Use of supervised learning for analyzing the reliability of crowdsourcing data on bus positioning
Abstract
Intelligent Transportation Systems allows sensors and GPS devices to monitor public transport systems in Smart Cities. Capturing and processing this data should, in theory, allow systems to make the public transport more reliable and predictable for the citizens, which would improve the quality of life of the urban population and the environment. Insufficient or low-quality data, nevertheless, may prevent its use on such real-time systems. This work studies the use of data obtained from crowdsourcing as an alternative to augment this data. In order to mitigate the uncertainties introduced by the crowdsourced data, this work proposes a reliability model for crowdsourced data conceived for the São Paulo bus-based public transport system.
References
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Sussman, J. S. (2008). Perspectives on intelligent transportation systems (ITS). Springer.
Vapnik, V. N. (1998). Statistical learning theory, volume 1. Wiley New York.
Wang, J.-n., Chen, X.-m., and Guo, S.-x. (2009). Bus travel time prediction model with ν-support vector regression. In Intelligent Transportation Systems, 2009. ITSC’09. 12th Intern. IEEE Conf. on, pages 1–6. IEEE.
Witten, I. H., Frank, E., Hall, M. A., and Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
Wu, C.-H., Ho, J.-M., and Lee, D.-T. (2004). Travel-time prediction with support vector regression. IEEE transactions on intelligent transportation systems, 5(4):276–281.
Zhu, T., Ma, F., Ma, T., and Li, C. (2011). The prediction of bus arrival time using global positioning system data and dynamic traffic information. In Wireless and Mobile Networking Conference (WMNC), 2011 4th Joint IFIP, pages 1–5. IEEE.
Altinkaya, M. and Zontul, M. (2013). Urban bus arrival time prediction: A review of computational models. International Journal of Recent Technology and Engineering, 2(4):164–169.
Batista, D. M., Goldman, A., Hirata, R., Kon, F., Costa, F. M., and Endler, M. (2016). Interscity: Addressing future internet research challenges for smart cities. In 7th International Conference on the Network of the Future (NOF), pages 1–6.
Biagioni, J., Gerlich, T., Merrifield, T., and Eriksson, J. (2011). Easytracker: automatic transit tracking, mapping, and arrival time prediction using smartphones. In Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems, pages 68–81. ACM.
Bishop, C. M. (2006). Pattern recognition and machine learning (information science and statistics) springer-verlag new york. Inc. Secaucus, NJ, USA.
Caprotti, F. (2016). Eco-cities and the transition to low carbon economies. Springer.
Cerotti, D., Distefano, S., Merlino, G., and Puliafito, A. (2016). A crowd-cooperative approach for intelligent transportation systems. IEEE Transactions on Intelligent Transportation Systems.
Chowdhury, M. A. and Sadek, A. W. (2003). Fundamentals of intelligent transportation systems planning. Artech House.
Cullina, E., Conboy, K., and Morgan, L. (2015). Measuring the crowd: a preliminary taxonomy of crowdsourcing metrics. In Proceedings of the 11th International Symposium on Open Collaboration, page 7. ACM.
Cury, M. J. F. and Marques, J. A. L. F. (2016). A cidade inteligente: uma reterritorialização/smart city: A reterritorialization. Redes, 22(1).
Daniel, F., Kucherbaev, P., Cappiello, C., Benatallah, B., and Allahbakhsh, M. (2018). Quality control in crowdsourcing: A survey of quality attributes, assessment techniques, and assurance actions. ACM Comput. Surv., 51(1):7:1–7:40.
De Santis, R., Fasano, A., Mignolli, N., and Villa, A. (2014). Smart city: fact and fiction. Munich Personal RePEc Archive.
Figueiredo, G. D. S. (2016). Técnicas de simulação para apoio à decisão em planejamento urbano. Master’s thesis, Universidade Federal do Rio de Janeiro.
Figueiredo, L. M. B. (2005). Sistemas inteligentes de transporte. PhD thesis, Univ. do Porto.
Ghotra, B., McIntosh, S., and Hassan, A. E. (2015). Revisiting the impact of classification techniques on the performance of defect prediction models. In Proceedings of the 37th International Conference on Software Engineering-Volume 1, pages 789–800. IEEE Press.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., and Tatham, R. L. (2009). Análise multivariada de dados. Bookman Editora.
Hosmer Jr, D. W., Lemeshow, S., and Sturdivant, R. X. (2013). Applied logistic regression, volume 398. John Wiley & Sons.
Howe, J. (2006). The rise of crowdsourcing. Wired magazine, 14(6):1–4.
Hwang, M., Kemp, J., Lerner-Lam, E., Neuerburg, N., and Okunieff, P. (2006). Advanced public transportation systems: state of the art update 2006. Technical Report FTA-NJ-26-7062-06.1, U.S. Department of Transportation.
Kohavi, R. (1995). The power of decision tables. In European conference on machine learning, pages 174–189. Springer.
Kormáksson, M., Barbosa, L., Vieira, M. R., and Zadrozny, B. (2014). Bus travel time predictions using additive models. In IEEE Intl. Conference on Data Mining, pages 875–880. IEEE.
Magalhães, C. T. d. A. (2008). Avaliação de tecnologias de rastreamento por gps para o monitoramento do transporte público por ônibus. Master’s thesis, Univ. Federal do Rio de Janeiro.
Mashhadi, A. J. and Capra, L. (2011). Quality control for real-time ubiquitous crowdsourcing. In Proceedings of the 2Nd International Workshop on Ubiquitous Crowdsouring, UbiCrowd ’11, pages 5–8, New York, NY, USA. ACM.
Misra, A., Gooze, A., Watkins, K., Asad, M., and Le Dantec, C. (2014). Crowdsourcing and its application to transportation data collection and management. Transportation Research Record: Journal of the Transportation Research Board, 2414:1–8.
Mitchell, T. M. (1997). Machine learning. 1997. Burr Ridge, IL: McGraw Hill, 45(37):870–877.
Mousa, H., Mokhtar, S. B., Hasan, O., Younes, O., Hadhoud, M., and Brunie, L. (2015). Trust management and reputation systems in mobile participatory sensing applications. Comput. Netw., 90(C):49–73.
Pedersen, J., Kocsis, D., Tripathi, A., Tarrell, A., Weerakoon, A., Tahmasbi, N., Xiong, J., Deng, W., Oh, O., and de Vreede, G.-J. (2013). Conceptual foundations of crowdsourcing: A review of is research. In Hawaii International Conference on System Sciences. IEEE.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Pons, I., Monteiro, J., and Speicys Cardoso, R. (2015). Big data para análise de métricas de qualidade de transporte: metodologia e aplicação. Technical report, ANTP.
Sussman, J. S. (2008). Perspectives on intelligent transportation systems (ITS). Springer.
Vapnik, V. N. (1998). Statistical learning theory, volume 1. Wiley New York.
Wang, J.-n., Chen, X.-m., and Guo, S.-x. (2009). Bus travel time prediction model with ν-support vector regression. In Intelligent Transportation Systems, 2009. ITSC’09. 12th Intern. IEEE Conf. on, pages 1–6. IEEE.
Witten, I. H., Frank, E., Hall, M. A., and Pal, C. J. (2016). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
Wu, C.-H., Ho, J.-M., and Lee, D.-T. (2004). Travel-time prediction with support vector regression. IEEE transactions on intelligent transportation systems, 5(4):276–281.
Zhu, T., Ma, F., Ma, T., and Li, C. (2011). The prediction of bus arrival time using global positioning system data and dynamic traffic information. In Wireless and Mobile Networking Conference (WMNC), 2011 4th Joint IFIP, pages 1–5. IEEE.
Published
2018-07-25
How to Cite
NEVES, Diego Vieira; DIAS, Felipe Cordeiro Alves; CORDEIRO, Daniel.
Use of supervised learning for analyzing the reliability of crowdsourcing data on bus positioning. In: BRAZILIAN WORKSHOP ON INTELLIGENT CITIES (WBCI), 1. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 70-79.
DOI: https://doi.org/10.5753/wbci.2018.3229.
