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shows two plots based on the difference between the shortest path a driver could take to their final
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The number of cars searching for parking, also known as “cruising,” is a risk factor linked to increased pollution and congestion and decreased road safety. Although the detrimental effects of cruising are known, the actual amount of cruising is unknown. A novel video data set of naturalistic driving is shown to provide reliable estimates of cruisi...
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... Current parking resource management strategies fail to effectively address this dynamically varying demand, frequently resulting in parking shortages during peak times and exacerbating urban traffic conditions. Consequently, the inadequacies of the current management system in forecasting and allocating parking resources highlight the inefficiencies in urban parking distribution, emphasizing the urgent need for enhancements to improve the overall effectiveness of urban transportation systems [3]. ...
Accurate predictions of parking occupancy are vital for navigation and autonomous transport systems. This research introduces a deep learning mode, AGCRU, which integrates Adaptive Graph Convolutional Networks (GCNs) with Gated Recurrent Units (GRUs) for predicting on-street parking occupancy. By leveraging real-world data from Melbourne, the proposed model utilizes on-street parking sensors to capture both temporal and spatial dynamics of parking behaviors. The AGCRU model is enhanced with the inclusion of Points of Interest (POIs) and housing data to refine its predictive accuracy based on spatial relationships and parking habits. Notably, the model demonstrates a mean absolute error (MAE) of 0.0156 at 15 min, 0.0330 at 30 min, and 0.0558 at 60 min; root mean square error (RMSE) values are 0.0244, 0.0665, and 0.1003 for these intervals, respectively. The mean absolute percentage error (MAPE) for these intervals is 1.5561%, 3.3071%, and 5.5810%. These metrics, considerably lower than those from traditional and competing models, indicate the high efficiency and accuracy of the AGCRU model in an urban setting. This demonstrates the model as a tool for enhancing urban parking management and planning strategies.
... Obviously, compared with distance-first walk, this strategy is more in line with parking assisted by PGS in reality. 4) Renege rate: Hampshire et al. used video data to study cruising behaviour and found that the maximal cruising time for drivers is approximately 10 min [36]. Therefore, the per-minute renege rate is set to 10% in this paper. ...
Spread of parking difficulty can be regarded as a special cascading failure process of urban parking systems. A comprehensive understanding of this process can be greatly helpful to build a more robust parking system. Parking network, a specified complex network, is proposed to model, simulate, and analyse the failure process of urban parking systems in this paper. This model is applied to the analysis of parking systems in an abstract city grid and the downtown area of Luohu, Shenzhen. The results demonstrate that the parking network can capture subtle variations among various parking cruising behaviours or strategies from a network perspective. To enhance the utility of the parking network, an auxiliary indicator named “Parking Difficulty Index” is introduced to help assess the failure degree of urban parking system, estimate the optimal timing for parking guidance intervention, and evaluate the effectiveness of various guidance strategies in mitigating parking difficulties.
... For example, parking meter transaction data, curbside parking data, and arterial traffic data can help estimate the proportion of traffic searching for parking along high occupancy arterials (Dowling et al. 2017). For behavioral research, surveys and videos are often ways to study the extent to which people perceive parking searches, and researchers use these methods to determine how many vehicles in a specific area are searching for parking (Hampshire et al. 2016;Qin et al. 2020). GPS data can help investigate parking searches' temporal and spatial aspects (van der Waerden, Timmermans and Van Hove 2015). ...
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.
... Additional to these methods, Hampshire et al. [7] proposed a method for cruising detection on the analysis of images captured via inside-car cameras pointed to the driver. This study allows a very accurate recognition of the starting time for the parking search. ...
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.
... Empirical parking data, which are difficult and costly to acquire, are not readily available. In fact, very few studies have collected such data, except Hampshire et al. (2016) who used in-vehicle video data to record the parking search behavior, and Assemi et al. (2020) and Belloche (2015) who used surveys as another alternative. These parking data collection methods, however, are not well suited for acquiring sufficient data rather than a small sample in an objective and easy-to-implement manner. ...
... However, the elasticity of parking prices is relatively low (Lehner and Peer, 2019), and to decrease the rate of private cars arriving to areas with high demand, like urban business districts, the prices should be high. In addition, the effects of parking prices can deteriorate over time (Alemi et al., 2018;Lee et al., 2017;Assemi et al., 2020;Millard-Ball et al., 2020;Hampshire et al., 2016). ...
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.
... The scarcity of on-street parking places in city centers motivates drivers to drive slowly (i.e., cruising) while cruising (i.e., searching) for a vacant on-street parking place and is associated with negative externalities such as air pollution, road accidents, fuel waste, and exacerbates congestion in inner-city streets (Hampshire et al., 2016). Cruising in a downtown area typically occurs when onstreet parking places are underpriced compared to parking lots (Shoup, 2006). ...
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.
... Yang and Qian [17] researched the estimation of onstreet occupancy within the SFPark system by time using transaction data in two areas. Some papers use parking data from surveys [36], video [37], GPS traces [38], and transaction data [17,39], while this project uses collected real-time sensor data directly. Parking demand in Hong Kong has been thoroughly studied [10,12], but those data are based on previous parking patterns, and the collected data come from manual calculations and survey data. ...
Parking plays an essential role in urban mobility systems across the globe, especially in metropolises. Hong Kong is a global financial center, international shipping hub, fast-growing tourism city, and major aviation hub, and it thus has a high demand for parking. As one of the initiatives for smart city development, the Hong Kong government has already taken action to install new on-street parking meters and release real-time parking occupancy information to the public. The data have been released for months, yet, to the best of our knowledge, there has been no study analyzing the data and identifying their unique characteristics for Hong Kong. In view of this, we examined the spatio-temporal patterns of on-street parking in Hong Kong using the data from the new meters. We integrate the t-SNE and k-means methods to simultaneously visualize and cluster the parking occupancy data. We found that the average on-street parking occupancy in Hong Kong is over 80% throughout the day, and three parking patterns are consistently identified by direct data visualization and clustering results. Additionally, the parking patterns in Hong Kong can be explained using land-use factors. Overall, this study can help the government better understand the unique characteristics of on-street parking and develop smart management strategies for Hong Kong.
... In Hampshire et al., (2016) [17], parking search behavior was analyzed using an onboard camera and a GPS device in the vehicle. GPS was used to track the route and time of the search, while gestures of the driver were used to identify the start of the parking search. ...
... In Hampshire et al., (2016) [17], parking search behavior was analyzed using an onboard camera and a GPS device in the vehicle. GPS was used to track the route and time of the search, while gestures of the driver were used to identify the start of the parking search. ...
Parking lots are places of high air pollution as numerous vehicles cruise to find vacant parking places. Open parking lots receive high vehicle traffic, and when limited empty spaces are available it leads to problems, such as congestion, pollution, and driver frustration. Due to lack of return on investment, open parking lots are little studied, and there is a research gap in understanding the magnitude of CO2 emissions and cruising observed at open parking lots. Thus, this paper aims to estimate CO2 emissions and cruising distances observed at an open parking lot. A thermal camera was utilized to collect videos during peak and non-peak hours. The resulting videos were utilized to collect cruising trajectories of drivers searching for empty parking spaces. These trajectories were analyzed to identify optimal and non-optimal cruising, time to park, and walking distances of drivers. A new CO2 model was proposed to estimate emissions in smaller geographical regions, such as open parking lots. The majority of drivers tend to choose parking spaces near a shopping center, and they prefer to cruise non-optimal distances to find an empty parking space near the shopping center. The observed mean non-optimal cruising distance is 2.7 times higher than the mean optimal cruising distance. Excess CO2 emissions and non-optimal cruising were mainly observed during visitor peak hours when there were limited or no empty parking spaces. During visitor peak hours, several vehicles could not find an empty parking space in the region of interest, which also leads to excess cruising.
... In the city of Beijing, private cars account for 30% of the travel demand, and each car travels more than 3 times with about 50 km distance a day on average [2]. However, as the endpoint of car travel, finding a parking spot often constitutes an appreciable fraction of the total travel time and contributes to traffic congestion and negative externalities [3][4][5]. According to the survey data, the time cost that a vehicle looks for a vacant spot accounts for 30-50% of the total travel time, nearly 70% of the direct travel cost, and 30% of traffic jams [6][7][8][9]. ...
... In the empirical study of parking, the existing knowledge is strongly biased towards the reaction of drivers to parking prices [18][19][20][21][22], while the other critical factors, e.g., the occupancy rate, delay, and distance to destination, remain obscure [1]. In recent years, various methods were used to observe parking searching and cruising, e.g., follow vehicles, park-and-visit tests, in-car video, and GPS tracking [4,[23][24][25][26][27][28][29][30][31], but they did not reach completely consistent conclusions in studying travelers' search behavior and its impact on the system. ...
Improving parking efficiency is essential to promoting the reform in urban transportation. But the large amount of deadweight costs caused by the parking is often underestimated because it is difficult to measure. Based on the existing investigations from the small fraction of cruising vehicles, this paper explores the influencing factors of the parking issue and describes it by the user equilibrium model. Then, two types of permit management schemes were proposed, lot-based and spot-based. By analyzing their performance in reducing system cost, three conclusions were drawn. Firstly, parking search leads to traveler’s schedule and location adjustments, raises the trip cost, reduces the parking lot occupancy, and makes the parking issues “invisible.” Secondly, permit scheme levels up managers’ control, and it performs well in reducing deadweight loss, but only by eliminating the search cost, the deadweight loss can be fundamentally reduced. Thirdly, reducing parking search needs information guidance; with the rapid growth of urban parking demand, managers should make a transition to the permit scheme with parking information.
