Exploiting New Sensor Technologies for Real-Time Parking Prediction in Urban Areas

This paper proposes a methodological framework - based on survival analysis and neural networks - to provide parking availability forecasts for extended prediction horizons. Two different types of predictions are provided: i. the probability of a free space to continue being free in subsequent time intervals, and ii. the short-term parking occupancy prediction in selected regions of an urban road network. The available data comes from a wide network of parking sensors installed on-street in the “smart” city of Santander, Spain. The sensor network is segmented in four different regions and, then, survival and neural network models are developed for each region separately. Findings show that the Weibull parametric models best describe the probability of a space continuing to be free in the forthcoming time intervals. Simple genetically optimized Multilayer Perceptrons accurately predict region parking occupancy up to 1 hour in the future by only exploiting 5 minute data. Finally, the real time, web based, implementation of the proposed parking prediction availability system is presented.