Abstract
Planning efficient public transport is a key issue in modern cities. When planning a route for a bus or the line for a tram or subway it is necessary to consider the demand of the people for this service. In this work we presented a method to use existing crowdsourcing data (like Waze and OpenStreetMap) and cloud services (like Google Maps) to support a transportation network decision making process. The method is based the Dempster-Shafer Theory to model transportation demand and uses data from Waze to provide a congestion probability and data from OpenStreetMap to provide information about location of facilities such as shops, in order to predict where people may need to start or end their trip using public transportation means. The paper also presents an example about how to use this method with real data. The example shows how to analyze the current availability of public transportation stops in order to discover its weak points.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Heilig, G.K., World urbanization prospects the 2011 revision. United Nations, Department of Economic and Social Affairs (DESA), Population Division, Population Estimates and Projections Section, New York (2012)
Harrison, C., et al.: Foundations for smarter cities. IBM J. Res. Dev. 54(4), 1–16 (2010)
Chourabi, H., et al. Understanding smart cities: an integrative framework. In: 45th Hawaii International Conference on System Science (HICSS), 2012. IEEE (2012)
Shafer, G.: A Mathematical Theory of Evidence, vol. 1. Princeton University Press, Princeton (1976)
Piro, G., et al.: Information centric services in Smart Cities. J. Syst. Softw. 88, 169–188 (2014)
Santos, L., Coutinho-Rodrigues, J., Antunes, C.H.: A web spatial decision support system for vehicle routing using Google Maps. Decis. Support Syst. 51(1), 1–9 (2011)
Haklay, M., Weber, P.: Openstreetmap: User-generated street maps. IEEE Pervasive Comput. 7(4), 12–18 (2008)
Neis, P., Zielstra, D., Zipf, A.: The street network evolution of crowdsourced maps: OpenStreetMap in Germany 2007–2011. Future Internet 4(1), 1–21 (2011)
Ciepłuch, B., et al.: Comparison of the accuracy of OpenStreetMap for Ireland with Google Maps and Bing Maps. In: Proceedings of the Ninth International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences. University of Leicester, 20–23 July 2010
Zilske, M., Neumann, A., Nagel, K.: OpenStreetMap for traffic simulation. In: Proceedings of the 1st European State of the Map–OpenStreetMap Conference (2011)
Klug, M. CS Transport-optimisation–a solution for web-based trip optimization basing on OpenStreetMap. In: 19th ITS World Congress (2012)
Joubert, J.W. Van Heerden, Q.: Large-scale multimodal transport modelling. Part 1: Demand generation (2013)
Boye, J., et al.: Walk this way: spatial grounding for city exploration. In: Mariani, J., Rosset, S., Garnier-Rizet, M., Devillers, L. (eds.) Natural Interaction with Robots, Knowbots and Smartphones, pp. 59–67. Springer, New York (2014)
Jacob, R., Zheng, J., Ciepłuch, B., Mooney, P., Winstanley, A.C.: Campus guidance system for international conferences based on OpenStreetMap. In: Carswell, J.D., Fotheringham, A.S., McArdle, G. (eds.) W2GIS 2009. LNCS, vol. 5886, pp. 187–198. Springer, Heidelberg (2009)
Silva, T.H., de Melo, P.O.S.V., Viana, A.C., Almeida, J.M., Salles, J., Loureiro, A.A.F.: Traffic condition is more than colored lines on a map: characterization of waze alerts. In: Jatowt, A., Lim, E.-P., Ding, Y., Miura, A., Tezuka, T., Dias, G., Tanaka, K., Flanagin, A., Dai, B.T. (eds.) SocInfo 2013. LNCS, vol. 8238, pp. 309–318. Springer, Heidelberg (2013)
Fire, M., et al.: Data mining opportunities in geosocial networks for improving road safety. In: IEEE 27th Convention of Electrical and Electronics Engineers in Israel (IEEEI) 2012. IEEE (2012)
Frez, J., et al.: Dealing with incomplete and uncertain context data in geographic information systems. In: Computer Supported Cooperative Work in Design (CSCWD), IEEE, Editor 2014, pp. 129–134. IEEE, Hsinchu, Taiwan (2014)
Yang, L., Wan, B.: A multimodal composite transportation network model and topological relationship building algorithm. In: International Conference on Environmental Science and Information Application Technology, 2009, ESIAT 2009. IEEE (2009)
Liu, S., et al.: Modeling and simulation on multi-mode transportation network. In: 2010 International Conference on Computer Application and System Modeling (ICCASM). IEEE (2010)
Xu, L., Gao, Z.: Bi-objective urban road transportation discrete network design problem under demand and supply uncertainty. In: IEEE International Conference on Automation and Logistics, 2008, ICAL 2008. IEEE (2008)
Castillo, E., et al.: The observability problem in traffic models: algebraic and topological methods. IEEE Trans. Intell. Transp. Syst. 9(2), 275–287 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Baloian, N., Frez, J., Pino, J.A., Zurita, G. (2015). Efficient Planning of Urban Public Transportation Networks. In: GarcÃa-Chamizo, J., Fortino, G., Ochoa, S. (eds) Ubiquitous Computing and Ambient Intelligence. Sensing, Processing, and Using Environmental Information. UCAmI 2015. Lecture Notes in Computer Science(), vol 9454. Springer, Cham. https://doi.org/10.1007/978-3-319-26401-1_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-26401-1_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26400-4
Online ISBN: 978-3-319-26401-1
eBook Packages: Computer ScienceComputer Science (R0)