is 200 Frw (0.2 USD) per minute. This parking space had the option to go in and out on both sides of the parking lot. Regarding accessibility, there is Kanombe International Airport, which makes the city accessible to international travelers, and Bugesera International Airport, which is 25 km south-east of Kigali and under construction. Additionally, the lack of smart parking and protected walkways against car accidents on commuters were highlighted by 77% of the respondents as the major emerging challenges that hold back walkability and parking in Kigali City. In conclusion, transformation in Kigali City is taking place, and the city is trying to cope with emerging technologies by making the city accessible, walkable, and parking spaces available in front of high-raised buildings as well as commercial areas. This goes hand in hand with the use of IOT and further emerging technologies to improve walkability in urban areas as well as adaptability, especially for rapidly developing cities like Kigali City.

Macroscopic parking dynamics and equitable pricing: Integrating trip-based modeling with simulation-based robust optimization

Characterizing the integrated traffic and parking dynamics in a coupled road-parking system is challenging due to the interactions between parking and non-parking vehicles and the presence of cruising-for-parking. In this paper, we propose a new trip-based macroscopic model of parking considering various sources of user heterogeneity that are key to modeling parking. The macroscopic or network fundamental diagram is used to describe the accumulation-based network speed at which vehicles are traveling. An efficient event-based simulation algorithm is proposed as the resolution scheme for the trip-based model. Numerical results reveal that after calibration, the proposed trip-based model yields similar results to those obtained using an accumulation-based approach and microscopic simulation. Taking the trip-based model as the simulation engine, we formulate a simulation-based robust optimization problem of equitable duration-based parking pricing. To solve this problem with simulation stochasticity, a new global optimizer termed NoisyDIRECT is proposed as an open-source solution algorithm. This algorithm automatically identifies the level of simulation stochasticity for each decision vector across the search space, based on which the variable number of simulation runs per point is determined. Thus, the computational resource can be better allocated so that the optimal solution can be found in a more computationally efficient and reliable manner. Numerical results demonstrate that NoisyDIRECT provides better solutions than those yielded by fixed-number sample-path optimization and non-robust optimization methods.

Site Selection of Car Parking with the GIS-Based Fuzzy Multi-Criteria Decision Making

Rapid motorization and uncertainty in urban growth patterns make parking space management a serious task, especially in middle-income developing countries, and this has severe social, economic, and environmental repercussions including increased congestion, crash frequency, fuel and time consumption, and air pollution. Due to the complexity of the urban transportation issue and the wide variety of variables involved, a multicriteria assessment is essential. This study used fuzzy logic and geographical information systems (GIS) to develop a multi-criteria decision making (MCDM) model for managing parking in Shiraz’s central business district (CBD). The literature was mined for information on the variables that affect parking site placement, and a poll of experts ([Formula: see text]) was used to determine their relative importance. The distance to travel attraction centers, distance to roads, land price, population density, and available land for multi-storey parking were among the factors considered. Meanwhile, the parking space shortage for each TAZ is calculated by subtracting the estimated parking space supply from the estimated parking space demand. An overlay of these two layers distinguishes locations that are in parking shortage zones and also meet multiple criteria. The results may aid policymakers in controlling parking demand by pinpointing the most promising places for investment.

DyPARK: A Dynamic Pricing and Allocation Scheme for Smart On-Street Parking System

In-advance availability of parking information plays an important role in parker/traveller decision-making for parking, curbing congestion, and managing parking lots efficiently. Specifically on-street parking poses many challenges compared to the off-street ones. Many users such as, store owners, municipal authorities, and police demand slots for on-street parking Free of Charges (FoC) for a short duration. In last few years, parking authorities collected data to attract the attention of researchers to present data-centric solutions for various problems such as, minimization of parking prices, maximization of revenue, and balacing the congestion at parking lots associated with smart parking systems. Motivated from the aforementioned problem, this paper proposes a scheme based on machine learning and game theory for dynamic pricing and allocation of parking slots in on-street parking scenarios. The dynamic pricing and allocation problem is modeled as Stackelberg game and is solved by finding its Nash equilibrium. Two types of Parking Users (PUs), i.e., Paid Parking Users (PPUs) and Restricted Parking Users (RPUs) are considered in this work. RPUs avail parking slots FoC once a day. PPUs compete to minimize the prices, and RPUs compete to maximize the FoC granted duration. The Parking Controllers (PCs) compete to maximize revenue generated from PPUs and to minimize total FoC parking duration granted to the RPUs. The random forest model is used to predict occupancy, which in turn is used to generate parking prices. Seattle city parking and its prices data sets are used to predict occupancy and to generate prices, respectively. In order to test the performance of communication system, the proposed DyPARK Pricing and Allocation Scheme (PAS) is compared with its four variants and is found worth. The proposed scheme is also compared with other state-of-the-art schemes using various performance evaluation metrics. Simulated results prove the superiority of the proposed scheme in comparison to the other state-of-the-art schemes.

Overview of Walking Rates, Walking Safety, and Government Policies to Encourage More and Safer Walking in Europe and North America

Walking is the most sustainable means of daily travel for short trip distances and is a key component of the overall transport system. This paper documents variation in walking rates among countries, cities in the same country, and in different parts of the same city. Our international analysis of official government statistics shows that walking rates are highest for short trips, higher for women than for men, decline with increasing income, and remain constant as age increases. Walking fatality rates are much higher in the USA compared with the other countries we examined, both per capita and per km walked. Government policies that would increase walking rates while improving pedestrian safety include: integrated networks of safe and convenient walking infrastructure; roadways and intersections designed for the needs of pedestrians; land-use regulations that encourage mixed uses and short trip distances; lower city-wide speed limits and traffic calming in residential neighborhoods; reduced supply and increased price of parking; traffic laws that give priority to pedestrians; improved traffic education for motorists and non-motorists; tax surcharges on large personal vehicles; and strict enforcement of laws against drink and distracted driving. Five decades of success with these policies in many European cities provide practical examples for car-oriented cities to follow, especially in North America.

Designing intelligent public parking locations for autonomous vehicles

Looking for parking spaces in crowded areas can be a stressful and time-consuming challenge. With the arrival of technologies like autonomous vehicles (AVs), users can park far away from their destinations. AVs can move from the city center, which has high congestion, into a parking spot outside the area, and thus have more flexibility in choosing the parking location. This study aims to provide a rent bid for the daytime parking of AVs that considers urban land use to evaluate parking strategies possibly chosen by AV users. We determine an actual parking demand function by integrating individual preferences into a p-median problem that controls user optimality. Combining all these together, a novel dynamic optimization formulation is proposed to design the location of parking facilities for AVs that examines the driver's parking preference including rent bid, waiting time for searching parking lots, and travel costs. A Lagrangian relaxation (LR) algorithm is presented to solve the stochastic parking location problem that integrates a reliability strategy to balance demand and supply for parking spaces. The average gap between the exact and the LR solution is about 6% which is very reasonable. We provide a detailed case study of our model with real data generated from daily household travel in New York City. The results show that the average parking price is reduced by 34%, and the average empty trips by AVs decrease by 22% using the proposed public parking strategy.

Fast Way to Predict Parking Lots Availability: For Shared Parking Lots Based on Dynamic Parking Fee System

This study mainly focuses on the estimation calculation of urban parking space. Urban parking has always been a problem that plagues governments worldwide. Due to limited parking space, if the parking space is not controlled correctly, with the city’s development, the city will eventually face the result that there is nowhere to park. In order to effectively manage the urban parking problem, using the dynamic parking fee pricing mechanism combined with the concept of shared parking is an excellent way to alleviate the parking problem, but how to quickly estimate the total number of available parking spaces in the area is a big problem. This study provides a fast parking space estimation method and verifies the feasibility of this estimation method through actual data from various types of fields. This study also comprehensively discusses the changing characteristics of parking space data in multiple areas and possible data anomalies and studies and explains the causes of data anomalies. The study also concludes with a description of potential applications of the predictive model in conjunction with subsequent dynamic parking pricing mechanisms and self-driving systems.

Managing parking with progressive pricing

Parking supply and demand are often imbalanced in urban areas, causing adverse consequences such as excessive search times and long walking distances. Many parking authorities price parking as a demand management strategy by charging either a fixed daily fee or an hourly price for parking. An emerging alternative is progressive pricing, whereby drivers pay an hourly price that increases if their tracked parking duration is longer than a predetermined threshold. This study investigates the optimal design of progressive pricing for revenue and social welfare maximization when there are two market segments. We study equilibrium properties of progressive pricing and show that its optimal design for revenue maximization segmentizes the demand. In contrast, progressive pricing does not improve social welfare over hourly pricing, because any additional surplus accrued by drivers is offset by the revenue of the parking authority. We develop a micro-simulation model of the City of Toronto’s downtown core with parking capability, and show that progressive pricing lowers average parking occupancy and search time in high demand parking clusters by 5.6% and 12.5%, respectively, compared to hourly pricing.

Nobody can pay the right price for parking: It’s time to change the system

The existing parking system assumes that drivers can pay the right price for parking, but we find the opposite in a field study (N = 567) and a survey study (N = 474). Drivers either overpaid or underpaid for parking at on-street parking meters 94 % or 98 % of the time, in the survey study and field study respectively, for 20–30 min on average in the field study. Such misalignment between parking payments and presumed price can mask the price signal and reduce its ability to influence drivers’ behavior and downstream environmental consequences. We also find that the public prefers an alternative, intelligent payment system that automatically collects on-street parking fees based on actual parking durations. These findings provide evidence for widespread parking payment inaccuracy and suggest an opportunity for public policy and technology to address the problem. This research offers important insights for transportation and planning professionals on the future of parking.

Car Users Willingness to Change Travel Mode in Response to On-street Parking Pricing: A Case Study of Dhaka City

Dhaka is facing severe on-street parking challenges as car ownership emerges. This situation has led traffic congestion not only in local roads but also in collector and arterial roads. Parking plays a significant function in the management of traffic and congestion. In Dhaka city most of the on-street parking spaces are free which persuades car users to park their car randomly. The objective of this research is to examine car users’ behavior if on-street parking pricing is implemented. A questionnaire survey was conducted in arterial, feeder (or collector) and in local roads at 16 locations in Dhaka city. Attention was drawn to confirm that the survey could deal with either professional drivers o r motorists driving their own car as respondents. A multinomial logistic regression model was developed by using the data collected from 500 respondents. Based on the analytical results along with in-depth on-site investigations it was found that the probability of changing mode due to on street parking pricing is insignificant. Result demonstrates that educational qualification is the most important variable for switching mode from car to other if on-street parking charge is applied. Most of the users whose parking is associated with education and work trips will not change their transport mode if on-street parking charge is put into operation. Those trips are most closely associated with travel in peak periods and largely responsible for congestion. Car users usually face difficulties to find space for parking. Journal of Engineering Science 13(2), 2022, 31-41

A parking pricing scheme considering parking dynamics

A parking pricing scheme considering parking dynamics

Joint Optimization of Facility Layout and Spatially Differential Parking Pricing for Parking Lots

The parking cruising problem can be attributed to the unbalanced spatial parking space occupancy in the parking lot with multiple trip purposes and high traffic volumes, and it has already become an obstacle to improving travel efficiency. This study aims to investigate the reasons for the unbalanced occupancy of parking spaces and develop strategies to achieve spatial equilibrium by reducing parking cruising time. A field survey is first conducted to analyze the factors influencing parking space choice behavior, such as searching distances, walking distances, parking price, and parkers’ attributes. Based on the survey, a mixed logit model is calibrated to reveal parking space choice patterns of different parkers. Afterwards, the parking lot is divided into three zones based on searching distances and walking distances, using the K-means algorithm. According to the results, a simulation-based optimization model is then established, for minimizing the variance of average occupancy. An optimal pricing strategy is obtained at last. A case study in Nanjing, China, is conducted to verify the feasibility and rationality of the proposed method. The difference between weekdays and weekends is explored. The results show that, with the optimal strategies, the standard deviation of average occupancy in each zone decreases by 39.52% during weekends and 42.29% during weekdays. Finally, political implications and recommendations on the layout design of facilities and the adoption of differential parking pricing strategies are provided.

Analysing the Impacts of Parking Price Policies with the Introduction of Connected and Automated Vehicles

It is known that parking prices can affect multiple characteristics such as traffic flow, delays, and congestion. Connected and autonomous vehicles (CAVs) do not need drivers and may return to the origin, if necessary, avoiding parking fees. However, if the destination area is not near the origin, it may not be economically viable to return. Hence, in the present study, four scenarios were tested to find the optimal parking strategy: (i) enter and park inside area (ii) enter, drop off and return to the origin (iii) enter, drop off and return to outside parking and (iv) enter and drive around. Different parking prices were used to determine the suitable option. The ‘Balanced’ scenario with multiple parking choices was found to be better compared to other scenarios, where the flow and travel distance were moderately (-19 and -26.3%) affected. Emissions were reduced significantly with CAVs.

The impact of parking pricing on mode choice

Over the past decades global urbanization has led to the rapid growth of cities. Mobility needs of inhabitants along with their emphasised car dependency have led to the constant increase in car ownership, while transport infrastructure hasn't followed this growth sufficiently. The consequences of this supply and demand imbalance are the numerous negative effects, such as: traffic congestion, time delays, illegal parking, emissions of pollutants, frustrations of drivers, etc. Having in mind that the essence of these problems lies in excessive car usage, policies contributing to the greater share of more sustainable transport mode are increasingly applied in transport management process nowadays. In this sense, parking policies, particularly parking charging, have proven to be very effective, supporting the implementation of sustainable transport goals in urban areas. It is well-known that parking charge can impact different patterns of user behaviour. For example, it can impact the user's choice of parking location or transport mode, trip destination, etc. Therefore, in order to properly define parking prices, it is necessary to understand the way users respond to them. Despite the importance of this topic, user response to parking policies has not yet been sufficiently examined. This paper aims to partially fill this gap. The aim of this paper is to investigate the parking price impact on user's choice of transport option based on the applied logistic regression, taking into account their socio-economic characteristics as well as trip characteristics. Transport options included the use of passenger car on the one hand and the use of more sustainable transport modes (public transport, taxi, walking) on the other. To fit the model we use data obtained by interviewing the users of one parking garage in Belgrade central area by revealed and stated preference methods.

Оцінка пропускної спроможності міської дорожньої мережі з урахуванням пропозиції паркування

In order to improve the estimation of network capacity for a given urban road system, it is assumed that the impact of destination parking management strategies should be considered in the network capacity estimation model. This study presents an extended road network capacity model that takes into account both the number of parking spaces and parking rates in each traffic zone. The network capacity model is formulated as a two-level programming problem with maximizing the total trip generation at the upper level and the combined trip distribution and traffic assignment (CTDTA) problem at the lower level. In order to reasonably characterize the impact of parking pricing and parking delays due to congestion, the CTDTA model involves two classes of travel demand. An efficient and practical algorithm for solving the two-level network capacity model is presented. Numerical experiments show the advantages of the proposed model and demonstrate the impact of parking supply and pricing on the results of road network capacity assessment.

Assessing the perception of E-scooters as a practical and equitable first-mile/last-mile solution

E-scooters have disrupted and altered the urban mobility landscape. During their introductory period, they have been commonly touted as part of a larger micromobility solution that erases equity barriers and solves the first-mile/last-mile problem. However, few studies in the nascent e-scooter literature have considered these claims. In this study, we used a d-efficient method to design and administer a stated choice experiment (SCE) to students at Portland State University (PSU) (n = 1,968). In the SCE, students were given a three-mode labelled choice set consisting of three travel modes: car, bike, and e-scooter + MAX light rail. (The e-scooter + MAX light rail choice is multimodal, with the e-scooter acting as the first-mile access mode). We generated attitudinal indices using exploratory factor analysis (EFA) in order to control for mode perception. To demonstrate the relationship of travel time and cost in addition to other covariates on mode choice, we estimated a mixed multinomial logit (MMNL) model. We performed elasticity and sensitivity analyses to uncover factors that encouraged first-mile/last-mile e-scooter usage. We then used the model to generate catchment area maps for multimodal trips in the Portland area. Results indicated that e-scooters were lackluster in bringing racial and gender equity in transportation. Additionally, we found that there was no place in Portland where combining an e-scooter and light rail to travel to PSU was most utilitarian compared to bike or private car. This suggests that e-scooters are not perceived as a preferred solution to the first-mile/last-mile problem. Yet, our findings revealed “dials” that can be tweaked through policy measures in order to promote this kind of use, including parking pricing, travel times, fares, and traveler attitudes towards modes. Overall, our analysis of the implementation of e-scooters suggests that their promise is overstated, at least without substantial policy changes to encourage desired use cases.

Reading between the Regulations: Parking Requirements, Planners’ Perspectives, and Transit

This article reports on local planers’ perspectives on metropolitan parking requirements. Workplace parking requirements, which are often in excess of demand, influence parking pricing and urban form. In turn, these affect transit demand and transit service potentials. These connections have led researchers and policy-makers to call for changes, but the perspectives of planners who create the parking requirements are not well understood. Using southern California cities as a study area, a telephone survey revealed that most parking requirements are driven by concerns about traffic mitigation, spillover parking, and risk avoidance. These factors push parking requirements in the direction of oversupply. The article proposes methods to reduce the risk of changing parking requirements and develops a typology of approaches for change. Transit agencies will benefit if they play a role in reforming local parking requirements.

Parking Management and Commuter Rail: The Case of Northeastern Illinois

This article examines the relationship between parking management and commuter rail transit using the Chicago metropolitan area in northeastern Illinois as a case example. Commuter rail transit and parking management are discussed within the broader context of transportation planning in the Chicago metropolitan region. Commuter rail ridership, mode of station access, and parking utilization rates are compared. Related air quality, social equity, and land-use concerns are reviewed.There are no simple solutions to the problem of spillover parking around commuter rail stations. The construction of new parking can be expensive. Raising parking prices may induce spillover parking. Doing nothing to address observed parking shortages may inhibit ridership. Shared parking is an excellent compromise in many situations, but even this is far from a universal solution.

On-Street Cruising for Parking Model in Consideration with Gaming Elements and Its Impact Analysis

On-street cruising by drivers impedes the effectiveness of road traffic conditions and increases energy consumption and environmental impact. Existing models of on-street cruising for parking mainly embody those intrinsic on-street parking factors and disregard the extrinsic impacts from off-street parking gaming factors. This research focused on both the intrinsic and extrinsic elements, especially gaming factors, of off-street parking, i.e., the price of off-street parking, the waiting time of off-street parking, and the difference in walking time between their parking lots to their destinations. On-street cruising for a parking model is reconstructed in this paper in consideration with the equilibrium cruising time, i.e., the maximum tolerable cruise time after evaluating the cost of on-street and off-street parking. Correlation analysis showed that the off-street parking gaming factors were all positively related with the maximum tolerable cruise time. A simulation model was further presented for on-street cruising for the parking model by the cellular automata approach with real-world data. Simulation experiments demonstrated that the average speed of vehicles on the street increases by 9.858 km/h, the average delay decreases by 44.934 s, and the price of on-street parking increases by 4.5 CNY/h. The proposed on-street cruising for parking model proved effective by decreasing the maximum tolerable cruising time to bring significant improvements in average speed, average delay, and on-street cruising vehicles in road traffic flow.