Illegal parking represents a costly problem for most cities as it leads to an increase in traffic congestion and emission of air pollutants, and decreases pedestrian, biking, and driving safety, making cities less clean, secure, and attractive to citizens and tourists. Most decision-support systems employed to deal with parking illegalities rely on cameras and video-processing algorithms to capture infractions in real-time. Despite being effective, their implementation is costly and challenging due to road environment conditions. On the other hand, studies that relay on spatiotemporal features to predict infractions can present a more efficient alternative, one that is less costly to implement and free of environment and spatial conditioning. In this work, we propose the Illegal Parking Score (IPS), a score of the conditional probability of illegal parking occurring in a road segment, based on spatiotemporal conditions, and able to distinguish between illegality types. The IPS is calculated for the Lisbon Municipality, in Portugal, and it is supported by a Light Gradient Boosting Machine model that allows for IPS prediction for unseen conditions. Likewise, we propose the IPS Simulator, a simulation tool that allows for users to infer the IPS by defining spatiotemporal conditions. This system will be deployed in the Lisbon City Council and provides responsible authorities with a tool to support their daily operations and promote sustainable transport and demand planning, by identifying and monitoring critical zones and by aiding in the design and gauge of parking regulation.
- illegal parking
- urban planning