Parking Search Time Research Articles

Comparison of probabilistic cruising-for-parking time estimation models

Models of parking search time predominantly rely on the binomial approximation, the calculation of which is dependent solely on the occupancy of the parking network. This paper sheds light on the importance of accounting for competition between parking searchers and the effects of dynamically changing occupancy. To this end, we investigate the accuracy of two probabilistic models, namely the binomial approximation and a model proposed by Cao and Menéndez (CaM). We utilize an agent-based microscopic parking simulation model as a benchmark for comparison purposes. Our findings highlight the significance of considering the competitive dynamics among drivers in the quest for parking spaces and the fluctuations in parking occupancy rates over short time intervals. We highlight the ranges of occupancies for which the binomial approximation and the CaM model provide more reliable estimations of parking search times. The CaM model provides, in general, more accurate estimations of search times since it accounts for various parking network characteristics such as competition and varying occupancy. The binomial approximation underestimates parking search times more substantially at high occupancies with a large number of searching vehicles, whereas the CaM model performs worse for lower spacing between parking spots and lower parking occupancies. Insights from this research are then used to discuss policy implications of accounting for competition and short-term occupancy variations when designing either parking models or parking-related control strategies.

Ordinal Regression Model of Parking Search Time

Parking search reduces the quality of parking service, as well as traffic network level of service, due to additionally generated traffic. Parking search also entails other negative effects, primarily ecological, social and economic. Even though the importance of this problem has been noted in the past, there is an impression that this issue has not been sufficiently researched and should be additionally analysed in order to properly understand this phenomenon. Therefore, the aim of this paper is to study the factors affecting parking search time that can be influenced through a set of parking management measures. In this paper, an ordinal regression model was developed to estimate these parameters and it was fitted using empirical data collected by interviewing drivers. Main model results show that parking occupancy has the highest impact upon the value of parking search time, indicating the significance of defining proper policies and measures aimed at reaching targeted parking occupancy. Parking frequency is the second parameter observed to be significant, demonstrating the importance of implementing proper parking information systems.

Improving urban viability through smart parking

In Smart Cities, vehicles can share intentions and retrieve information through IoT (Internet-of-Things) devices. This work proposes a reservation system that exploits communication between vehicles and city infrastructure to reduce the time a road user needs to find parking. The system is designed to manage the coexistence of next-generation vehicles, that communicate with city infrastructure and traditional vehicles that don't. We reconstructed an IoT-instrumented urban area using the MATSim simulator to evaluate the system. The proposed system reduces the parking search time of next-generation vehicles without disadvantaging traditional vehicles, making it a candidate for scenarios where both vehicle types coexist.

Parking Search in the Physical World: Calculating the Search Time by Leveraging Physical and Graph Theoretical Methods

Parking plays a central role in transport policies and has wide-ranging consequences: While the average time spent searching for parking exceeds dozens of hours per driver every year in many Western cities, the associated cruising traffic generates major externalities, by emitting pollutants and contributing to congestion. However, the laws governing the parking search time remain opaque in many regards, which hinders any general understanding of the problem and its determinants. Here, we frame the problem of parking search in a very generic, but mathematically compact formulation that puts the focus on the role of the street network and the unequal attractiveness of parking spaces. This problem is solved in two independent ways, valid in any street network and for a wide range of drivers’ behaviours. Numerically, this is done by means of a computationally efficient and versatile agent-based model. Analytically, we leverage the machinery of Statistical Physics and Graph Theory to derive a generic mean-field relation giving the parking search time as a function of the occupancy of parking spaces; an expression for the latter is obtained in the stationary regime. We show that these theoretical results are applicable in toy networks as well as in complex, realistic cases such as the large-scale street network of the city of Lyon, France. Taken as a whole, these findings clarify the parameters that directly control the search time and provide transport engineers with a quantitative grasp of the parking problem. Besides, they establish formal connections between the parking issue in realistic settings and physical problems. Funding: This work was supported by IDEXLYON (IDEXLYON 2020–2021); Institut Rhonalpin des Systèmes Complexes (IXXI) (Vulnerabilite). Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2023.1206 .

Tracking Parking Search and Occupancy in Zurich

This article describes how parking search times and occupancy in a highly attractive area in central Zurich (Switzerland) develop on a weekday morning when a farmer’s market takes place on Bürkliplatz, a central market place inside the study area. Individual vehicles were tracked by bike, drivers were interviewed about their parking behaviour and their route was tracked using GPS. Additionally, the parking occupancy was registered every fifteen minutes during a five-hour time period. A connection between market opening hours and parking dynamics within the perimeter was observed. Drivers usually overestimate their parking search duration.

Development of a Data-Driven On-Street Parking Information System Using Enhanced Parking Features

On-street parking information (OSPI) systems help reduce congestion in the city by lessening parking search time. However, current systems use features mainly relying on costly manual observations to maintain a high quality. In this paper, on top of traditional location-based features based on spatial, temporal and capacity attributes, vehicle parked-in and parked-out events are employed to fill the quality assurance gap. The parking events (PEs) are used to develop dynamic features to make the system adaptive to changes that impact on-street parking availability. Additionally, a parking behavior change detection (PBCD) model is developed as an OSPI supplementary component to trigger potential parking map updates. The evaluation shows that the developed OSPI availability prediction model is on par with state-of-the-art models, despite having simpler but more enhanced and adaptive features. The foundational temporal and aggregated spatial parking capacity features help the most, while the PE-based features capture variances better and enable adaptivity to disruptions. The PE-based features are advantageous as data are automatically gathered daily. For the PBCD model, impacts by construction events can be detected as validation. The methodology proves that it is possible to create a reliable OSPI system with predominantly PE-based features and aggregated parking capacity features.

Cruising for Parking with Autonomous and Conventional Vehicles

Parking is a cumbersome part of auto travel because travelers have to search for a spot and walk from that spot to their final destination. This conventional method of parking will change with the arrival of autonomous vehicles (AV). In the near future, users of AVs get dropped off at their final destination and the occupant-free AVs search for the nearest and most convenient parking spot. Hence, individuals no longer bear the discomfort of cruising for parking while sitting in their vehicle. This paper quantifies the impact of AVs on parking occupancy and traffic flow on a corridor that connects a home zone to a downtown zone. The model considers a heterogeneous group of AVs and conventional vehicles (CV) and captures their parking behavior as they try to minimize their generalized travel costs. Insights are obtained from applying the model to two case studies with uniform and linear parking supply along the corridor. We show that (i) CVs park closer to the downtown zone in order to minimize their walking distance, whereas AVs park farther away from the downtown zone to minimize their parking search time, (ii) AVs experience a lower search time than CVs, and (iii) higher AV penetration rates reduce travel costs for both AVs and CVs.

What drives the utility of shared transport services for urban travellers? A stated preference survey in German cities

The supply of shared mobility solutions has been increasing during the last years, so has the popularity of Mobility-as-a-Service. Both promise an easy access to and usage of shared vehicles or shared rides. Nevertheless, usage of these services remains low in German cities. Hence, the question arises: what determines the utility of travellers regarding shared modes and how is this different to conventional modes? To answer this, we conduct a stated preference experiment amongst 1,445 respondents (8,670 observations). The sample is drawn from residents of the 83 largest cities in Germany. We consider four shared (e-scooter-, bike-, carsharing, and ridepooling) and three conventional modes (walking, private car, and public transport). We estimate a mixed logit model and calculate the respective value of travel time (VoT) as well as the value of access, egress, and parking search time. The importance of the individual attributes is analysed drawing on a part-worth analysis. Further, we calculate average treatment effects to show simulated mode-choice probability changes. We find that costs are more important than travel time for carsharing and ridepooling whilst they are equally important for the remaining modes. For shared services, access is more important than egress. Moreover, among the shared services, e-scootersharing shows the highest VoT (23.73 EUR/h), followed by bikesharing (18.53 EUR/h). Finally, cost changes to private cars and public transport show the highest simulated shifting potential with carsharing profiting most from cost increases in these two modes.

Cluster analysis of parking behaviour: A case study in Munich

Estimates show that vehicles cruising for on-street parking contribute to 30% of urban traffic congestion. On-street parking information (OSPI) systems are increasingly becoming a more popular service to help lessen the on-street parking search time and consequently reduce congestion. However, despite the service offerings of these prediction models, the on-street parking behaviour of people in cities have not been studied to the same magnitude. The lack of appropriate empirical parking data is one main reason. This study focuses on the analysis of parking behaviour by capturing the on-street parking dynamics, which can give a better insight on a city’s parking contextualization. The case study examined is the parking behaviour dynamics within Munich by inferring from parked-in and parked-out events data from vehicles. A two part clustering analysis was conducted: (1) agglomerative clustering on the temporal trend of parking dynamics (TTPD) and (2) a two-stage DBSCAN – K-means clustering on the parking duration information. The results show that using the methodology introduced, the parking behaviour within the city can be obtained using this unsupervised learning approach.

The impacts of shared autonomous vehicles on car parking space

It is anticipated that Shared Autonomous Vehicles (SAV) may revolutionise the way people travel as it may lead to less ownership of vehicles and a gradual increase in demand responsive mobility. These vehicles are likely to able to park far away from popular destinations after they drop off their clients. It is, therefore, capable of releasing parking spaces around central business districts while cutting down parking search time and cost. But the magnitude at which these spaces will be released in relation to SAV fleet size is not currently known. This work therefore seeks to understand this relationship using the University of the West of England, Frenchay campus characterised by 23 carparks of varying size – as a case study – to quantitatively understand these impacts. This was done by simulating 2181 parking slot within the campus; motor traffic (SAVs and non-SAVs); and the route networks from the 3 entry stations – East, North and Longmead entrances – to each parking slot. Carpark preference data was obtained and analysed. It formed the context on which drivers choose a parking space within the system. These data alongside with the graphical features of the Frenchay campus were imported into ARENA simulation toolkit: a discrete event-based simulation software. Results indicated the obvious that as SAV fleet size increases, spaces released also increases but in addition, individual carpark reacted dynamically in response to the demand on neighbouring carparks. It also showed that there is currently an excess of parking supply compared to the number of vehicles entering the campus daily. It further discusses the potential of multiple occupancy SAVs to further release all parking spaces on the Frenchay campus. The work concludes with the potentials for this research to be extended to a citywide approach with a special interest in the ability for SAV to release kerb parking spaces.

SPATIALLY-EXPLICIT TOOLSET FOR ESTABLISHING AND ASSESSING HETEROGENEOUS PARKING PRICES IN THE SMART CITY

Abstract. Cruising for parking stems from local mismatch between the patterns of demand and supply for parking, which reflect urban heterogeneity in a complex non-linear way. We propose ParkSage, a set of spatially-explicit algorithms for establishing spatially heterogeneous parking prices that guarantee target levels of occupation and parking search time. We apply ParkSage for establishing overnight parking prices that guarantee 85% occupation in the Israeli city of Bat Yam. We investigate the resolution of pricing units necessary for reducing cruising and demonstrate that pricing by transport analysis zones (TAZ) produces similar benefits to the commonly proposed pricing by road links, in addition to being more comprehensible to drivers.

Electric vehicles and residential parking in an urban environment: Results from a stated preference experiment

This paper presents the results of a preference survey of 1545 respondents’ willingness to purchase electric vehicles (EVs) in Philadelphia. We pay particular attention to respondents’ willingness to pay for convenient charging systems and parking spaces. If the value of dedicated parking substantially outweighs the value of convenient charging systems, residential-based on-street charging systems are unlikely to ever be politically palatable. As expected, respondents are generally willing to pay for longer range, shorter charging times, lower operating costs, and shorter parking search times. For a typical respondent, a $100 per month parking charge decreases the odds of purchasing an EV by around 65%. Across mixed logit and latent class models, we find substantial variation in the willingness to pay for EV range, charge time, and ease of parking. Of note, we find two primary classes of respondents with substantially different EV preferences. The first class tends to live in multifamily housing units in central parts of the city and puts a high value on parking search time and the availability of on-street charging stations. The second class, whose members are likelier to be married, wealthy, conservative, and residing in single-family homes in more distant neighborhoods, are willing to pay more for EV range and charge time, but less for parking than the first group. They are also much likelier to consider purchasing EVs at all. We recommend that future research into EV adoption incorporate neighborhood-level features, like parking availability and average trip distances, which vary by neighborhood and almost certainly influence EV adoption.

Survey to Explore Behavior, Intelligent Transportation Systems Needs, and Level of Service Expectations for Student Parking at a University Campus

University campuses generate large volumes of traffic, with students forming the largest user class with distinctive trip characteristics. This study examined the parking choices of students at a large urban campus. A student survey was conducted with 1,022 respondents to collect data on students’ parking behavior, intelligent transportation systems (ITS) needs, and level of service (LOS) expectations. The survey results included home-to-campus mode choices, arrival time buffers, parking locations, “last-mile” mode choices, and factors that influence the type of permit to purchase. Online permit sales, flexible parking zones, and real-time parking lot availability information were the three most preferred ITS enabled services. Using the survey feedback, new LOS criteria for parking search time are proposed, which range from up to 2 min for LOS A, to longer than 15 min for LOS F. This research makes several contributions. The survey results not only contribute to a better understanding of student transportation choices and parking patterns on campus, but the list of preferred ITS enabled services also gives the university valuable user feedback on where to improve the campus parking experience. More importantly, the LOS criteria for parking search time provides a new tool for traffic engineers to measure the quality of service of a parking facility. Although the survey was conducted on a particular campus, information gathered for the ITS enabled services for parking and LOS criteria for parking search time are likely to be transferable to other university campuses.

Enhancement of Parking Management System in Cairo using Smartphones

Over the past two decades, traffic authorities in many cities have developed so-called parking guidance and information systems (PGI) to help drivers to find the available parking spaces. In Cairo, the capital of Egypt, the PGI has been recently implemented in 2015. This research mainly aims to develop and assess a new approach for introducing parking information based on a smartphone application that can inform the users about the number of available parking spaces within the studied garages not only in the real- time but also in the near future based on historical data. The need for such an application was investigated before deployment by a questionnaire survey, while post-deployment feedback was used to evaluate the developed application as a tool to enhance and overcome the current parking management system defects. In addition, the current parking management system has been evaluated based on drivers’ opinion. The results showed that despite the PGI system has been deployed since 2015, only 70% of the participants were aware of this system. Out of them, 23% used the system. The main purpose of this is that the VMSs were not working sufficiently and some of them are not clear enough to the drivers. Around 46% of the interviewed drivers stated that when they didn’t find a space in the garage, they usually do not find an alternative parking space easily and they waste around 15 to 20 minutes searching for available parking spaces, while the average search time for those who are using the system was between 5 to 10 minutes. Finally, 99% of the participants believe that using more advanced parking guidance system through smartphone application will be more useful. A smartphone application has been developed as an advanced alternative tool for providing users with the number of available parking spaces not only in the current time but also in the near future time. This was done by developing predictive models for parking spaces availability at each garage over the daytime using the collected data. This application was evaluated based on the feedback of about 70 participants who used the application. The feedback analysis showed that 82% of the users were satisfied with the application, only one out of 70 participants have complained about the ease of use. Also, 89% of the participants have agreed that the application help in reducing parking search time with an overall average of 8 minutes.

Yellow Brick Roadmap to Demand-Based Parking Pricing: Findings from Washington, D.C.

Washington, D.C. (the District) has been a national leader with its progressive approach to parking management. Owing to the District’s strong housing and employment growth over the past decade, the District Department of Transportation (DDOT) needs a program to balance the competing parking needs of residents, commuters, visitors, and businesses. Using Federal funding from the Federal Highway Administration (FHWA) Value Pricing Pilot Program, DDOT planned and implemented a demand-based parking pricing pilot program in the Penn Quarter and Chinatown neighborhoods. The results of the pilot program confirmed that demand-based pricing programs can be both cost-efficient and effective, and highlighted a path to expanding demand-based pricing Districtwide. Using lessons learned from this project, practitioners will be better prepared to plan their own demand-based pricing programs, positioning themselves to effectively balance parking supply and demand in their own communities. The paper discusses the impacts of demand based pricing on a range of metrics such as parking search times, cruising, occupancy, and length of stay. It also assesses the impacts of the strategies on the larger transportation system and the study area.

Modeling the choice to switch from traditional modes to ridesourcing services for social/recreational trips in Lebanon

This study investigates the current and potential uptake of ridesourcing services, such as Uber and Careem, by the students of the American University of Beirut, Lebanon. A hybrid choice model is developed to predict the switching choice from traditional modes of transport to ridesourcing services for social/recreational trips made by these students in Lebanon. Data are provided by a web-based survey that includes revealed and stated preferences, besides demographics. It is found that the switching choice is determined by several observed factors, such as door-to-door travel time, waiting time for pick-up, and one-way fares, in addition to a latent variable that captures individual differences in perceptions and attitudes towards ridesourcing services. A base switching probability from traditional modes to ridesourcing services (calculated under a base scenario representing realistic values of the attributes of ridesourcing services if the latter were used to make the most recent social/recreational trip) is estimated to be 0.22. This probability is expected to reach 0.31 under a forecasted policy scenario consisting of 40% reduction in ridesourcing fares. Car users will be more sensitive to switch to ridesourcing services for their social/recreational trips if the ridesourcing fare reduction (40%) is associated with restricted parking conditions consisting of (a) 100% increase of parking fees from actual prices, and (b) 20-minute increase of parking search time and parking time from the actual car travel time. In this case, the resulting switching probability is expected to reach 0.38. By using the estimated choice model to forecast policy scenarios as such, this study can guide planners, policymakers, and service operators to prioritize effective policies in response to the behavioral change caused by the diffusion of innovative transport services and technologies. The study also contributes to a better understanding of the uptake of ridesourcing services in developing country contexts where public transport services are often inadequate.

Parking policy as an instrument for reducing passenger cars’ harmful emissions

Passenger car related harmful gas emissions have become one of the most significant problems that cities worldwide are facing during the last thirty years. One of the approaches to harmful emissions reduction is certainly preventing or reducing traffic congestion in central urban areas and high trip-attraction zones. One of the congestion influence factors, i.e. (already low) traffic flow speed further reduction, is parking, that is search for a vacant on-street parking space. Since parking search is primarily due to the high occupancy of parking spaces, (better) parking management can affect its reduction. The paper presents the impact of parking time limit and parking price on the passenger cars’ total parking search (cruising for parking) time, as well as their related harmful gases emissions. A survey presented in this paper was carried out in the central urban area of Belgrade, the so-called red parking zone, in order to examine the passenger car users’ preference regarding the parking time limit and parking price variations. The potential benefits in the field of air pollution and fighting climate change due to the implementation of a balanced parking policy and on-street parking price were analysed in this paper.

The effectiveness of parking policies to reduce parking demand pressure and car use

Evaluating the effectiveness of parking policies to relieve parking demand pressure in central areas and to reduce car use requires an investigation of traveler responses to different parking attributes, including the money and time costs associated with parking. Existing parking studies on this topic are inadequate in two ways. First, few studies have modeled parking choice and mode choice simultaneously, thus ignoring the interaction between these two choice realms. Second, existing studies of travel choice behavior have largely focused on the money cost of parking while giving less attention to non-price-related variables such as parking search time and egress time from parking lot to destination. To address these issues, this paper calibrates a joint model of travel mode and parking location choice, using revealed-preference survey data on commuters to the University of Michigan, Ann Arbor, a large university campus. Key policy variables examined include parking cost, parking search time, and egress time. A comparison of elasticity estimates suggested that travelers were very sensitive to changes in egress time, even more so than parking cost, but they were less sensitive to changes in search time. Travelers responded to parking policies primarily by shifting parking locations rather than switching travel mode. Finally, our policy simulation results imply some synergistic effects between policy measures; that is, when pricing and policy measures that reduce search and egress time are combined, they shape parking demand more than the sum of their individual effects if implemented in isolation.

Market Segmentation Analysis of Commuter Parking Relative to Shared Private Residential Parking Spots

Using shared private residential spots for commuter parking is a relatively new parking method in China. It promotes the utilization of residential parking resources, while reducing parking supply pressure during the day. This study adopted the market segmentation approach for analyzing this new method, based on surveying parkers’ socioeconomic and attitudinal factors. The Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to identify various influential factors. The results reveal that attitudes and socioeconomic heterogeneity influence parkers’ willingness and attitudes, which play a more significant role in this process. Moreover, the K-means was used to segment the market. Finally, six segments with distinct characteristics were identified, based on a willingness to utilize private residential spots for commuter parking, sensitivity to parking search time, walking distances, the economy, environmental awareness and parking payment method. Suggestions for marketing promotion for parkers of each segment are also discussed. The findings in this paper will help clarify the mechanism behind commuter-parking location choices and benefit third parties by enabling them to establish precise marketing strategies.

Cruising and on-street parking pricing: A difference-in-difference analysis of measured parking search time and distance in San Francisco

When on-street parking is scarce, the cost of parking includes the extra time and fuel spent searching for a parking space (or cruising). Cruising also unnecessarily contributes to local congestion, vehicle emissions, air pollution, and climate change. The theoretical literature shows that these social costs can be reduced, or even eliminated, if high-quality information on the demand for and supply of parking is used to set parking prices at optimal levels. Not surprisingly, cities plagued by parking shortages, congested streets, and limited financial resources are interested in parking policies that reduce cruising and improve the efficient use of their existing parking and roadway infrastructure. The current study sheds light on the effect of the San Francisco parking pricing program (known as SFpark) on curbside parking search time and distance in urban neighborhoods on non-commuter parking. The study differs from previous empirical evaluations of similar parking pricing programs in its use of direct field measurements of parking search time and distance, rather than simulated data or proxy variables, such as parking availability. We use generalized mixed effect difference-in-difference models with data collected before and after the implementation of SFpark in both treatment and control areas to estimates effects of the San Francisco smart parking project, most importantly the demand responsive parking pricing scheme. The models control for time effects by using data from a separate control area, as opposed to using variables such as block face parking price and employment. The results suggest a significant reduction in average parking search time and distance due to SFpark. Average parking search time and distance declines by approximately 15% and 12%, respectively, from the control to the treatment areas.