Social Parking: Applying the Citizens as Sensors Paradigm to Parking Guidance and Information

Nowadays, the problem of parking guidance information (PGI) is one of the great challenges of smart cities. Sensor networks have been traditionally used, but they sometimes constitute a high administrative cost. For this reason, this paper presents social parking, a system that is based on the citizens as sensors paradigm, where data are collected by users and are processed using data mining techniques. Moreover, an ontology is used to enable the standardization of information. This way, social parking is compatible with the FIWARE platform. A forecast algorithm was also designed and verified to estimate the number of free parking spots inside a parking lot. With this aim, we used public parking data from eight parking lots in the city of Zaragoza. Client applications allowed testing of all the functions of the parking system. These tests were carried out in three experimental parking lots in the city of Malaga.

[1]  Qian Liu,et al.  Workplace Parking Provision and Built Environments: Improving Context-Specific Parking Standards Towards Sustainable Transport , 2019, Sustainability.

[2]  Howard Rheingold,et al.  Smart Mobs: The Next Social Revolution , 2002 .

[3]  Ben-Jye Chang,et al.  Wireless Sensor Network-Based Adaptive Vehicle Navigation in Multihop-Relay WiMAX Networks , 2008, 22nd International Conference on Advanced Information Networking and Applications (aina 2008).

[4]  Mohamed Benaddy,et al.  Development of Semantic Web applications: state of art and critical review , 2018, ICEMIS '18.

[5]  Panta Lucic,et al.  Intelligent parking systems , 2006, Eur. J. Oper. Res..

[6]  B. McLellan,et al.  Total material requirement for the global energy transition to 2050: A focus on transport and electricity , 2019, Resources, Conservation and Recycling.

[7]  Ioannis Chatzigiannakis,et al.  A privacy-preserving smart parking system using an IoT elliptic curve based security platform , 2016, Comput. Commun..

[8]  D. Edwards.  Data Mining: Concepts, Models, Methods, and Algorithms , 2003 .

[9]  Xiaodong Lin,et al.  An Intelligent Secure and Privacy-Preserving Parking Scheme Through Vehicular Communications , 2010, IEEE Transactions on Vehicular Technology.

[10]  Yasuo Asakura,et al.  Effects of parking availability information on system performance: a simulation model approach , 1994, Proceedings of VNIS'94 - 1994 Vehicle Navigation and Information Systems Conference.

[11]  C.J.H. Mann,et al.  Handbook of Data Mining and Knowledge Discovery , 2004 .

[12]  Ram Rajagopal,et al.  Parking Sensing and Information System: Sensors, Deployment, and Evaluation , 2016, ArXiv.

[13]  Daeyoung Kim,et al.  PGS: Parking Guidance System based on wireless sensor network , 2008, 2008 3rd International Symposium on Wireless Pervasive Computing.

[14]  Yi Huang,et al.  Smart Parking Guidance, Monitoring and Reservations: A Review , 2017, IEEE Intelligent Transportation Systems Magazine.

[15]  Luis Muñoz,et al.  On the Use of Information and Infrastructure Technologies for the Smart City Research in Europe: A Survey , 2018, IEICE Trans. Commun..

[16]  Steven C. Wheelwright,et al.  Forecasting: Methods and Applications, 3rd Ed , 1997 .

[17]  Felix Caicedo.  Real-time parking information management to reduce search time, vehicle displacement and emissions , 2010 .

[18]  R. Kummitha,et al.  How do we understand smart cities? An evolutionary perspective , 2017 .

[19]  Christos G. Cassandras,et al.  New “Smart Parking” System Based on Resource Allocation and Reservations , 2013, IEEE Transactions on Intelligent Transportation Systems.

[20]  Rita L. Sallam,et al.  Magic Quadrant for Business Intelligence and Analytics Platforms , 2013 .

[21]  Seyed M. Buhari,et al.  A cross-layer framework for sensor data aggregation for IoT applications in smart cities , 2016, 2016 IEEE International Smart Cities Conference (ISC2).

[22]  Hrishikesh Venkataraman,et al.  Greening the economy: A review of urban sustainability measures for developing new cities , 2017 .

[23]  José López Vicario,et al.  Autonomous Car Parking System through a Cooperative Vehicular Positioning Network , 2017, Sensors.

[24]  Dorian Pyle,et al.  Data Preparation for Data Mining , 1999 .

[25]  Dieter Fensel,et al.  Knowledge Engineering: Principles and Methods , 1998, Data Knowl. Eng..

[26]  Petros A. Ioannou,et al.  On-Street and Off-Street Parking Availability Prediction Using Multivariate Spatiotemporal Models , 2015, IEEE Transactions on Intelligent Transportation Systems.

[27]  Beniamino Murgante,et al.  Cities and Smartness: A Critical Analysis of Opportunities and Risks , 2013, ICCSA.

[28]  Lillian J. Ratliff,et al.  How Much Urban Traffic is Searching for Parking? , 2017, ArXiv.

[29]  Amit P. Sheth,et al.  Citizen Sensing, Social Signals, and Enriching Human Experience , 2009, IEEE Internet Computing.

[30]  Emiliano Miluzzo,et al.  People-centric urban sensing , 2006, WICON '06.

[31]  Jagruti Sahoo,et al.  Agile Urban Parking Recommendation Service for Intelligent Vehicular Guiding System , 2014, IEEE Intelligent Transportation Systems Magazine.

[32]  Je Agar,et al.  Constant Touch: a Global History of the Mobile Phone , 2004 .

[33]  Flora D. Salim,et al.  Urban computing in the wild: A survey on large scale participation and citizen engagement with ubiquitous computing, cyber physical systems, and Internet of Things , 2015, Int. J. Hum. Comput. Stud..