Intelligent parking reservation service on the Internet

Shibui, M.; Naganawa, T.; Ogiwara, Miyoko; Yoshikai, N.

doi:10.1109/saintw.2001.998224

Cited by 30 publications

(12 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kumar et al [110] also presented a Zigbeebased smart parking system that provides parking monitoring, guidance, and parking reservation. Inaba et al [117] introduced a smart parking prototype system supported by the Japanese telephone operator NTT. Telephone users can easily find a parking space and make a reservation by using the Internet.…”

Section: Parking System Deploymentmentioning

confidence: 99%

A Survey of Smart Parking Solutions

Lin

Rivano

Mouël

2017

IEEE Trans. Intell. Transport. Syst.

422

185

View full text Add to dashboard Cite

Section: Parking System Deploymentmentioning

confidence: 99%

A Survey of Smart Parking Solutions

Lin

Rivano

Mouël

2017

IEEE Trans. Intell. Transport. Syst.

422

185

View full text Add to dashboard Cite

“…Although it is not free to use USSD for most of network operators, it is still a cheap and reliable technology to adopt in parking reservations. Inaba et al [18] utilized RFID tags to store and update the reservations status and they discussed the difference between real-time and share-time reservations where the difference between them is that in share-time reservations, drivers must use the service in a known entry and exit time frame as they share the resource time. Whereas in real-time reservations, they are allowed to park for unlimited interval of time for being independent on other drivers.…”

Section: Related Workmentioning

confidence: 99%

iParker—A New Smart Car-Parking System Based on Dynamic Resource Allocation and Pricing

Kotb

Shen

Zhu

et al. 2016

IEEE Trans. Intell. Transport. Syst.

173

View full text Add to dashboard Cite

Parking in major cities, particularly with dense traffic, directly effects the traffic flow and people's life. In this paper, we introduce a new smart parking system that is based on intelligent resource allocation, reservation, and pricing. The proposed system solves the current parking problems by offering guaranteed parking reservations with the lowest possible cost and searching time for drivers and the highest revenue and resource utilization for parking managers. New fair pricing policies are also proposed that can be implemented in practice. The new system is based on mathematical modeling using mixed-integer linear programming (MILP) with the objective of minimizing the total monetary cost for the drivers and maximizing the utilization of parking resources.Index Terms-Dynamic pricing, dynamic resource allocation, mixed integer linear programming (MILP), reservation, smart car parking.

show abstract

“…[2,3]. Advances in information and communication technologies have made it possible for PGIS to provide accurate updates in parking guidance information for drivers such as parking facilities' location, real-time availability of parking space, capacity, opening hours, parking price, predicted levels of occupancy and performance [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Short‐term forecasting of available parking space using wavelet neural network model

Tang

Blythe

et al. 2015

IET Intelligent Transport Systems

View full text Add to dashboard Cite

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.

show abstract

Intelligent parking reservation service on the Internet

Cited by 30 publications

References 0 publications

A Survey of Smart Parking Solutions

A Survey of Smart Parking Solutions

iParker—A New Smart Car-Parking System Based on Dynamic Resource Allocation and Pricing

Short‐term forecasting of available parking space using wavelet neural network model

Contact Info

Product

Resources

About