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Searching parking spaces in urban environments based on non-stationary Poisson process analysis

Searching parking spaces in urban cities has been a problem in development of intelligent transportation system for a long time. The existing methods usually require numerous sensors to collect vacancy information, and a central server shares and updates the real-time information on request. However, this sensor-based scheme often demands huge investment on infrastructure construction and maintenance. We propose a cost-effective parking space searching method by prediction with time-varying analysis of historical data. We model the discrete occupancy rates of the parking lots as non-stationary Poisson processes, whist taking consideration of traveling cost with different directions of the parking lots. The case study on the urban city area demonstrates that the proposed search method is feasible to be applied to real world efficiently and cost-effectively.