INTRODUCTION: Parking in road freight transport has been a problem for a long time. Several EU legislative decisions have contributed to reducing the sustainability of the parking system in recent ...times. OBJECTIVES: The aim of this paper is to point out the negative impact of social law requirements on the parking of freight vehicles. The aim is also to propose the methodology of determining the necessary number of parking spaces for possible compliance with the requirements of social law. METHODS: Own research is realized by the numbering of parking spaces on selected routes in the EU and comparing with the number of freight vehicles on the route. RESULTS: We have shown that there are not enough parking areas for current transport flows in frame of whole EU. In assessing of the requirement of EU social law to prohibit weekly rest in the cab of a vehicle, we have come the conclusion that in current capacity of hotels in highway resting areas, it is not possible to meet this requirement of social law. CONCLUSION: The proposed methodology defines the needed number of parking places for a specific area. From the point of view of the sustainability of road freight transport, it is essential that parking areas are planned in accordance with regulatory requirements. Otherwise, drivers are forced to cheat, what leads to distortion of the whole road freight transport market.
Carsharing is growing rapidly in popularity, often backed by government and private partners, such as universities and developers. While reduced parking demand is frequently cited as a reason to ...promote carsharing, virtually no quantitative analysis has been done on the impact of carsharing on parking demand. Instead, prior studies focus on vehicle ownership, which has an implied connection to parking demand. This paper analyzes the impact of carsharing on parking demand in a university setting (with just over 1000 carsharing users) using a member survey and parking permit sales data. Changes in parking demand are broken down by geographic area and parking type. Members report the vast majority (over 76%) of forgone vehicles would be parked in the same area as the member's household on most weekdays, nights, and weekends. Roughly 30% would be parked on the street at most times, with the percentage parked in personal driveways and garages peaking at roughly 40% on nights and weekends and dropping to 26% on weekdays. Members reported an increase in shopping trips made by car or truck (statistically significant at 1% level), leading to a small increase in parking demand at stores, but this increase was much smaller than the reductions seen elsewhere. The paper also assesses other impacts which have so far been nearly exclusively measured in relatively large cities. For example, the survey revealed a reduction of 15.3 personal vehicles for every carsharing vehicle, roughly equivalent to findings from major cities.
•Quantified impacts of carsharing, using survey and parking permit data.•Compared university-setting impacts with prior studies on larger cities.•Compared vehicle ownership/leasing/usage across subgroups of members.•Broke parking demand impact down by location and type (e.g. street).
This paper proposes a two-stage optimization framework for generating the optimal parking motion trajectory of autonomous ground vehicles. The motivation for the use of this multilayer optimization ...strategy relies on its enhanced convergence ability and computational efficiency in terms of finding optimal solutions under the constrained environment. In the first optimization stage, the designed optimizer applies an improved particle swarm optimization technique to produce a near-optimal parking movement. Subsequently, the motion trajectory obtained from the first stage is used to start the second optimization stage, where gradient-based techniques are applied. The established methodology is tested to explore the optimal parking maneuver for a car-like autonomous vehicle with the consideration of irregularly parked obstacles. Simulation results were produced and comparative studies were conducted for different mission cases. The obtained results not only confirm the effectiveness but also reveal the enhanced performance of the proposed optimization framework.
Many car makers have examined parking assistance systems and auto parking systems that automatically find free parking spaces and can park a car. The around view monitoring (AVM) can compensate for ...the disadvantages of distance-sensor-based detection because it can recognize parking spaces based on parking slot markers instead of empty spaces. However, in the case of AVM-based parking marker recognition, false-positive (FP) features can be detected from 3-D objects and shadows, and the parking slot marker is occluded by the vehicle on which the AVM camera is attached. In this paper, we propose a probabilistic occupancy filter to detect parking slot markers. This filter uses a series of AVM images and onboard sensors to improve the occlusion problem and reduce the FPs from other objects. Each pixel of an AVM image has an occupancy probability of parking slot marker features. The occupancy probability compensates for using vehicle motion and updates using the error model of the AVM system and the performance indicator of the feature extractor.
•The FRD-VCG mechanism to restrain participants opt out in shared parking market.•Priority attributes are incorporated into original bids to generate fairness bids.•Evaluation function generates ...comparable values for winner determination.•Advantages of the FRD-VCG mechanism are simulated.
We propose a fair recurrent double VCG (FRD-VCG) auction mechanism to approach the emerging shared parking management problem. In a given shared parking environment with a parking management platform and a double-side perspective, the proposed mechanism considers how to restrain the potential participants (parking slot demanders and slot suppliers) opt out, which is based on the participants’ priority attributes and are calculated with respect to historic auction records provided by the parking platform. Participants’ fairness bids are then generated combining their priority attributes and their submitted bids (bid price and parking time) with the support of a novel evaluation function, which integrates priority attributes, bid price and parking time into an output value. The parking slot allocation rule and transaction payment rule are further designed to dealing with these issues include winner determination and price setting, respectively. Simulations show advantages of the proposed FRD-VCG mechanism, i.e., comparing with the double VCG (D-VCG) mechanism for the shared parking management problem where priority attributes and evaluation function are not considered, the proposed FRD-VCG mechanism has the potential to persuade participants to remain in the market whilst it improves the market’s retention rate, the parking slot’s utilization rate and the participants’ utilities.
This paper introduces a novel methodology for optimizing parking recommendations on the campus of Pardubice University, aimed at enhancing the management of existing parking areas. The model takes ...into account factors such as distance, travel time, parking spot availability, and user preferences, leveraging data processed via the Google Maps API and Open-CV. Fuzzy logic is employed within the model to deal with imprecise concepts, providing adaptability. Performance evaluations yielded an impressive accuracy of 92%, attesting to its viability for real-world implementation. This research significantly advances smart parking solutions, showing promise for reductions in wasted time, alleviated traffic congestion, and improved parking efficiency.
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.
In planning new apartment developments, off-street car parking provision is frequently raised as a key concern. However, there is little understanding of the adequacy of off-street car parking for ...individual apartment households. Drawing on a survey of apartment residents in Perth, Melbourne, and Sydney (n = 1316), this research assesses the adequacy of off-street car parking provision for apartment households and provides an understanding of factors associated with an undersupply and oversupply of off-street car parking. Results show that around two-thirds (65.9%) of apartment households have a ‘balanced’ amount of off-street car parking, where the number of cars owned is equal to the number of allocated off-street car parking spaces. The remaining households either have an oversupply of off-street car parking (20.2%) or an undersupply (14.0%), almost always involving one parking space too many or one too few. Factors associated with an under/oversupply of off-street car parking are found to be largely related to household characteristics and residents' perceptions of parking issues. The findings highlight the potential for ‘unbundling’ off-street car parking from the purchase price or rental cost of apartment housing, while developing more tailored residential parking requirements that seek to prevent an under/oversupply of parking.
Effective on-street parking is key to reduce urban traffic and pollution in densely populated cities. Thus, researchers have focused on forecasting future occupancy values depending on factors like ...time, space, or weather. This approach shows high average performances, but fails in predicting congested scenarios, actually the most critical. This work proposes a data-driven parking level of service (LOS) predictor that outperforms traditional methods, solving its inherent class imbalance issue by means of Random Undersampling Boost classifiers. We trained and validated the LOS classifiers using 13 months of data collected from the smart parking system in the city of Madrid, Spain. Results display average recall values above 0.94 and 0.87 at prediction horizons up to 10 and 60 minutes respectively. We compare these results with traditional regression-based occupancy predictors showing that our classifier clearly outperforms the formers predicting the minority classes, which carry the most significant information for drivers and parking managers. We further analyze the impact on performance of temporal and spatial features, revealing mid-term temporal data as the most relevant forecasting information, and low correlations between parking behaviors in bordering neighborhoods. In the light of these results, we believe that the proposed data-driven parking LOS classification has the potential to open a novel perspective on the parking occupancy forecasting field.
In smart parking environments, how to choose suitable parking facilities with various attributes to satisfy certain criteria is an important decision issue. Based on the multiple attributes decision ...making (MADM) theory, this study proposed a smart parking guidance algorithm by considering three representative decision factors (i.e., walk duration, parking fee, and the number of vacant parking spaces) and various preferences of drivers. In this paper, the expected number of vacant parking spaces is regarded as an important attribute to reflect the difficulty degree of finding available parking spaces, and a queueing theory-based theoretical method was proposed to estimate this expected number for candidate parking facilities with different capacities, arrival rates, and service rates. The effectiveness of the MADM-based parking guidance algorithm was investigated and compared with a blind search-based approach in comprehensive scenarios with various distributions of parking facilities, traffic intensities, and user preferences. Experimental results show that the proposed MADM-based algorithm is effective to choose suitable parking resources to satisfy users' preferences. Furthermore, it has also been observed that this newly proposed Markov Chain-based availability attribute is more effective to represent the availability of parking spaces than the arrival rate-based availability attribute proposed in existing research.
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