Finding available parking spaces made easy

We discuss the problem of predicting the number of available parking spaces in a parking lot. The parking lot is modeled by a continuous-time Markov chain, following Caliskan, Barthels, Scheuermann, and Mauve. The parking lot regularly communicates the number of occupied spaces, capacity, arrival and parking rate through a vehicular network. The navigation system in the vehicle has to compute from these data the probability of an available parking space upon arrival. We derive a structural result that considerably simplifies the computation of the transition probabilities in the navigation system of the vehicle.

[1]  Martin Mauve,et al.  Predicting Parking Lot Occupancy in Vehicular Ad Hoc Networks , 2007, 2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring.

[2]  Jiannong Cao,et al.  An Intelligent Car Park Management System based on Wireless Sensor Networks , 2006, 2006 First International Symposium on Pervasive Computing and Applications.

[3]  I. Dhillon.  Algorithm for the Symmetric Tridiagonal Eigenvalue/Eigenvector Problem , 1998 .

[4]  U. Hartmann,et al.  Parking monitor system based on magnetic field senso , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[5]  Masaaki Kijima,et al.  Markov processes for stochastic modeling , 1997 .

[6]  Ahmed Driss,et al.  A Takagi Factorization of a Real Symmetric Tridiagonal Matrix , 2008 .

[7]  Adrian Perrig,et al.  VANET-based approach for parking space availability , 2007, VANET '07.

[8]  W. Oertel,et al.  Determining car-park occupancy from single images , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[9]  Martin Mauve,et al.  Decentralized discovery of free parking places , 2006, VANET '06.

[10]  Wade Trappe,et al.  ParkNet: a mobile sensor network for harvesting real time vehicular parking information , 2009, MobiHoc S3 '09.

[11]  Marco Gruteser,et al.  ParkNet: drive-by sensing of road-side parking statistics , 2010, MobiSys '10.