A Real-Time Parking Prediction System for Smart Cities

Abstract

A methodological framework for multiple steps ahead parking availability prediction is presented. Two different types of predictions are provided: the probability of a free space to continue being free in subsequent time intervals, and the short-term parking occupancy prediction in selected regions of an urban road network. The available data come from a wide network of on-street parking sensors 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 parking space to continue to be free in the forthcoming time intervals. Moreover, simple genetically optimized multilayer perceptrons accurately predict region parking occupancy rates up to 30 minutes in the future by exploiting 1-minute data. Finally, the real time, Web-based, implementation of the proposed parking prediction availability system is presented.

Keywords:

• Duration Modeling
• Internet of Things
• Neural Networks
• Parking Occupancy
• Parking Sensors
• Smart City

This work is part of the MITOS experiment, incorporated in the SmartSantader project and funded by the European Union Seventh Framework Programme (FP7).