Evaluation and prediction of patterns and behaviours of micro mobility in the city of Lisbon



To support new planning and management approaches altogether with new tools to evaluate impact and prediction of behaviours.


Research questions

  • What are the demographic, environmental and infrastructural characteristics of the GIRA stations with more demand?
  • What are the estimated pickups and drop offs in GIRA stations for a certain day period?




Challenge Brainstorming Session


Data Understanding


Information about bike pick ups and drop offs in 73 GIRA stations along with the routes made by GIRA users between January 2018 and October 2018 (697 415 trips with 130 838 with routes information).


Since the beggining of GIRA service there is an increase in utilization.


During week:

  • In the morning: 8h – 10h
  • In the afternoon: 17h – 20 h

During weekends:

  • 17h – 20h

Stations with more pick ups:

  • Av. República
  • Campo Grande
  • Av. Duque de Ávila
  • Alameda D. Afonso Henriques
  • Marquês de Pombal

The trips with the geometry of the routes were filtered according with a set of rules (36 874 from 130 838 trips):

  • Trips < 100 m and > 19 km
  • Trips duration < 1 min and > 45 min
  • Trips mean velocity < 5 km/h and > 25 km/h
  • Trips outside Lisbon
  • Trips outliers (Tukeys IQR)

To assess the demographic, environmental and infrastructural characteristics of the routes made by GIRA users, the routes were aggregated to an hexagonal grid (3 ha) along with the contextual data.



Data preparation 


All the trips for each station were filtered according with a set of rules (600 829 from 697 415):

  • Trips < 100 m and > 19 km and NOT NULL
  • Trips duration < 1 min and > 45 min
  • Trips mean velocity < 5 km/h and > 25 km/h




  • For each station contextual data was aggregated within a buffer of 260 m;
  • Was compued for each station the distance from the nearest university, train station, metro station;
  • Data from bike pickups and dropoffs was aggregated in each station in an hourly basis.


Demographic, environmental and infrastructural characteristics of the GIRA stations


Correlation between contextual data within each buffer station and bike pickups:





Modelling: Machine Learning


Random Forest Regressor:

  • RMSE: 265.23
  • MAE: 208.69
  • MAPE: 0.47




 Literature review


  • 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.