The Illegal Parking Score
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2022 |
| Outros Autores: | , , |
| Tipo de documento: | Artigo |
| Idioma: | eng |
| Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| Texto Completo: | http://hdl.handle.net/10362/142443 |
Resumo: | 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. |
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The Illegal Parking ScoreUnderstanding and predicting the risk of parking illegalities in Lisbon based on spatiotemporal featuresillegal parkingtransportationsimulatordecision-supporturban planningGeography, Planning and DevelopmentTransportationUrban StudiesSDG 9 - Industry, Innovation, and InfrastructureSDG 11 - Sustainable Cities and CommunitiesJardim, 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.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNJardim, BrunoAlpalhão, NunoSarmento, PedroNeto, Miguel de Castro2022-07-26T22:26:16Z2022-09-012022-09-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article11application/pdfhttp://hdl.handle.net/10362/142443eng2213-624XPURE: 45614388https://doi.org/10.1016/j.cstp.2022.07.011info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-05-22T18:03:53Zoai:run.unl.pt:10362/142443Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-22T18:03:53Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
| dc.title.none.fl_str_mv |
The Illegal Parking Score Understanding and predicting the risk of parking illegalities in Lisbon based on spatiotemporal features |
| title |
The Illegal Parking Score |
| spellingShingle |
The Illegal Parking Score Jardim, Bruno illegal parking transportation simulator decision-support urban planning Geography, Planning and Development Transportation Urban Studies SDG 9 - Industry, Innovation, and Infrastructure SDG 11 - Sustainable Cities and Communities |
| title_short |
The Illegal Parking Score |
| title_full |
The Illegal Parking Score |
| title_fullStr |
The Illegal Parking Score |
| title_full_unstemmed |
The Illegal Parking Score |
| title_sort |
The Illegal Parking Score |
| author |
Jardim, Bruno |
| author_facet |
Jardim, Bruno Alpalhão, Nuno Sarmento, Pedro Neto, Miguel de Castro |
| author_role |
author |
| author2 |
Alpalhão, Nuno Sarmento, Pedro Neto, Miguel de Castro |
| author2_role |
author author author |
| dc.contributor.none.fl_str_mv |
NOVA Information Management School (NOVA IMS) Information Management Research Center (MagIC) - NOVA Information Management School RUN |
| dc.contributor.author.fl_str_mv |
Jardim, Bruno Alpalhão, Nuno Sarmento, Pedro Neto, Miguel de Castro |
| dc.subject.por.fl_str_mv |
illegal parking transportation simulator decision-support urban planning Geography, Planning and Development Transportation Urban Studies SDG 9 - Industry, Innovation, and Infrastructure SDG 11 - Sustainable Cities and Communities |
| topic |
illegal parking transportation simulator decision-support urban planning Geography, Planning and Development Transportation Urban Studies SDG 9 - Industry, Innovation, and Infrastructure SDG 11 - Sustainable Cities and Communities |
| description |
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. |
| publishDate |
2022 |
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2022-07-26T22:26:16Z 2022-09-01 2022-09-01T00:00:00Z |
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info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/article |
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http://hdl.handle.net/10362/142443 |
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eng |
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eng |
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2213-624X PURE: 45614388 https://doi.org/10.1016/j.cstp.2022.07.011 |
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openAccess |
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11 application/pdf |
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