Article

Parking behaviour: The curious lack of cruising for parking in San Francisco

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Abstract

Cruising for parking has long been perceived as a major source of congestion and emissions in urban areas, but recent empirical work suggests that parking may not be as onerous as folklore suggests, and that the amount of vehicle travel attributable to cruising is minimal. In this paper, we reconcile these perspectives through a dynamic programming model of parking search, and empirical insights from a large-scale GPS dataset in San Francisco and the California Household Travel Survey. We first draw a conceptual distinction between parking search, the time between the driver’s decision to park and when a parking space is taken; and cruising, defined as excess vehicle travel due to parking search. In places with little or no through traffic, up to half of traffic can be searching for parking, but cruising can be zero. We then operationalize this distinction through a dynamic programming model. The model predicts that when parking is perceived to be scarce, drivers are more willing to take a convenient available space, even if it is some distance from their destination. Counter-intuitively, scarce parking can even suppress vehicle travel as perceived parking scarcity leads drivers to stop short of their destinations and accept a longer walk. Empirical data from California indicate that neighborhood density (a proxy for parking availability) has little impact on cruising for parking, but increases walk distances from parking locations to final destinations. We conclude that cruising for parking is self-regulating, and that in certain circumstances parking scarcity can even reduce vehicle travel.

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... Cruising has attracted considerable attention, as it creates extra costs for vehicle occupants in terms of search time and cost, and for others in terms of reduced speeds and local congestion, air pollution and noise emissions (Shoup, 2006;Alemi et al., 2018). Recent research (Millard-Ball et al., 2020) conceptually distinguishes between cruising and searching for parking. It assumes that drivers begin to look for available on-street parking earlier in areas where on-street parking is scarce, thus accepting longer walks to their destination. ...
... He inquired into the length of time it took to find a parking space the last time a car was parked near the residence, and the time of day concerned. From this data we take a mean search duration of 10 min between 8 a.m. and 4 p.m. (n = 35), 11 min from 4 to 5 p.m. (n = 29), and as much as 17 min from 5 to 6 p.m. (n = 37) and after 6 p.m. (n = 29), suggesting an increase by about 50% in the late afternoon (see Millard-Ball et al., 2020, for more discussion of parking search and cruising). ...
... Urban policy does little to enforce legal parking. There is a common perception among residents and visitors that parking their car needs excessive cruising, similar to other cities (Millard-Ball et al., 2020). Subjective estimations about typical cruising duration are very likely strongly exaggerated, and may refer to days when the Dortmund football team plays a home match in the nearby stadium and many spectators park their cars in the Kreuzviertel (qualitative evidence in Straub, 2019). ...
Article
High-density inner-city residential neighbourhoods are often characterised by serious parking pressure and illegal parking. We study parking choices in a historical neighbourhood in Dortmund, Germany, using a household survey. Specifically, we look at the availability of and distance to private parking, the use of available private and on-street parking, and search duration. Additionally, we look at simple measures of satisfaction with parking and with the neighbourhood in general. Our results show that available private parking is not necessarily used where there is little control of illegal on-street parking. Furthermore, search durations and distance to cars parked on-street suggest that parking pressure is lower than commonly perceived in the neighbourhood. Private parking is under-utilised to the extent that we estimate that illegal parking can be reduced by 28 to 49% if private parking were consistently used by those who have it available. Even more substantial reductions in illegal parking can be achieved by deviating from standard sizes for public parking spaces. From our results we draw conclusions for urban parking policy. These include introducing parking fees, coupled with paid parking permits for residents and, perhaps, employees; defining short-stay parking zones; providing parking spaces of different sizes; and increasing the level of enforcement.
... After all, the coverage and penetration of tracking devices are limited. At the aggregate level, the average cruising time (ACT) for a given area can serve as a good indicator (Shoup 2006; van Ommeren, Wentink and Rietveld 2012;Inci, van Ommeren and Kobus 2017;Lee, Agdas and Baker 2017;Millard-Ball, Hampshire and Weinberger 2020). ...
... More recently, GPS data is preferable for its accuracy and coverage (only with enough samples). For instance, Millard-Ball et al. (2020) use a GPS dataset to compute the difference between the actual distances driven and the most direct routes of parking-search vehicles in San Francisco and found that the average cruising distance (ACD) is 32.1 m with the standard deviation of 177.5 m (Millard-Ball, Hampshire and Weinberger 2020). Gu et al. (2020) developed a macroscopic parking dynamics model that considers both on-street and off-street parking. ...
Article
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The growing need for temporary pickups/drop-offs and commercial deliveries is crowding out the already inadequate on-street parking spaces designated for car trips, deteriorating the phenomenon of parking search. This paper: (1) uses empirical data and conducts descriptive and comparative analysis using a spatial lag model to analyze the factors influencing average cruising time (ACT) related to parking search, and (2) proposes a novel framework to predict grid-based ACT and to estimate average emission metrics (AEM). The study inputs an aggregated GPS dataset in a 6-month period to the framework and uses New York City and Los Angeles as case study cities. The descriptive and comparative analysis results support the spatial spillover effect of parking search and reveal that residential area, retail area, accommodation, and food services (hotels, restaurants, bars, etc.) employees are the most significant influencing factors on ACT and that temporary pickups/drop-offs and commercial delivery are also unneglectable sources of parking search. The prediction results show a concentrated distribution of ACT in New York City due to private vehicles’ spillover of parking searches. Los Angeles exhibits a relatively high degree of overlap between parking hotspots and emission blackspots, particularly in areas with intense truck activity, further substantiating the close relationship between truck activities and elevated emissions. Following the key findings, the paper proposes several policy recommendations. In practice, this prediction framework can ingest short-term data to provide ACT prediction maps to identify parking hotspots and emission blackspots.
... Weinberger et al. [26] proposed a heuristic to try to understand when the user started looking for parking, and applied machine learning to classify whether ended trips contain a search or not, basing the predictions on the ratio of the actual paths to the shortest route. Even Millard-Bell et al. [15] considered the last portion of recorded trips and computed the difference between the actual distance traveled and the respective shortest path distance. The estimates were then filtered to identify cruising, e.g., by retaining only trips where the driver passed at least twice through any road segment. ...
... Montini et al. [16] proposed a range of 800 meters, supporting the choice as an overestimation of the previous boundaries of parking search of 350m found by Weinberger et al. [26]. Millard et al. [15] did not need a radius estimation, as they performed the cruising detection based on the ratio between excessive travel and the shortest path to the destination. However, their results suggest that the average cruising area begins 400m before the destination. ...
Conference Paper
Interacting with a smart parking system to find a parking spot might be tedious and unsafe if performed while driving. We present a sys- tem based on a Boosted Tree classifier that runs on the smartphone and automatically detects when the driver is cruising for parking. The system does not require direct intervention from the driver and is based on the analysis of context data. The classifier was trained and tested on real data (615 car trips) collected by 9 test users. With this research, we contribute (i) by providing a literature review on cruising detection, (ii) by proposing an approach to model cruising behavior, and (iii) by describing the design, training, and testing of the classifier and discussing its results. In the long term, our work aims to improve user experience and safety in car-related contexts by relying on human-centered features that implicitly understand users’ behavior and anticipate their needs.
... For example, based on survey data, May and Turvey (1985) proposed to utilize an exponential function to describe the relationship between and , while Axhausen et al. (1994) proposed an inversely proportional function of 1 − . Several other authors have inferred the cruising time based on field surveys, GPS trajectories, or interviews with drivers (van Ommeren et al., 2012;Lee et al., 2017;Millard-Ball et al., 2020;Weinberger et al., 2020;Zhu et al., 2020). They have determined that the parking search time can range from some seconds (van Ommeren et al., 2012) to several minutes (Lee et al., 2017;Zhu et al., 2020), and this duration might be influenced by factors such as the arrival time or trip purpose. ...
... The constantly increasing vehicle ownership worldwide inevitably results in difficulty in finding a parking space in urban areas (1)(2)(3)(4). According to a recent study (1), cruising for parking is estimated to be 15% to 30% in the urban traffic flow. ...
Article
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Cities worldwide are striving to find more efficient approaches to address the prevalent parking challenges in urban areas. A key aspect of achieving an optimal parking environment is the collection of curbside parking data, which enables informed decision-making and effective management of on-street parking spaces. This study proposes a solution for curbside parking monitoring and data collection using roadside LiDAR systems. By leveraging laser beam variation detection, this solution can extract essential information about parking usage. Unlike existing solutions, such as imagery or embedded sensor-based monitoring, our solution offers portability and ease of deployment for short-term or long-term curbside parking data collection. Additionally, the LiDAR sensor captures only three-dimensional data and is independent of illumination conditions, ensuring stable operation throughout the day while safeguarding privacy by not capturing imagery. These features align with the requirements of city agencies for parking data collection. The workflow follows a simple trend without the need for complex training, as typically seen in machine learning-based methods, and instead relies on parameter tuning based on real-world environmental factors. To validate the effectiveness of our method, we collected curbside parking data for five days at a midtown traffic junction with eight parking spaces. Manual validation confirmed a 95% match between identified parking events and observed data across different time periods. The study further presents parking statistics based on the identified events, revealing crucial insights about parking usage in the study area.
... Just like other cities in Indonesia, the city of X relies on regional income in the tourism sector. The parking factor is one of the factors that tourists consider when choosing a tourist destination (Li et al., 2016;Millard-Ball et al., 2020;Wang et al., 2015). (Wang et al., 2015) specifically found that the availability of parking lots and parking retribution have a negative effect on tourist behavior when traveling. ...
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The existence of abundant illegal parking attendants in the city of X causes a leakage of parking revenue. The phenomenon of loss in parking levies encourages us to conduct this research. This research is an exploratory research with a quantitative descriptive approach to calculate the potential of parking loss in X city. Data were collected through interview and observation which never been done. We interviewed illegal parking attendants from 4 sub-districts by using convenience sampling which never been used before. This study found that the optimal potential of parking levies in a year reached IDR 8.682.868.820,- while the average realization of parking levies within five years was only IDR 1.427.539.500.- per year. This means that only 16.44% of potential parking levies are realized in a year. So there are still 83.56% potential parking levies haven't been recognized. It shows that local governments have not been able to identify the number of potential parking levies and impress parking mismanagement.
... In such situations, the total search time decreases, particularly when drivers park near the point where they start to search for a vacant place, as the participants did in scenario 2 (Fig. 7,9). This assertion corresponds to Millard-Ball et al. (2020), who found that search time for on-street parking decreased if the parking place was near the starting search point of the driver. This finding contrasts with the situation of underpriced on-street, like scenario 1, where lower prices lead to longer cruises around the destination. ...
Article
Scarcity of on-street parking in city centers is a known factor motivating drivers to drive slowly (“to cruise”) while searching for an available parking place and is associated with negative externalities e.g. congestion, accidents, fuel waste, and air pollution. Finding the correct prices is suggested to bring cruising to a sustainable level. Current research methods based on surveys and simulations fail to provide a complete understanding of drivers’ cruising preferences and their behavioral response to price changes. We used the PARKGAME serious game, which provides a real-world abstraction of the dynamic cruising experience. Eighty-three players participated in an experiment under two pricing scenarios. Pricing was spatially designed as “price rings” decreasing when receding from the desired destination point. We analyzed search time, parking distance, parking location choice, and spatial searching patterns. We show that such a pricing policy may substantially reduce the cruising problem, motivating drivers to park earlier—further away from the destination or in the lot, especially when occupancy levels are extremely high. We further discuss the policy implications of these findings.
... Some studies have tried to estimate this ratio, but have not reached a consistent conclusion [12][13][14]. On the other hand, some travelers adjust the departure time [15] and parking location [16] to avoid parking difficulties. Instead, the parking lot utilization rate is low, making managers underestimate the severity of the parking problem. ...
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Parking facilities in central urban areas have limited supply, high utilization, and turnover rate, leading to the high parking cost. To draw the issues of parking uncertainty, high search time, and underutilization of parking lots, this study shows the application of permits in parking management. It first analyzes the characteristics and costs of “arrival priority” and “reservation priority” modes, and then, it proposes the parking permit reservation and allocation mode based on “service order optimization” and designs an “ant colony-genetic” algorithm to solve the optimal service order. The numerical example shows that the measures of quantity control and matching optimization are effective in parking management. The parking reservation mode of “service order optimization” has advantages in parking lot utilization rate, service demand quantity, and total parking cost.
... However, the elasticity of parking prices is relatively low (Lehner and Peer 2019), and to decrease private cars arrival at highly demanded areas, like urban business districts, the prices should be high. In addition, the effects of parking prices can deteriorate in time (Alemi et al. 2018;Lee et al. 2017;Assemi et al. 2020;Millard-Ball et al. 2020;Hampshire et al. 2016). ...
Preprint
Full-text available
Parking occupancy in the area is defined by three major parameters - the rate of cars arrivals, the dwell time of already parked cars, and the willingness of drivers who are searching but yet did not find a vacant parking spot, to continue their search. We investigate a series of theoretical and numeric models, deterministic and stochastic, that describe parking dynamics in the area as dependent on these parameters, over the entire spectrum of the demand to supply ratio, focusing on the case when the demand is close to or above the supply. We demonstrate that a simple deterministic model provides a good analytical approximation for the major characteristics of the parking system - the average fraction of cars among the arriving that will find parking in the area, the average number of cars that cruise for parking, and average cruising time. Stochastic models make it possible to estimate the distributions of these characteristics as well as the parameters that are related to the variance of these distributions, like the fraction of the arriving cars that find parking in less than t minutes.
... On the other hand, Weinberger et al. (2020) insist on the role of the drivers' idiosyncratic behaviours in generating excess travel distances (perhaps they have kept driving because they were arguing about where to go for dinner or trying to lull a baby to sleep); the excess travel may be misapprehended for cruising (e.g., using GPS data) in districts of San Francisco and Ann Arbor where there is actually no lack of vacant spots. For the specific case of San Francisco, Millard-Ball et al. (2020) further note that cruising is actually rare because the lack of vacant spots may be internalised in the regular drivers' behaviours and 'perceived parking scarcity leads drivers to stop short of their destinations', thus curtailing cruising. However, these caveats do not suppress the ample evidence of cruising for parking in other cities (SARECO / Prédit-Ademe, 2005;Shoup, 2006;Hampshire and Shoup, 2018;Cookson and Pishue, 2017). ...
Preprint
Full-text available
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. 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 parking problem in a mathematically well posed manner which 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 arbitrary behaviours of the drivers. 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 the complex, large-scale case of the city of Lyon, France. Taken as a whole, these findings clarify the factors that directly control the search time and establish formal connections between the parking issue in realistic settings and physical problems.
... Researchers have focused on cruising because it implies that mispriced parking creates excess driving. Millard-Ball et al (2020), in contrast, point out that if people anticipate parking shortages, then rather than drive to their destination and cruise, they might park further away and walk. In this case mispriced parking would reduce driving. ...
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The curb is a monopoly good: it is network infrastructure owned entirely by cities. Unlike most network infrastructure, however, it is not managed primarily by prices. Cities instead allocate the curb with a confusing patchwork of meters, time limits, permits, and—perhaps most notably—fines. Urban parking thus stands out among network infrastructure for its low level of payment, low level of quality, and large share of revenue derived from punishment. We develop an explanation for why this is so, which emphasizes the curb’s public ownership and low production costs. We then use both qualitative and quantitative methods to examine some of our theory’s implications. We draw on descriptive data from a variety of US cities, but focus primarily on the city of Los Angeles as a case study.
... By using the macroscopic fundamental diagram (MFD) theory, they achieved the time-based optimal equilibrium result. Ref. [28] established a dynamic programming model for UPM and in this model, and they reconciled the relationship between cruising for parking and time cost and found a self-regulating mechanism in the process of cruising; they also directed that varying of travel demand attributable to cruising is minimal when compared with parking fees from large-scale GPS data and a SP survey for households. ...
Article
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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.
... Du et al. [7] established a mathematical model to analyse cruising behaviour for parking in a working area and found that cruising for parking can increase traffic flow. Millard-Ball et al. [8] used dynamic programming models to analyse cruising behaviour for parking based on GPS data and a household travel survey in San Francisco, California. The results showed that when travellers perceive scarce parking spaces, they are more likely to choose a parking space far from their destinations. ...
Article
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Parking problems caused by a lack of parking spaces have exacerbated traffic congestion and worsened environmental pollution. An analysis of the cruising process for parking can provide new perspectives to reduce cruising. Based on a parking survey conducted in Beijing, the authors collected a large amount of trajectory data of cruising vehicles. Then, fluctuation indexes of trajectories were proposed to analyse travellers’ cruising processes for parking. The spectral clustering method based on a hidden Markov model (HMM) was used to recognise the cruising trajectories. The recognition performance for three-dimensional trajectory data is better. Cruising trajectories for Clusters 1, 2, 3, 4, and 6 have large fluctuations and a weightier effect on road traffic. These groups can be taken as target groups for intelligent parking guidance and recommendations. The recognition accuracies for parking location and parking status increase with increasing intercepted trajectory lengths. 150 m from far to near the desired destination can be used as a threshold of the cruising trajectory length to accurately predict travellers’ parking location and status. These research results can be applied in intelligent parking systems to dynamically predict parking situations, formulate parking guidance schemes and information release strategies, and improve parking efficiency.
Article
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 .
Article
Parking occupancy in a delineated area is defined by three major parameters – the rate of car arrivals, the dwell time of already parked cars, and the willingness of drivers to continue their search for a vacant parking spot. We investigate a series of theoretical and numeric models, both deterministic and stochastic, that describe parking dynamics in an area as dependent on these parameters, over the entire spectrum of the demand-to-supply ratio, focusing on the case when the demand is close to or above the supply. We demonstrate that a simple deterministic model provides a good analytical approximation for the major characteristics of a parking system – the average fraction of cars among the arrivals that will find parking in the area, the average number of cars that cruise for parking, and the average cruising time. Stochastic models make it possible to estimate the distributions of these characteristics as well as the parameters defined by the distribution PDF, like the fraction of the arriving cars that find parking in less than t minutes. The results are robust to the distribution of drivers’ dwell and renege times and can be directly applied to assess the real-world parking dynamics.
Chapter
Scarcity of on-street parking in cities centers is a known factor motivating drivers to drive slowly (“to cruise”) while searching for an available parking place and is associated with negative externalities e.g., congestion, accidents, fuel waste and air pollution. Finding the correct prices is suggested to bring cruising to a sustainable level. However, current research methods based on surveys and simulations fail to provide a full understanding of drivers’ cruising preference and their behavioral response to price changes. We used the PARKGAME serious game, which provides a real-world abstraction of the dynamic cruising experience. Eighty-three players participated in an experiment under two pricing scenarios. Pricing was spatially designed as “price rings”, decreasing when receding from the desired destination point. Based on the data, we analyzed search time, parking distance, parking location choice and spatial searching patterns. We show that such a pricing policy may substantially reduce the cruising problem, motivating drivers to park earlier—further away from the destination or in the lot, especially when occupancy levels are extremely high. We further discuss the policy implications of these findings.KeywordsSerious gamesCruisingDriver behaviorParking search
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This paper presents a method for determining parking search behavior using GPS traces. The research takes advantage of a GPS based household travel survey, an extensive dataset of GPS with video, and a commercially purchased set of trip segments. Strategies for data cleaning, matching traces to digitized networks, assessing the probability that a trace is of good quality, and strategies for determining whether or not a trip involves excess travel due to parking search are described. We define and operationalize two definitions of excess search – popularly known as cruising. Our results suggest that cruising in San Francisco, CA and Ann Arbor, Michigan is acute in some locations but overall experienced in less than 5–6% of vehicle trips, and that it accounts for less than 1% of vehicle travel in these cities–considerably less than in previous estimates.
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We propose an approximation method for estimating the probability p(τ,n) of searching for on-street parking longer than time τ from the start of a parking search near a given destination n, based on high-resolution maps of parking demand and supply in a city. We verify the method by comparing its outcomes to the estimates obtained with an agent-based simulation model of on-street parking search. As a practical example, we construct maps of cruising time for the Israeli city of Bat Yam, and demonstrate that despite the low overall demand-to-supply ratio of 0.65, excessive demand in the city center results in in a significant ratio of parking searches that last longer than 5 or even 10 minutes. We discuss the application of the proposed approach for urban planning.
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Background An autonomous vehicle will go unaccompanied to park itself in a remote parking lot without a driver or a passenger inside. Unlike traditional vehicles, an autonomous vehicle can drop passengers off near any location. Afterward, instead of cruising for a nearby free parking, the vehicle can be automatically parked in a remote parking lot which can be in a rural fringe of the city where inexpensive land is more readily available. Objective The study aimed at avoidance of mistakes in the identification of the vehicle with the help of the automatic identification device. Methods It is proposed to back up license plate identification procedure by making use of three distinct identification techniques: RFID, Bluetooth and OCR with the aim of considerably reducing identification mistakes. Results The RFID is the most reliable identification device but the Bluetooth and the OCR can improve the reliability of RFID. Conclusion A very high level of reliable vehicle identification device is achievable. Parking lots for autonomous vehicles can be very efficient and low-priced. The critical difficulty is to automatically make sure that the autonomous vehicle is correctly identified at the gate.
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We propose a new way to measure the share of traffic that is cruising for parking: observe how many cars pass a newly vacated space before a driver parks in it. This statistical method provides a quick, cheap and approximate way to estimate what share of traffic is cruising. Using 876 observations of newly vacated on-street parking spaces in central Stuttgart, we estimated that 15 per cent of the traffic was cruising for parking.
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This paper reports on the development of an agent-based cruising-for-parking simulation using the cellular automaton (CA) approach. The software was tested on a small-scale scenario, and a first verification step was performed for a real-world scenario for the town center of Zurich. Approaches to integrating the simulation into MATSim, a multi-agent transport simulation program, are discussed. The software is open source and can be downloaded from a free software repository. Empirical data that may be valuable for future model calibration are currently being surveyed in a global positioning system (GPS) study at the authors' institute.
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Parking policy is one of the key links between transport and land-use policy. Parking policies are often compromised in their effectiveness due to the perceived tension between three of the objectives that parking supports: regeneration, restraint and revenue. In particular the belief that parking restraint measures could damage the attractiveness of city centres to both retail and commercial enterprises limits the political acceptability of pricing policies and planning. This paper presents a review of the evidence base upon which commuter, leisure and shopping and residential parking policies are based. Whilst underdeveloped, the literature suggests that greater attention should be given to analysing and presenting the accessibility impacts that different parking restraint measures have on travelers of all modes. The research base in many instances does not support, or provides evidence counter to, the assumption that parking restraint makes centres less attractive. Further disaggregate work is needed to understand how context specific these findings might be.
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This paper presents a method for determining parking search behavior using GPS traces. The research takes advantage of a GPS based household travel survey, an extensive dataset of GPS with video, and a commercially purchased set of trip segments. Strategies for data cleaning, matching traces to digitized networks, assessing the probability that a trace is of good quality, and strategies for determining whether or not a trip involves excess travel due to parking search are described. We define and operationalize two definitions of excess search – popularly known as cruising. Our results suggest that cruising in San Francisco, CA and Ann Arbor, Michigan is acute in some locations but overall experienced in less than 5–6% of vehicle trips, and that it accounts for less than 1% of vehicle travel in these cities–considerably less than in previous estimates.
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We estimate the effect of parking occupancy on distances walked between parking and residential locations in Amsterdam. Using data from scanner cars, we show that walking distances only increase when the occupancy rate exceeds 85 per cent. However, the marginal effect of occupancy on walking beyond 85 per cent is limited: every parker imposes 8 m on each subsequent parker. Our analysis suggests it is optimal to have almost all parking spaces occupied late in the evening when few residents aim to park. Our result has important consequences for policy makers who use residential parking permits to prevent cruising for parking.
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Existing work emphasizes the importance of traffic congestion externalities, but typically ignores cruising-for-parking externalities. We estimate the marginal external cruising costs of parking-that is, the time costs that an additional parked car imposes on drivers by inducing them to cruise for parking-which is one of the main components of cruising-for-parking externalities. The level of cruising is identified by examining to what extent the car inflow rate into a parking location falls with parking occupancy level. For a commercial street in Istanbul, we demonstrate that a marginal car parking for an hour induces 3.6 other cars to cruise for parking. This translates into an external cruising cost that is in the same order of magnitude with the external traffic congestion cost created by the trip. © The Author (2017). Published by Oxford University Press. All rights reserved.
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The goal of this study is to explore the perceptions and behaviors of drivers who cruise for parking. We conducted surveys with drivers in Brisbane, Australia, to understand potential factors that influence drivers’ cruising behavior. This study reveals that errors in drivers’ perception of parking cost are one of the leading factors encouraging drivers to cruise for on-street parking. Drivers are not necessarily well informed about parking costs, even when they claim to be familiar with these costs. The survey also reveals that the more informed drivers are about the local traffic and parking conditions, the less likely they are to cruise for extended periods of time. This finding demonstrates the value of traffic and parking information to effectively mitigate cruising for parking. The interview results also demonstrate that the on-street parking premium (i.e., accessibility or convenience factor) could be much larger than our common assumptions and a significant contributor to increased cruising time. Finally, this study introduces the sunk cruising cost and its potential impact on cruising time. Our hypothesis is that the effect of the sunk cost may manifest in a greater tendency for drivers to continue cruising because the time spent cruising is simply unrecoverable past expenditure. The survey data supports our hypothesis, and with findings on the drivers’ misperception about parking cost and the familiarity factor, this result highlights the value of accurate and timely parking cost and availability of information to drivers to tackle the cruising-for-parking issue.
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Several recent papers have used the approximation that the number of curbside parking spaces searched before finding a vacant space equals the reciprocal of the expected curbside vacancy rate. The implied expected cruising-for-parking times are significantly lower than those that have been obtained through observation and simulation. Through computer simulation of cars cruising for parking around a circle in stochastic steady state, this paper shows that the approximation leads to underestimation of expected cruising-for-parking time and, at high occupancy rates, considerable underestimation. The paper also identifies several “effects” that contribute to the approximation being an increasingly poor one as the occupancy rate increases.
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This paper reviews the literature on parking with an emphasis on economic issues. Parking is not just one of the most important intermediate goods in the economy; it is also a vast use of land. Many theoretical and empirical papers analyze the quantity and pricing of parking by concentrating on particular aspects of the issue. The aspects covered in this review are cruising for parking, spatial competition, (minimum and maximum) parking requirements, parking pricing and road pricing in the bottleneck model, and temporal-spatial pricing. Various forms of parking, including residential parking, shopping mall parking, and employer-provided parking, are also reviewed before identifying understudied topics that should be on the research agenda.
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This article proposes two models to analyse parking search: an analytical model called PARKANALYST and a geosimulation model, termed PARKAGENT, which explicitly accounts for street network and drivers' parking-related decisions. We employ both models to analyse the impact of occupancy rate and demand-to-supply ratio on cruising for parking and to compare the models' outcomes. We estimate the main characteristics of parking dynamics, and find that the spatial effects influence system dynamics starting from an occupancy rate of 85% while become really important for analysing parking when the occupancy rate is above 92–93%.
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In this paper we propose an assignment model on urban networks to simulate parking choices; this model is able to simulate the impact of cruising for parking on traffic congestion. For simulating parking choice and estimating the impact of cruising on road congestion we propose a multi-layer network supply model, where each layer simulates a trip phase (on-car trip between the origin and destination zone, cruising for parking at destination zone and walking egress trip). In this model the cruising time is explicitly simulated on the network. The proposed model is tested on a trial network and on a real-scale network; numerical tests highlighted that the proposed model is able to simulate user parking choice behaviour and the impact of cruising for parking upon road congestion, particularly when the average parking saturation degrees exceed 0.7.
On-street parking search. review and future research direction
  • Brooke
Empirical evidence on cruising for parking
  • van Ommeren