Prediction System for the Management of Bicycle Sharing Systems
- 296 Downloads
Abstract
Bicycle sharing systems are very common in urban areas; their goal is to improve citizens’ mobility around the city. The efficient management of bicycle stations is the greatest challenge for such systems and makes it difficult to satisfy the users’ demand for bicycles. To overcome this challenge, it is important to predict their use and to constantly monitor the number of bicycles available at stations, ensuring that they are distributed according to the demand in those places. In this work, we analyse the demand for bicycles per user and predict the routes users may travel to determine the possibility of predicting the behaviour of users. To this end, meteorological information and historical data on the use of the stations were incorporated into the system.
Keywords
Demand forecasting Classifiers Route predictionNotes
Acknowledgments
This work was supported by the Spanish Ministry, Ministerio de Economía y Competitividad and FEDER funds. Project. SURF: Intelligent System for integrated and sustainable management of urban fleets TIN2015-65515-C4-3-R.
References
- 1.
- 2.Haider, Z., Nikolaev, A., Kang, J.E., Kwon, C.: Inventory rebalancing through pricing in public bike sharing systems. Eur. J. Oper. Res. 270, 103–117 (2018)MathSciNetCrossRefGoogle Scholar
- 3.Wang, M., Zhou, X.: Bike-sharing systems and congestion: evidence from US cities. J. Transp. Geogr. 65, 147–154 (2017)CrossRefGoogle Scholar
- 4.Caggiani, L., Camporeale, R., Ottomanelli, M., Szeto, W.Y.: A modeling framework for the dynamic management of free-floating bike-sharing systems. Transp. Res. Part C: Emerg. Technol. 87, 159–182 (2018)CrossRefGoogle Scholar
- 5.Feng, C., Hillston, J., Reijsbergen, D.: Moment-based availability prediction for bike-sharing systems. Perform. Eval. 117, 58–74 (2017)CrossRefGoogle Scholar
- 6.Oliveira, G.N., Sotomayor, J.L., Torchelsen, R.P., Silva, C.T., Comba, J.L.D.: Visual analysis of bike-sharing systems. Comput. Graph. 60, 119–129 (2016)CrossRefGoogle Scholar
- 7.Lozano, Á., De Paz, J.F., Villarrubia, G., De La Iglesia, D.H., Bajo, J.: Multi-agent system for demand prediction and trip visualization in bike sharing systems. Appl. Sci. 8(1), 67 (2018)CrossRefGoogle Scholar
- 8.Otero, I., Nieuwenhuijsen, M.J., Rojas-Rueda, D.: Health impacts of bike sharing systems in Europe. Environ. Int. 115, 387–394 (2018)CrossRefGoogle Scholar
- 9.Haider, Z., Nikolaev, A., Kang, J.E., Kwon, C.: Inventory rebalancing through pricing in public bike sharing systems. Eur. J. Oper. Res. 270, 103–117 (2018)MathSciNetCrossRefGoogle Scholar
- 10.Zhang, Y., Brussel, M.J.G., Thomas, T., van Maarseveen, M.F.A.M.: Mining bike-sharing travel behavior data: an investigation into trip chains and transition activities. Comput. Environ. Urban Syst. 69, 39–50 (2018)CrossRefGoogle Scholar
- 11.Ji, S., Cherry, C.R., Han, L.D., Jordan, D.A.: Electric bike sharing: simulation of user demand and system availability. J. Clean. Prod. 85, 250–257 (2014)CrossRefGoogle Scholar
- 12.de Chardon, C.M., Caruso, G., Thomas, I.: Bike-share rebalancing strategies, patterns, and purpose. J. Transp. Geogr. 55, 22–39 (2016)CrossRefGoogle Scholar
- 13.De La Iglesia, D.H., De Paz, J.F., Villarrubia, G., Barriuso, A.L., Bajo, J., Corchado, J.M.: Increasing the intensity over time of an electric-assist bike based on the user and route: the bike becomes the gym. Sensors 18(1), 220 (2018)CrossRefGoogle Scholar