Understanding drivers’ perspective on parking guidance information

Ji, Yanjie; Guo, Aw; Blythe, PT; Tang, Dounan; Wang, Wei

doi:10.1049/iet-its.2012.0167

Cited by 34 publications

(13 citation statements)

References 18 publications

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“…Some of them had been deployed in practice like Parking Guidance and Information (PGI) systems [10]- [12]. PGI systems provide drivers with real time information on parking within controlled areas through variable message signs.…”

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.

181

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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.

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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.

181

View full text Add to dashboard Cite

show abstract

“…As studied in Caicedo [11], drivers that can obtain information about parking availability are 45% more successful than those cannot obtain parking availability information in their parking decision‐making process. In addition, managers and operators of parking facilities may foresee the performance of parking facilities and carry out timely intervention, strategic decisions to manage congestion and over‐demand for parking – indeed the Tyne and Wear Urban Traffic Management and Control (UTMC) centre used by Newcastle upon Tyne and for the base dataset provide prediction of parking availability on a 30 min headway and also configures the traffic management system to cope with significant ‘unusual flows’ of vehicles from large events [12]. Thus, accurate forecasting of APS information can also help urban traffic management and control department to ease traffic congestion and control traffic demand.…”

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

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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.

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“…Since their first introduction in the 70s in Germany, PGIs have been proliferated in many cities around the world. The highest concentration of PGI systems can be in Europe, UK and Japan [20]. PGI systems can include dynamic information about the entire city area or can be limited to a particular parking lot.…”

Section: Introductionmentioning

confidence: 99%

“…PGI systems can include dynamic information about the entire city area or can be limited to a particular parking lot. The systems are useful for reducing the traffic & parking search time and for improving the utilisation and management of parking resources [20]. Some few examples of PGI systems that incorporate mobile apps and online services are SFPark [21] and Streetline [22].…”

Section: Introductionmentioning

confidence: 99%

Smart City Technologies: Design and Evaluation of An Intelligent Driving Assistant for Smart Parking

Niculescu

Wadhwa

Quek

2016

International Journal on Advanced Science, Engineering and Information Technology

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Smart cities technologies are gradually changing our urban landscape thanks to the proliferation of billions of smart devices permanently connected through the internet. Among technologies with the highest impact on citizen's quality of life are intelligent transportation systems and in particular, smart parking applications. In this paper, we present a study evaluation the design of a smart parking assistant developed in our lab. The system is implemented as a mobile app with an integrated GUI adapted for Android tablets. The app extends common park guidance information systems (PGI) offering suggestions based on parking fee or proximity to the destination. Two novel features -beyond the state of the art of currently available systems -are added: the use of natural language and the ability to react in real-time to changes in parking occupancy. If the number of parking lots drops to a critical level, the application redirects the driver to another parking place. Furthermore, the app includes GPS and Google maps interfacing modules which enable the application to detect the driver location and calculate the nearest car park distance. A group of five experts with a background in interface design and natural language processing evaluated the prototype using Nielsen's set of heuristics in a think-loud approach. Results and implications for further interaction design are extensively discussed.

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Understanding drivers’ perspective on parking guidance information

Abstract: Understanding drivers' perspective on parking guidance information.

Cited by 34 publications

References 18 publications

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

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

Smart City Technologies: Design and Evaluation of An Intelligent Driving Assistant for Smart Parking

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