Highlights We propose the Illegal Parking Score (IPS), a novel tool to measure, predict and simulate the risk of parking illegalities. The IPS is calculated in the Lisbon municipality, in Portugal. The downtown and surrounding areas are more prone to illegalities, namely of access conditioning. Weekdays have a higher IPS than weekends or holidays. The period of 10 am to 1 pm leads to more infractions. Higher temperatures and low precipitation result in a higher risk of observing a parking illegality. This tool can support local authorities and Lisbon Municipal Police in their daily operations. It can promote urban sustainable planning, by helping to identify areas that will profit from interventions and assist in the design and gauge of parking regulations.
Abstract 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.
The illegal parking score – Understanding and predicting the risk of parking illegalities in Lisbon based on spatiotemporal features
2022-07-22
11 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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