Activity 1

Activity 1: Data preparation and open data infrastructure assessment

 

Main objective
Prepare data and open data infrastructure for Smart Management Platform.


Tasks
• Task 1.1: Data definition and requirements definition
• Task 1.2: Services and Use Cases refinement

 

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Task 1.1 Data definition and requirements definition

The data requirements and their definition for each case study were in a first stage based on literature review, where were identified in previous studies the datasets needed to address the challenges in the framework of UCD Lab project. During this datasets exploration phase, regular meetings with the city of Lisbon were carried out to identify the datasets that the city of Lisbon has already collected in their transactional systems along with data available in Lisbon open data portal (Lisboa Aberta) that could be used in the development of each case study of the project.

Besides these meetings with the city of Lisbon, also meetings with external entities namely EMEL (a municipal company that manages parking and a bike sharing system in Lisbon) and contacts with CARRIS (a bus service provider in Lisbon) where made to identify datasets and their respective characteristics to be used in the development of the case studies.

 

Literature review

#1 - Micromobility

Abbasi, M. & Abduli, M. A., 2013. Forecasting Municipal Solid waste Generation by Hybrid Support Vector Machine and Partial Least Square Model. Int. J. Environ. Res, 7(1), pp. 27-38.

Ali Abdoli, M., Falah Nezhad, M., Salehi Sede, R. & Behboudian, S., 2012. Longterm forecasting of solid waste generation by the artificial neural networks. Environmental Progress and Sustainable Energy, 12, 31(4), pp. 628-636.

Al-Khatib, I. A. et al., 2015. Public perception of hazardousness caused by current trends of municipal solid waste management. Waste Management, 1 2, Volume 36, pp. 323-330.

Anilkumar, P. & Chithra, K., 2016. Land Use Based Modelling of Solid Waste Generation for Sustainable Residential Development in Small/Medium Scale Urban Areas. Procedia Environmental Sciences, Volume 35, pp. 229-237.

Biswas, A. & De, A. K., 2016. A Fuzzy Goal Programming Approach for Solid Waste Management Under Multiple Uncertainties. Procedia Environmental Sciences, Volume 35, pp. 245-256.

Hazra, I. T., 2017. Prediction of municipal solid waste generation for developing countries in temporal scale: A fuzzy inference system approach, s.l.: s.n.

Intharathirat, R., Abdul Salam, P., Kumar, S. & Untong, A., 2015. Forecasting of municipal solid waste quantity in a developing country using multivariate grey models. Waste Management, 1 5, Volume 39, pp. 3-14.

Kannangara, M., Dua, R., Ahmadi, L. & Bensebaa, F., 2018. Modeling and prediction of regional municipal solid waste generation and diversion in Canada using machine learning approaches. Waste Management, 1 4, Volume 74, pp. 3-15.

Kolekar, K., Hazra, T. & Chakrabarty, S., 2016. A Review on Prediction of Municipal Solid Waste Generation Models. Procedia Environmental Sciences, Volume 35, pp. 238-244.

Lebersorger, S. & Beigl, P., 2011. Municipal solid waste generation in municipalities: Quantifying impacts of household structure, commercial waste and domestic fuel. Waste Management, 9, 31(9-10), pp. 1907-1915.

Oribe-Garcia, I. et al., 2015. Identification of influencing municipal characteristics regarding household waste generation and their forecasting ability in Biscay. Waste Management, 1 5, Volume 39, pp. 26-34.

Pan, A., Yu, L. & Yang, Q., 2019. Characteristics and forecasting of municipal solid waste generation in China. Sustainability (Switzerland), 1 3.11(5).

Prades, M., Gallardo, A. & Ibàñez, M. V., 2015. Factors determining waste generation in Spanish towns and cities. Environmental Monitoring and Assessment, 20 11.187(1).

Rimaityte, I. et al., 2012. Application and evaluation of forecasting methods for municipal solid waste generation in an Eastern-European city. Waste Management and Research, 1, 30(1), pp. 89-98.

Solano Meza, J. K., Orjuela Yepes, D., Rodrigo-Ilarri, J. & Cassiraga, E., 2019. Predictive analysis of urban waste generation for the city of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks. Heliyon, 1 11.5(11).

Song, J. et al., 2014. Simulated annealing based hybrid forecast for improving daily municipal solid waste generation prediction. Scientific World Journal, Volume 2014.

Soni, U., Roy, A., Verma, A. & Jain, V., 2019. Forecasting municipal solid waste generation using artificial intelligence models—a case study in India. SN Applied Sciences, 2.1(2).

Town, J., Sudan JEWM, S., Lomeling, D. & Wani Kenyi, S., s.d. Forecasting weekly SW generation using ANNs and ARMA models in Forecasting solid waste generation in Juba Town, South Sudan using Artificial Neural Networks (ANNs) and Autoregressive Moving Averages (ARMA), s.l.: s.n.

Verma, A., Kumar, A. & Singh, N. B., 2019. Application of Multi Linear Model for Forecasting Municipal Solid Waste Generation in Lucknow City: A Case Study. Current World Environment, 31 12, 14(3), pp. 421-432.

Vieira, V. H. d. M. & Matheus, D. R., 2018. The impact of socioeconomic factors on municipal solid waste generation in São Paulo, Brazil. Waste Management and Research, 1 1, 36(1), pp. 79-85.

Xu, L., Gao, P., Cui, S. & Liu, C., 2013. A hybrid procedure for MSW generation forecasting at multiple time scales in Xiamen City, China. Waste Management, 6, 33(6), pp. 1324-1331.

Xu, L. et al., 2016. Path analysis of factors influencing household solid waste generation: a case study of Xiamen Island, China. Journal of Material Cycles and Waste Management, 1 4, 18(2), pp. 377-384.

Younes, M. K. et al., 2015. Solid waste forecasting using modified ANFIS modeling. Journal of the Air and Waste Management Association, 3 10, 65(10), pp. 1229-1238.

#2 – Waste management

Abbasi, M. & Abduli, M. A., 2013. Forecasting Municipal Solid waste Generation by Hybrid Support Vector Machine and Partial Least Square Model. Int. J. Environ. Res, 7(1), pp. 27-38.

Ali Abdoli, M., Falah Nezhad, M., Salehi Sede, R. & Behboudian, S., 2012. Longterm forecasting of solid waste generation by the artificial neural networks. Environmental Progress and Sustainable Energy, 12, 31(4), pp. 628-636.

Al-Khatib, I. A. et al., 2015. Public perception of hazardousness caused by current trends of municipal solid waste management. Waste Management, 1 2, Volume 36, pp. 323-330.

Anilkumar, P. & Chithra, K., 2016. Land Use Based Modelling of Solid Waste Generation for Sustainable Residential Development in Small/Medium Scale Urban Areas. Procedia Environmental Sciences, Volume 35, pp. 229-237.

Biswas, A. & De, A. K., 2016. A Fuzzy Goal Programming Approach for Solid Waste Management Under Multiple Uncertainties. Procedia Environmental Sciences, Volume 35, pp. 245-256.

Hazra, I. T., 2017. Prediction of municipal solid waste generation for developing countries in temporal scale: A fuzzy inference system approach, s.l.: s.n.

Intharathirat, R., Abdul Salam, P., Kumar, S. & Untong, A., 2015. Forecasting of municipal solid waste quantity in a developing country using multivariate grey models. Waste Management, 1 5, Volume 39, pp. 3-14.

Kannangara, M., Dua, R., Ahmadi, L. & Bensebaa, F., 2018. Modeling and prediction of regional municipal solid waste generation and diversion in Canada using machine learning approaches. Waste Management, 1 4, Volume 74, pp. 3-15.

Kolekar, K., Hazra, T. & Chakrabarty, S., 2016. A Review on Prediction of Municipal Solid Waste Generation Models. Procedia Environmental Sciences, Volume 35, pp. 238-244.

Lebersorger, S. & Beigl, P., 2011. Municipal solid waste generation in municipalities: Quantifying impacts of household structure, commercial waste and domestic fuel. Waste Management, 9, 31(9-10), pp. 1907-1915.

Oribe-Garcia, I. et al., 2015. Identification of influencing municipal characteristics regarding household waste generation and their forecasting ability in Biscay. Waste Management, 1 5, Volume 39, pp. 26-34.

Pan, A., Yu, L. & Yang, Q., 2019. Characteristics and forecasting of municipal solid waste generation in China. Sustainability (Switzerland), 1 3.11(5).

Prades, M., Gallardo, A. & Ibàñez, M. V., 2015. Factors determining waste generation in Spanish towns and cities. Environmental Monitoring and Assessment, 20 11.187(1).

Rimaityte, I. et al., 2012. Application and evaluation of forecasting methods for municipal solid waste generation in an Eastern-European city. Waste Management and Research, 1, 30(1), pp. 89-98.

Solano Meza, J. K., Orjuela Yepes, D., Rodrigo-Ilarri, J. & Cassiraga, E., 2019. Predictive analysis of urban waste generation for the city of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks. Heliyon, 1 11.5(11).

Song, J. et al., 2014. Simulated annealing based hybrid forecast for improving daily municipal solid waste generation prediction. Scientific World Journal, Volume 2014.

Soni, U., Roy, A., Verma, A. & Jain, V., 2019. Forecasting municipal solid waste generation using artificial intelligence models—a case study in India. SN Applied Sciences, 2.1(2).

Town, J., Sudan JEWM, S., Lomeling, D. & Wani Kenyi, S., s.d. Forecasting weekly SW generation using ANNs and ARMA models in Forecasting solid waste generation in Juba Town, South Sudan using Artificial Neural Networks (ANNs) and Autoregressive Moving Averages (ARMA), s.l.: s.n.

Verma, A., Kumar, A. & Singh, N. B., 2019. Application of Multi Linear Model for Forecasting Municipal Solid Waste Generation in Lucknow City: A Case Study. Current World Environment, 31 12, 14(3), pp. 421-432.

Vieira, V. H. d. M. & Matheus, D. R., 2018. The impact of socioeconomic factors on municipal solid waste generation in São Paulo, Brazil. Waste Management and Research, 1 1, 36(1), pp. 79-85.

Xu, L., Gao, P., Cui, S. & Liu, C., 2013. A hybrid procedure for MSW generation forecasting at multiple time scales in Xiamen City, China. Waste Management, 6, 33(6), pp. 1324-1331.

Xu, L. et al., 2016. Path analysis of factors influencing household solid waste generation: a case study of Xiamen Island, China. Journal of Material Cycles and Waste Management, 1 4, 18(2), pp. 377-384.

Younes, M. K. et al., 2015. Solid waste forecasting using modified ANFIS modeling. Journal of the Air and Waste Management Association, 3 10, 65(10), pp. 1229-1238.

#3 – Parking

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.

#4 Pollution

(...)

#5 – Crowd management

Airinei, D. & Homocianu, D., 2010. Data visualization in business intelligence. s.l., s.n., pp. 164-167.

Albino, V., Berardi, U. & Dangelico, R. M., 2015. Smart cities: Definitions, dimensions, performance, and initiatives. Journal of Urban Technology, 22(1), pp. 3-21.

Anon., 2016. European Handbook of Crowdsourced Geographic Information. s.l.:Ubiquity Press.

Benevolo, C., Dameri, R. P. & D’Auria, B., 2016. Smart mobility in smart city action taxonomy, ICT intensity and public benefits. Em: Lecture Notes in Information Systems and Organisation. s.l.:Springer Heidelberg, pp. 13-28.

Connors, E., s.d. Guidelines for Law Enforcement | Planning And Managing Security For Major Special Events: IL J, s.l.: s.n.

Daraio, C. et al., 2016. Efficiency and effectiveness in the urban public transport sector: A critical review with directions for future research. s.l.:Elsevier.

Han, Y. et al., 2019. Short-term prediction of bus passenger flow based on a hybrid optimized LSTM network. ISPRS International Journal of Geo-Information, 22 8.8(9).

Heldens, S. & Litvak, N., 2018. Scalable Detection of Crowd Motion Patterns, s.l.: s.n.

Heydecker, B. G. & Addison, J. D., 2011. Analysis and modelling of traffic flow under variable speed limits. Transportation Research Part C: Emerging Technologies, 19(2), pp. 206-217.

Khozium, M. O., Abuarafah, A. G. & Abdrabou, E., 2012. A Proposed Computer-Based System Architecture for Crowd Management of Pilgrims using Thermography, s.l.: s.n.

Klauser, F., 2013. Spatialities of security and surveillance: Managing spaces, separations and circulations at sport mega events. Geoforum, 10, Volume 49, pp. 253-262.

Kuechler, B. & Vaishnavi, V., 2011. Promoting Relevance in IS Research: An Informing System for Design Science Research, s.l.: s.n.

Larsson, A. & Ranudd, E., s.d. The Analysis of Pedestrian Movement and Behaviour of Different Crowds during Stadium Egress, s.l.: s.n.

Leung, I. X. Y., Chan, S.-Y., Hui, P. & Lio', P., 2011. Intra-City Urban Network and Traffic Flow Analysis from GPS Mobility Trace. 29 5.

Li, Y. et al., 2017. Forecasting short-term subway passenger flow under special events scenarios using multiscale radial basis function networks. Transportation Research Part C: Emerging Technologies, 1 4, Volume 77, pp. 306-328.

Löhner, R., Haug, E., Zinggerling, C. & Oñate, E., 2018. Real-time micro-modelling of city evacuations. Computational Particle Mechanics, 1 1, 5(1), pp. 71-86.

Matheus, R., Janssen, M. & Maheshwari, D., 2018. Data science empowering the public: Data-driven dashboards for transparent and accountable decision-making in smart cities. Government Information Quarterly, 1 7.

Mohan Rao, A. & Ramachandra Rao, K., 2012. MEASURING URBAN TRAFFIC CONGESTION – A REVIEW. International Journal for Traffic and Transport Engineering, 12, 2(4), pp. 286-305.

Ramos Sampaio, B. & Sampaio, Y., s.d. Efficiency Analysis of Public Transport Systems: Lessons for Institutional Planning, s.l.: s.n.

Schaffers, H. et al., 2011. LNCS 6656 - Smart Cities and the Future Internet: Towards Cooperation Frameworks for Open Innovation, s.l.: s.n.

Schönfelder, S. ;., Axhausen, K. W. ;., Antille, N. ;. & Bierlaire, M., 2002. Exploring the potentials of automatically collected GPS data for travel behaviour analysis a Swedish data source.

Siła-Nowicka, K. et al., s.d. Analysis of Human Mobility Patterns from GPS Trajectories and Contextual Information, s.l.: s.n.

Taibah, H. & Arlikatti, S., 2015. Crowd Management at Pilgrimage Events 188, s.l.: s.n.

Wei, Y. & Chen, M. C., 2012. Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks. Transportation Research Part C: Emerging Technologies, 21(1), pp. 148-162.

Xue, R., Sun, D. J. & Chen, S., 2015. Short-term bus passenger demand prediction based on time series model and interactive multiple model approach. Discrete Dynamics in Nature and Society, Volume 2015.

Yang, J. et al., 2010. Accuracy characterization of cell tower localization. s.l., s.n., pp. 223-226.

Task 1.2 Services and Use Cases refinement

For the definition of the services and use cases several meetings were taken place with several departments of the city of Lisbon, namely with the Lisbon Urban Intelligence Center (CGIUL) that make the bridge and identified the needs of the different departments regarding services that can be useful for their operation. For the definition and refinement of each case study regular meetings were made with:

  • EMEL (a municipal company that manages parking and a bike sharing system in Lisbon) for the definition of the case study #1 – Micromobility
  • DMHU (Municipal Department for Urban Hygiene) for the definition of the case study #2 – Waste management
  • DMMC/DS (????) and SMPC (Municipality Service for Civil Protection) for the definition of the case study #4 – Pollution
  • Contacts with CARRIS (a bus service provider in Lisbon) for the definition of the case study #5 – Crowd management.

The definition of the case studies along with the services they can provide is an iterative process and is dependent of the characteristics and limitations of the datasets that consequently will influence the results, obtained in the modelling phase. To guarantee that the final models provide the necessary basis to produce a service that could be useful for the city of Lisbon, adjustments are made in the initial questions addressed, always with straight cooperation between the technical team that is developing the models and the several departments/entities interested in the development of the project.