Prediction-based parking allocation framework in urban environments

ABSTRACT

Finding a parking space is usually challenging in urban areas. The literature shows that 30% of traffic congestion is caused by searching for parking spaces, which results in unnecessary energy consumption and environmental pollution. With the development of sensor technologies, smart parking guidance systems provide users with a variety of real-time parking space information. However, users cannot know whether the target parking space remains available upon arrival. Moreover, parking resources may be under competition when multiple users target the same open parking space. In this research, we develop a new framework named prediction-based parking allocation (PPA) that provides smart parking services to users. In PPA, we first construct a prediction model of parking occupancy and predict the subsequent parking availabilities. Then, we design a matching-based allocation strategy to assign users to selected parking spaces. To the best of our knowledge, this is the first study that combines occupancy prediction and space allocation simultaneously to address smart parking issues. Finally, we collect a real dataset from the SFPark on-street parking system for performance evaluation. According to experimental results, PPA can effectively increase the parking success rate and reduce costs, fuel consumption, and carbon emissions.

KEYWORDS:

• Parking occupancy prediction
• parking space allocation
• smart parking
• SFPark
• data mining

Disclosure statement

No potential conflict of interest was reported by the authors.

Data and codes availability statement

The data and codes that support the findings of this study are available with a DOI at http://doi.org/10.6084/m9.figshare.11371902.

Additional information

Funding

This work was supported by the Ministry of Science and Technology, Taiwan [MOST 105-2119-M-006-030].

Notes on contributors

Eric Hsueh-Chan Lu

Eric Hsueh-Chan Lu is currently an associate professor in the Department of Geomatics, National Cheng Kung University (NCKU), Taiwan, R.O.C. Before this, he was an assistant professor in the Department of Computer Science and Information Engineering, National Taitung University (NTTU), Taiwan, R.O.C. during August 2012 and July 2014. From October 2011 to July 2012, he was a postdoctoral fellow in Academia Sinica and National Cheng Kung University, respectively. Dr. Lu received his Ph.D. degree from National Cheng Kung University, Taiwan, R.O.C., in 2010, majored in computer science and information engineering. Dr. Lu has a wide variety of research interests covering data mining, database systems, location-based services, geographic information systems, mobile computing, intelligent transportation systems, social networking and cloud computing. He has published more than 40 research papers in referred journals and international conferences. He received the best student paper award of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks (LBSN). He is a member of IEEE and has also served as reviewers for a number of international conferences related to data mining and mobile application.

Chen-Hao Liao

Chen-Hao Liao received the B.S. and Master degree in the Department of Geomatics at National Cheng Kung University (NCKU), Taiwan, R.O.C. in 2015 and 2017 respectively. His research interests include data mining, location-based services, intelligent transportation systems and geographic information systems.