Where is that car parked? A wireless sensor network-based approach to detect car positions

Burgstahler, Daniel; Knapp, F.; Zöller, Sebastian; Rückelt, Tobias; Steinmetz, Ralf

doi:10.1109/lcnw.2014.6927697

Cited by 9 publications

(7 citation statements)

References 7 publications

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“…Other researches tried to analyze the parking lot problem from different aspects. Car parking vacancy detection schemes were proposed to help drivers to locate the parking space [16], [17]. The methodologies included neural network based on visual feature extraction from parking space as well as machine learning through sensor captured data.…”

Section: Related Workmentioning

confidence: 99%

Cost and Time-Integrated Road-to-Park Cruising Prevention Scheme in Smart Transportation

Hung

Tsang

et al. 2019

IEEE Access

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Vehicle cruising for a destination-nearby parking lot has been a problem to drivers in cities. This problem causes drivers to consume unexpected time and resources to compete for a parking lot. Conventional parking schemes are in place to predict or reserve the parking spaces. Some of these schemes optimize the total cost only for locating parking spaces and road traffic condition is usually ignored. In this investigation, key road traffic conditions, such as vehicle queueing at the intersection of roads and parking fee information, are considered since they influence time and cost in the parking process. As such, a new comprehensive parking scheme is needed in place to assist drivers to identify the appropriate parking lot, estimate the transportation time to reach the parking lot, as well as the parking expenses. In essence, the total journey time and the total cost are important factors. However, former research lacked a harmonious consideration of these two factors. In this paper, a new parking model (PM) is developed for a holistic optimization of time and cost. The performance is measured by a newly defined indicator, referred to as a figure of merit, which is a fusion of time and cost. The analysis reveals that the incorporation of PM improves the performance by 24% ∼ 43%.

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Section: Related Workmentioning

confidence: 99%

Cost and Time-Integrated Road-to-Park Cruising Prevention Scheme in Smart Transportation

Hung

Tsang

et al. 2019

IEEE Access

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“…Important observations from Table 4 are • In [1], researchers' develop an operational system by using machine learning based approach to monitor dense traffic in mess environments on roads at slightly higher computation cost. • Nice systems reported in [7,38,39] use expensive sensors and requires special arrangements to decide where a car is parked. • Framework proposed in [10] uses only a single detection and recognition algorithm on a very large database.…”

Section: Constraints/applications Of the Developed Systemmentioning

confidence: 99%

Towards a fully automated car parking system

Mahmood

Haneef

Muhammad

et al. 2018

IET Intelligent Transport Systems

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“…There are many Parking Guidance System (PGS) approaches [1] which face the parking problem and contribute to smart parking. They vary in costs and also in necessary infrastructure.…”

Section: A Infrastructure-based Solutionsmentioning

confidence: 99%

“…Sensors placed on the ground, can detect accurately the position of parked vehicles [1]. However, such solutions are costly to deploy and maintain.…”

Section: Future Workmentioning

confidence: 99%

“…Among many different sorts of congestion like the standard network flow congestion, there are also many forms of parking-related congestion [5]. This causes undesirable problems such as environmental issues, e.g., (1) air pollution/noise increment, (2) energy consumption, and (3) parking space shortage [28]. Studies conducted between 1927 and 2001 in the center of major business districts in eleven cities on four continents came to the conclusion that up to 30% of vehicles in a traffic jam are looking for a parking space and the average time to find space available is eight minutes [30].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

parkITsmart: Minimization of Cruising for Parking

Tsiaras

Hobi

Hofstetter

et al. 2015

2015 24th International Conference on Computer Communication and Networks (ICCCN)

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Finding a parking space in urban areas is a daily challenge for drivers across the world, due to the increasing amount of vehicles and the limited amount of parking spaces. Drivers who are looking for a parking space in peak hours are often forced to drive around city blocks until they spot a free parking space. This process is termed in literature "cruising for parking" and is proven to (a) cost a lot of time and gas for drivers, (b) generate unnecessary traffic load, and (c) affect the environment negatively due to increased vehicle emissions. This work proposes a Parking Monitoring and Management System (PMMS) that collects, processes, and presents data about available parking spaces and their tariffs within a geographical region. The end-user application of the PMMS, parkITsmart, delivers at drivers bird's-eye view concerning the parking availability. To facilitate this, the PMMS gathers data from drivers', vehicles, their mobile phones, and Parking Inspectors (PIs). This work shows that in the Internet-of-Things (IoT) environment, "pairing" cars and drivers' mobile phones, collecting data from their sensors, and from PIs in a parking monitoring and management system, can decrease significantly cruising times for parking and can increase the time demands of the parking controlling process. Abstract-Finding a parking space in urban areas is a daily challenge for drivers across the world, due to the increasing amount of vehicles and the limited amount of parking spaces. Drivers who are looking for a parking space in peak hours are often forced to drive around city blocks until they spot a free parking space. This process is termed in literature "cruising for parking" and is proven to (a) cost a lot of time and gas for drivers, (b) generate unnecessary traffic load, and (c) affect the environment negatively due to increased vehicle emissions. This work proposes a Parking Monitoring and Management System (PMMS) that collects, processes, and presents data about available parking spaces and their tariffs within a geographical region. The end-user application of the PMMS, parkITsmart, delivers at drivers bird's-eye view concerning the parking availability. To facilitate this, the PMMS gathers data from drivers', vehicles, their mobile phones, and Parking Inspectors (PIs). This work shows that in the Internet-of-Things (IoT) environment, "pairing" cars and drivers' mobile phones, collecting data from their sensors, and from PIs in a parking monitoring and management system, can decrease significantly cruising times for parking and can increase the time demands of the parking controlling process.

show abstract

Where is that car parked? A wireless sensor network-based approach to detect car positions

Cited by 9 publications

References 7 publications

Cost and Time-Integrated Road-to-Park Cruising Prevention Scheme in Smart Transportation

Cost and Time-Integrated Road-to-Park Cruising Prevention Scheme in Smart Transportation

Towards a fully automated car parking system

parkITsmart: Minimization of Cruising for Parking

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