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Jardim, B., Alpalhão, N., Sarmento, P., & Neto, M. D. C. (2022). The Illegal Parking Score: Understanding and predicting the risk of parking illegalities in Lisbon based on spatiotemporal features. Case Studies on Transport Policy, 10(3), 1816-1826. https://doi.org/10.1016/j.cstp.2022.07.011------This work was supported by the Connecting Europe Facility (CEF) – Telecommunications sector in the framework of project Urban Co-Creation Data Lab [INEA/CEF/ICT/A2018/1837945]. This work was also supported by Portuguese national funds through FCT (Fundação para a Ciência e a Tecnologia) under research grant FCT UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC), as well as the project C-TECH—Climate Driven Technologies for Low Carbon Cities (POCI-01-0247 FEDER-045919 | LISBOA-01-0247-FEDER-045919) co-financed by the ERDF European Regional Development Fund through the Operational Program for Competitiveness and Internationalization COMPETE 2020, the Lisbon Portugal Regional Operational Program LISBOA 2020 and by the Portuguese Foundation for Science and Technology FCT under MIT Portugal Program. The authors would also like to thank the Municipal Police of the Lisbon City Council for providing the data on parking illegalities used in this work. 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. publishersversion published |
