Cruising-for-parking is a common phenomenon in many urban areas worldwide. Properly understanding and mitigating cruising can reduce travel times, alleviate traffic congestion, and improve the local ...environment. Most of the existing studies estimating cruising traffic are based on empirical data and/or detailed simulation models. Both approaches have large data requirements, and the detailed simulation models tend to have high computational costs. In this paper, we present a case study of an area within the city of Zurich, Switzerland, using a recently proposed macroscopic model to analyze the current conditions of cruising-for-parking. The results are validated with empirical data. The macroscopic model, inspired by a bottleneck model, reproduces the dynamics of both, the parking and the traffic system, as well as their interactions. As such, it calculates the delays encountered by drivers while waiting for parking, and the impact of such delays on the overall traffic stream, which involves not only the searching traffic but also the through traffic. It is shown that the macroscopic parking model could, additionally, incorporate the data generated by agent-based models, cooperatively producing valid and trustworthy results of cruising estimations, while requiring comparatively few data inputs and relatively low computational costs. The study shows that in a small area of Zurich (0.28 km
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) with a demand of 2687 trips in a typical working day, cruising-for-parking generates 83 h of additional travel time and 1038 km of additional travel distance. Surprisingly, the worst conditions are observed at noon, corresponding to a maximum number of 30 searchers with an average search time of 13 min. Additionally, four types of parking policies are discussed, and their potential impacts on traffic performance are either quantitatively or qualitatively illustrated. The four policies include: the adjustment of the parking supply, the adjustment of parking time controls, the adoption of dynamic parking charges, and the provision of parking forecasts.
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.
•This paper calibrates a joint model of travel mode and parking location choice.•Travelers are very sensitive to changes in egress time, even more so than parking price.•Travelers respond to parking policies primarily by shifting parking locations rather than switching travel mode.•There are synergistic effects between parking pricing and policy measures that reduce search and egress time.
Appertaining parking lots of public buildings provide a large proportion of parking supply in cities. However, these parking lots mainly serve the parking demands of public buildings, leading to a ...low utilization ratio of parking spaces. It is therefore required to implement a shared parking strategy for these parking lots. In this study, a parking space allocation method (PSAM) at the network level is proposed to allocate the parking demand to a parking lot and then the parking space. The users are divided into M-users (users of the buildings) and P-users (public users). The shared parking strategy is analyzed from the aspects of open window, parking fee, and ratio of reservation spaces. The users are allocated to a parking lot by a multinomial logit(MNL) model. Specifically, it is determined whether they can enter parking lot and which space they are allocated according to the specific rules. After all the users are allocated with a parking space, the rejection number of M-users, occupancy rate, and profits of each parking lot are collected and a NSGA-II (non-dominated sorting genetic algorithm II) algorithm is designed to determine the optimal strategy for each parking lot according to the above. Compared with the results of all-time all-space shared parking strategy, our method shows better performance in balancing the interests of all appertaining parking lots and protecting the interests of M-users while obtaining considerable profits for the parking lots.
The technique to forecast available parking spaces (APSs) is the foundation theory of parking guidance information systems (PGISs). This study utilises data collected on parking availability at ...several off-street parking garages in Newcastle upon Tyne, England, to investigate the changing characteristics of APS. Using these baseline data the research reported here aims to build up a short-term APS forecasting model and applies the wavelet neural network (WNN) method to the PGIS problem. After selecting optimal preferences, including training set size, delay time and embedding dimension, the APS short-term forecasting model based on WNN is built and tested using the real-world dataset. By experimental tests conducted using the same dataset, WNN's prediction performance is compared with the largest Lyapunov exponents (LEs) method in the aspects of accuracy, efficiency and robustness. It is found that WNN prevails through a more efficient structure and employs, barely half of the computational cost compared to the largest LEs method, which could be significant if applied to real-time operation. Moreover, WNN enjoys a more accurate performance, for its prediction average mean square error (MSE) is 6.4 ± 3.1 (in a parking building with 492 parking lots) for workdays and 8.5 ± 6.2 for weekends, compared to the MSE of largest LEs method, 18.7 and 29.0, respectively.
•Parking demand in the morning commute can be managed by dynamic pricing.•System optimal parking flow pattern and prices are formulated by a linear program.•Optimal terminal parking occupancy exists ...and is unique under our network settings.•Optimal parking flow can be achieved by setting location-based pricing schemes.•Optimal parking prices can be set dependent on the time or time-varying occupancy.
This paper investigates how recurrent parking demand can be managed by dynamic parking pricing and information provision in the morning commute. Travelers are aware of time-varying pricing information and time-varying expected occupancy, through either their day-to-day experience or online information provision, to make their recurrent parking choices. We first formulate the parking choices under the User Equilibrium (UE) conditions using the Variational Inequality (VI) approach. More importantly, the System Optimal (SO) parking flow pattern and SO parking prices are also derived and solved efficiently using Linear Programming. Under SO, any two parking clusters cannot be used at the same time by travelers between more than one Origin–Destination (O–D) pairs. The SO parking flow pattern is not unique, which offers sufficient flexibility for operators to achieve different management objectives while keeping the flow pattern optimal. We show that any optimal flow pattern can be achieved by charging parking prices in each area that only depend on the time or occupancy, regardless of origins and destinations of users of this area. In the two numerical experiments, the best system performance is usually achieved by pricing the more preferred (convenient) area such that it is used up to a terminal occupancy of around 85–95%. Optimal pricing essentially balances the parking congestion (namely cruising time) and the level of convenience.
It is believed that automated valet parking (AVP) system has great potential to mitigate the parking headache for the future smart city, as it can provide on-demand parking services, bringing immense ...benefits from energy saving for vehicles to time saving for drivers. For an AVP system, parking reservation is an indispensable part so that vehicles can accomplish automated parking in accordance with the reserved parking information. However, the reservation requests may not only reveal the driver identity, but also disclose his/her sensitive locations, e.g., the most visited places, which are of great concerns to users. To deal with this challenge, the anonymous techniques can be naturally applied during parking reservation, but directly applying the anonymous techniques in AVP will introduce a new security issue, i.e., the anonymous user may maliciously crash the AVP system by repetitively sending the reservation requests, which is called "double-reservation attack." In this paper, we propose a new privacy-preserving reservation scheme for securing AVP system. Specifically, each anonymous user must have only one valid reservation token at any moment, and the token can only be used for booking one vacant parking space once. The proposed scheme does not only preserve the user's identity privacy and location privacy but also prevents the "double-reservation attack" based on several elegant building blocks, i.e., zero-knowledge proofs of knowledge and proxy resignature. Detailed security analysis confirms the security properties of our proposed scheme. In addition, extensive simulations are conducted to compare our proposed scheme with three previous schemes, and the experiment results demonstrate that our scheme is also much efficient in a WiFi-based testbed.
Abstract
This research aims to improve the utilization efficiency of parking facilities in residential areas. The real‐time and fixed‐time shared parking spot allocation models based on a time window ...constraint are proposed, respectively. The real‐time model adopts the dynamic response service mechanism, introducing a multi‐objective decision weighting method to construct the weighted evaluation function. Then, the 0–1 planning model with user optimization is established, utilizing branch‐bound algorithm for a solution. The fixed‐time model adopts the periodic service mechanism, where a rejection penalty factor is introduced to add penalty cost. Then, the 0–1 programming model with system optimization is constructed, where genetic annealing algorithm solves the large‐scale calculation problem. The results from this case study illustrate the fixed‐time allocation mode has more balanced utilization of parking facilities, whereas over‐utilization of preferred parking lots occurs in the real‐time model; additionally, when supply and demand are in balance, the fixed‐time model can obtain higher system revenues, reduce effective rejection rate by 9.43%, and increase resource utilization efficiency by 5.28%. In conclusion, the real‐time allocation mode reflects the advantage of a user's optimal allocation mode when supply is greater than demand; conversely, the fixed‐time allocation mode has the advantage of optimum system resources utilization efficiency.
Parking management plays an important role in the efficiency of transportation systems. We study the problem of matching shared parking slots with demanders online on a parking platform with the aim ...of managing parking spaces intelligently and efficiently. In our setting, the demanders submitting parking requests sequentially are assumed to be drawn from a distribution over demander types. No further assumptions about the demander arrival stream are needed. Matching decisions are triggered by arrival events instead of time points. This benefits the demanders by immediate responses to parking requests. An online matching algorithm is designed to make real‐time decisions and maximize social welfare. We demonstrated that the algorithm has a large competitive ratio, which implies a good performance of the algorithm. Numerical experiments are conducted to further examine the performance of the algorithm. The experimental results show that our proposed algorithm can achieve such a large competitive ratio in a real‐time matching way as the batch matching algorithm does in a serious delay matching way.
Automatic parking system (APS) plays an important role in alleviating drivers' burden and improving vehicle safety. This study proposes a laser-based simultaneous localisation and mapping (SLAM) ...automatic parallel parking and tracking control scheme, which include environment perception and reconstruction, parking path planning, and path following. First, the proposed approach selects short-range lidar to enlarge its perceptron on parking environment and senses available parking space to build neighbourhood mapping with the help of simultaneous localisation. The test evaluation indicates that laser-SLAM has achieved good performance for recognising free spaces. Then, scene-dependent B-spline parallel parking path planning algorithm is proposed to optimise the parking path curve and makes the path meet vehicle kinematics model constrains well, in which vehicle actuator's dynamic performance and parking path smoothness are taken as key attributes. The experiment results indicate that the designed parallel parking path is better smooth. At last, a path following control method based on model predictive control (MPC) algorithm is proposed. The authors built an intelligent vehicle APS platform, and the results indicate that the designed MPC algorithms has excellent robustness and can minimise the tracking error. All the proposed scheme is quantitatively evaluated using different parking scene simulation with satisfactory performance.
Recently, we see an increasing number of vehicles coming into our lives, which makes finding car parks a difficult task. To overcome this challenge, efficient and advanced parking techniques are ...required, such as finding the proper parking slot, increasing users’ experience, dynamic path planning and congestion avoidance. To this end, this survey provides a detailed overview starting from Smart Parking (SP) towards the emerging Autonomous Valet Parking (AVP) techniques. Specially, the SP includes digitally enhanced parking, smart routing, high density parking and vacant slot detection solutions. Moreover, the AVP involves Short-range Autonomous Valet Parking (SAVP) and Long-range Autonomous Valet Parking (LAVP). Finally, open issues and future work are provided.
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