Considering the increase of urban population and traffic congestion, smart parking is always a strategic issue to work on, not only in the research field, but also from economic interests. Thanks to information and communication technology evolution, drivers can more efficiently find satisfying parking spaces with smart parking services. The existing and ongoing works on smart parking are complicated and transdisciplinary. While deploying a smart parking system, cities, as well as urban engineers, need to spend a very long time to survey and inspect all the possibilities. Moreover, many varied works involve multiple disciplines, which are closely linked and inseparable. To give a clear overview, we introduce a smart parking ecosystem and propose a comprehensive and thoughtful classification by identifying their functionalities and problematic focuses. We go through the literature over the period of 2000–2016 on parking solutions as they were applied to smart parking development and evolution, and propose three macro-themes: information collection, system deployment, and service dissemination. In each macro-theme, we explain and synthesize the main methodologies used in the existing works and summarize their common goals and visions to solve current parking difficulties. Finally, we give our engineering insights and show some challenges and open issues. Our survey gives an exhaustive study and a prospect in a multidisciplinary approach. Besides, the main findings of the current state-of-the-art throw out recommendations for future research on smart cities and the Internet architecture.

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A Survey of Smart Parking Solutions

Handbook of Approximation: Algorithms and Metaheuristics

With the proliferation of location-based services, mobile devices, and embedded wireless sensors, more and more applications are being developed to improve the efficiency of the transportation system. In particular, new applications are arising to help vehicles locate open parking spaces. Nevertheless, while engaged in driving, travelers are better suited being guided to a particular and ideal parking slot, than looking at a map and choosing which spot to go to. Then the question of how an application should choose this ideal parking spot becomes relevant.Vehicular parking can be viewed as vehicles (players) competing for parking slots (resources with different costs). Based on this competition, we present a game-theoretic framework to analyze parking situations. We introduce and analyze Parking Slot Assignment Games (Psag) in complete and incomplete information contexts. For both models we present algorithms for individual players to choose parking spaces ideally. To evaluate the more realistic incomplete information Psag, simulations were performed to test the performance of various proposed algorithms.

/pdf/parking-slot-assignment-games-4qxemp9qhz.pdf

Parking slot assignment games

A fundamental application in vehicular ad-hoc networks (VANETs) is the discovery of available parking spaces as vehicles navigate through urban road networks. Vehicles are now capable of finding such parking spots using their on-board sensing and computational infrastructure and then they can disseminate this information for use by other members of the travelling community in the geographic vicinity. In this context, we examine the problem of locating an available parking space for a vehicle entering an urban network of roads. Upon its entry, a vehicle has to determine the best way to visit parking spots reported to be free. In deciding this, the vehicle has to consider the time required to reach each candidate position, its distance from the final destination should the driver walk, and of course, the probability that the spot(s) will be still-free once the vehicle shows up at location. We formulate the question at hand as a Time-Varying Travelling Salesman problem and we propose an approach for computing the route that a vehicle must traverse in order to visit all parking spaces known to be available. Our method takes into account the limited computational resources of vehicles and attempts to find the best feasible trip. This is done in conjunction with a cost function that estimates the probability to find a space filled. In order to ascertain the effectiveness of our proposal, we compare it with a best-first approach and examine computational overheads. We also investigate how close to optimal results our approach comes.

http://cgi.di.uoa.gr/~ad/Publications/VeEfDe-mdm2011.pdf

Reaching Available Public Parking Spaces in Urban Environments Using Ad Hoc Networking

Parking availability information (or occupancy of parking facility) is highly valued by travelers, and is one of the most important inputs to many parking models. This paper proposes a model-based practical framework to predict future occupancy from historical occupancy data alone. The framework consists of two modules: estimation of model parameters, and occupancy prediction. At the core of the predictive framework, a queuing model is employed to describe the stochastic occupancy change of a parking facility. While the underlying queuing model can be any reasonable model, we demonstrate the framework with the well-established continuous-time Markov M\M\C\C queue in this paper. The possibility of adopting a different queuing model that can potentially incorporate the parking-searching process is also discussed. The parameter estimation module and the occupancy prediction module are both built on the underlying queuing model. To apply the estimation and prediction methods in real world, a few practical considerations are accounted for in the framework with methods to handle variations of arrival and departure patterns from day to day and within a day, including special events. The proposed framework and models are validated using both simulated and real data. Our San Francisco case studies demonstrate that the parameters estimated offline can lead to accurate predictions of parking facility occupancy both with and without real-time update. We also performed extensive numerical experiments to compare the proposed framework and methods with several pure machine-learning methods in recent literature. It is found that our approach delivers equal or better performance, but requires a computation time that is orders of magnitude less to tune and train the model. Additionally, our approach can predict for any time in the future with one training process, while pure machine-learning methods have to train a specific model for a different prediction interval to achieve the same level of accuracy.

https://par.nsf.gov/servlets/purl/10066458

How likely am I to find parking? – A practical model-based framework for predicting parking availability

textNavigational systems assist drivers in finding a route between two locations that is time optimal in theory but seldom in practice due to delaying circumstances the system is unaware of, such as traffic jams Upon arrival at the destination the service of the system ends and the driver is forced to locate a parking place without further assistance We propose a City Based Parking Routing System (CBPRS) that monitors and reserves parking places for CBPRS participants within a city The CBPRS guides vehicles using an ant based distributed hierarchical routing algorithm to their reserved parking place Through means of experiments in a simulation environment we found that reductions of travel times for participants were significant in comparison to a situation where vehicles relied on static routing information generated by the well known Dijkstra’s algorithm Furthermore, we found that the CBPRS was able to increase city wide traffic flows and decrease the number and duration of traffic jams throughout the city once the number of participants increased

CBPRS: A City Based Parking and Routing System

This paper is about the introduction of an electric car rental system in the city of Amsterdam. These cars can be used as an alternative to public transport or taxis by travelers or tourists arrivi...

J.L. Boehlé

Papers

CBPRS: A City Based Parking and Routing System

A rental system of electrical cars in Amsterdam

CBPRS: A City Based Parking and Routing System