PARKING

Identification of patterns and prediction of parking in the city of Lisbon to improve surveillance efficiency.

 

Objective

To create new models either to predict or to generate viable alternatives for illegal parking in the city.


Research question

What are the characteristics of the locations where exists an higher number of occurrences regarding irregular parking?

Where and at what time of day is expected to exist an higher number of illegalities regarding irregular parking?

 

Challenge

 

Challenge Brainstorming Session

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Data understanding

 

Information about occurrences of irregular parking for 2018 and 2019 in Lisbon registed by the municipal police (60 000 occurrences).

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Less abusive parking in Easter, Summer and Christmas vacations season.

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 Abusive parking during weekdays between 8h to 11h.

Less concentrated between 14h and 19h.

Abusive parking during weekdays between 8h to 11h. Less concentrated between 14h and 19h.

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Abusive parking in Campo Santa Clara (56 occurrences).

 

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Data preparation: Machine Learning

 

Irregular parking occurrences along with contextual data were aggregated at census tract level.

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Data preparation: Time Series

 

Irregular parking occurrences data was aggregated at street level in time bins of 3 hours

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Main characteristics of the locations where exists irregular parking

 

Correlation between contextual data within each census tract and irregular parking.

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Modeling: Machine Learning

 

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Literature review

 

  • Albino, V., Berardi, U. & Dangelico, R. M., 2015. Smart cities: Definitions, dimensions, performance, and initiatives. Journal of Urban Technology, 22(1), pp. 3-21.
  • Aljoufie, M., 2016. Analysis of Illegal Parking Behavior in Jeddah. Current Urban Studies, 04(04), pp. 393-408.
  • Anon., s.d. IoT-Based Smart City Development using Big Data.
  • Barone, R. E. et al., 2014. Architecture for parking management in smart cities. IET Intelligent Transport Systems, 8(5), pp. 445-452.
  • Barriga, J. J. et al., 2019. Smart parking: A literature review from the technological perspective. s.l.:MDPI AG.
  • Caragliu, A., del Bo, C. & Nijkamp, P., 2011. Smart cities in Europe. Journal of Urban Technology, 4, 18(2), pp. 65-82.
  • Gao, S. et al., 2019. Predicting the spatiotemporal legality of on-street parking using open data and machine learning. Annals of GIS, 2 10, 25(4), pp. 299-312.
  • Giuffrè, T., Siniscalchi, S. M. & Tesoriere, G., 2012. A Novel Architecture of Parking Management for Smart Cities. Procedia - Social and Behavioral Sciences, 10, Volume 53, pp. 16-28.
  • Grossi, G. & Pianezzi, D., 2017. Smart cities: Utopia or neoliberal ideology?. Cities, 1 9, Volume 69, pp. 79-85.
  • Hélio, M. & Silva, R., 2017. Predicting Space Occupancy for Street Paid Parking, s.l.: s.n.
  • Kodransky, M. & Hermann, G., 2011. Europe's Parking U-Turn: From Accommodation to Regulation, s.l.: s.n.
  • Morillo Carbonell, C. & Campos Cacheda, J. M., 2016. EFFECT OF ILLEGAL ON-STREET PARKING ON TRAVEL TIMES IN URBAN ENVIRONMENT. s.l., Universitat Politecnica de Valencia.
  • My Thanh, T. T. & Friedrich, H., 2017. Legalizing the illegal parking, a solution for parking scarcity in developing countries. s.l., Elsevier B.V., pp. 4950-4965.
  • Parmar, J., Das, P. & Dave, S. M., 2020. Study on demand and characteristics of parking system in urban areas: A review. s.l.:Periodical Offices of Chang- an University.
  • Tsakalidis, A. & Tsoleridis, P., 2015. The impacts of illegal parking on the urban areas' traffic and environmental conditions: The case of the city of Thessaloniki. Spatium, 1(33), pp. 41-46.
  • Vlahogianni, E. I., Kepaptsoglou, K., Tsetsos, V. & Karlaftis, M. G., 2016. A Real-Time Parking Prediction System for Smart Cities. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 3 3, 20(2), pp. 192-204.

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