Deep Learning for On-Street Parking Violation Prediction

Illegal parking along with the lack of available parking spaces are among the biggest issues faced in many large cities. These issues can have a significant impact on the quality of life of citizens. On-street parking systems have been designed to this end aiming at ensuring that parking spaces will be available for the local population, while also providing easy access to parking for people visiting the city center. However, these systems are often affected by illegal parking, providing incorrect information regarding the availability of parking spaces. Even though this can be mitigated using sensors for detecting the presence of cars in various parking sectors, the cost of these implementations is usually prohibiting large. In this paper, we investigate an indirect way of predicting parking violations at a fine-grained level, equipping such parking systems with a valuable tool for providing more accurate information to citizens. To this end, we employed a Deep Learning (DL)-based model to predict fine-grained parking violation rates for on-street parking systems. Moreover, we developed a data augmentation and smoothing technique for further improving the accuracy of DL models under the presence of missing and noisy data. We demonstrate, using experiments on real data collected in Thessaloniki, Greece, that the developed system can indeed provide accurate parking violation predictions.