Análise de Padrões Espaciais e Temporais da Mobilidade de Táxis em San Francisco e Roma
Resumo
A disponibilidade de registros de localização e tempo de serviços de táxi possibilita a análise de padrões de mobilidade em centros urbanos e melhor compreensão das rotinas da população local. Este trabalho utiliza um conjunto de dados com mais de trinta milhões de registros de localização e tempo de táxis de San Francisco e Roma para comparar padrões de mobilidade urbana nessas duas metrópoles. As rotas anômalas dos táxis em ambas as cidades foram encontradas e discutidas, assim como as rotas mais frequentes em San Francisco. Os resultados indicam a viabilidade do algoritmo utilizado para corrigir as distâncias das rotas com erros de GPS em que o táxi estava parado.
Referências
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Bracciale, L., Bonola, M., Loreti, P., Bianchi, G., Amici, R., and Rabuffi, A. (2014). CRAWDAD dataset roma/taxi (v. 2014-07-17). Downloaded from http://crawdad.org/roma/taxi/20140717.
Castro, P. S., Zhang, D., Chen, C., Li, S., and Pan, G. (2013). From Taxi GPS Traces to Social and Community Dynamics: A Survey. ACM Comput. Surv., 46(2):17:1–17:34.
Chen, C. (2014). Understanding social and community dynamics from taxi GPS data. PhD thesis, Evry, Institut national des t´el´ecommunications.
Cunha, F. D., Alvarenga, D. A., Viana, A. C., Mini, R. A., and Loureiro, A. A. (2015). Understanding Interactions in Vehicular Networks Through Taxi Mobility. In Proceedings of the 12th ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, & Ubiquitous Networks, pages 17–24. ACM.
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Gibbons, J. D. and Chakraborti, S. (2003). Nonparametric statistical inference. Marcel Dekker, New York.
Kellner, L. and Egger, R. (2016). Tracking Tourist Spatial-Temporal Behavior in Urban Places, A Methodological Overview and GPS Case Study. In Information and Communication Technologies in Tourism 2016, pages 481–494. Springer.
Li, B., Zhang, D., Sun, L., Chen, C., Li, S., Qi, G., and Yang, Q. (2011). Hunting or waiting? discovering passenger-finding strategies from a large-scale real-world taxi dataset. In Pervasive Computing and Communications Workshops (PERCOM Workshops), 2011 IEEE International Conference on, pages 63–68. IEEE.
Liu, S., Wang, S., Liu, C., and Krishnan, R. (2015). Understanding taxi drivers’ routing choices from spatial and social traces. Frontiers of Computer Science, 9(2):200–209.
Murashkin, M. and Geroliminis, N. (2015). Analysis of taxi drivers’ behavior with GPS data from a megacity. In hEART, Lyngby. European Association for Research in Transportation (hEART), Department of Transport at Technical University of Denmark (DTU Transport).
Piorkowski, M., Sarafijanovic-Djukic, N., and Grossglauser, M. (2009). CRAWDAD dataset epfl/mobility (v. 2009-02-24). Downloaded from http://crawdad.org/epfl/mobility/20090224.
Robusto, C. (1957). The cosine-haversine formula. The American Mathematical Monthly, 64(1):38–40.
Rossi, L., Walker, J., and Musolesi, M. (2015). Spatio-temporal techniques for user identification by means of gps mobility data. EPJ Data Science, 4(1):1–16.
Siła-Nowicka, K., Vandrol, J., Oshan, T., Long, J. A., Demsar, U., and Fotheringham, A. S. (2015). Analysis of human mobility patterns from GPS trajectories and contextual information. International Journal of Geographical Information Science, pages 1–26.
Thang, N. V., Thang, P. M., and Tan, T. D. (2012). The performance improvement of a lowcost ins/gps integration system using the street return algorithm. Vietnam Journal of Mechanics, 34(4):271–280.
Valero, B., Luis, J., Julián, A., Belén, A., Villén, G., and Natalia (2014). GNSS. GPS: Fundamentos y aplicaciones en geom´atica. Editorial de la Universidad Polit´ecnica de Valencia, Valencia.
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Zhang, Y. (2014). How do taxis work in beijing? Master of urban and regional planning, University of California, Los Angeles, Los Angeles.
Zheng, Z., Rasouli, S., and Timmermans, H. (2015). Two-regime Pattern in Human Mobility: Evidence from GPS Taxi Trajectory Data. Geographical Analysis.
Zhu, Y., Zheng, Y., Zhang, L., Santani, D., Xie, X., and Yang, Q. (2012). Inferring taxi status using gps trajectories. arXiv preprint arXiv:1205.4378.
Bracciale, L., Bonola, M., Loreti, P., Bianchi, G., Amici, R., and Rabuffi, A. (2014). CRAWDAD dataset roma/taxi (v. 2014-07-17). Downloaded from http://crawdad.org/roma/taxi/20140717.
Castro, P. S., Zhang, D., Chen, C., Li, S., and Pan, G. (2013). From Taxi GPS Traces to Social and Community Dynamics: A Survey. ACM Comput. Surv., 46(2):17:1–17:34.
Chen, C. (2014). Understanding social and community dynamics from taxi GPS data. PhD thesis, Evry, Institut national des t´el´ecommunications.
Cunha, F. D., Alvarenga, D. A., Viana, A. C., Mini, R. A., and Loureiro, A. A. (2015). Understanding Interactions in Vehicular Networks Through Taxi Mobility. In Proceedings of the 12th ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, & Ubiquitous Networks, pages 17–24. ACM.
Ding, L., Fan, H., and Meng, L. (2015). Understanding taxi driving behaviors from movement data. In AGILE 2015, pages 219–234. Springer.
Gibbons, J. D. and Chakraborti, S. (2003). Nonparametric statistical inference. Marcel Dekker, New York.
Kellner, L. and Egger, R. (2016). Tracking Tourist Spatial-Temporal Behavior in Urban Places, A Methodological Overview and GPS Case Study. In Information and Communication Technologies in Tourism 2016, pages 481–494. Springer.
Li, B., Zhang, D., Sun, L., Chen, C., Li, S., Qi, G., and Yang, Q. (2011). Hunting or waiting? discovering passenger-finding strategies from a large-scale real-world taxi dataset. In Pervasive Computing and Communications Workshops (PERCOM Workshops), 2011 IEEE International Conference on, pages 63–68. IEEE.
Liu, S., Wang, S., Liu, C., and Krishnan, R. (2015). Understanding taxi drivers’ routing choices from spatial and social traces. Frontiers of Computer Science, 9(2):200–209.
Murashkin, M. and Geroliminis, N. (2015). Analysis of taxi drivers’ behavior with GPS data from a megacity. In hEART, Lyngby. European Association for Research in Transportation (hEART), Department of Transport at Technical University of Denmark (DTU Transport).
Piorkowski, M., Sarafijanovic-Djukic, N., and Grossglauser, M. (2009). CRAWDAD dataset epfl/mobility (v. 2009-02-24). Downloaded from http://crawdad.org/epfl/mobility/20090224.
Robusto, C. (1957). The cosine-haversine formula. The American Mathematical Monthly, 64(1):38–40.
Rossi, L., Walker, J., and Musolesi, M. (2015). Spatio-temporal techniques for user identification by means of gps mobility data. EPJ Data Science, 4(1):1–16.
Siła-Nowicka, K., Vandrol, J., Oshan, T., Long, J. A., Demsar, U., and Fotheringham, A. S. (2015). Analysis of human mobility patterns from GPS trajectories and contextual information. International Journal of Geographical Information Science, pages 1–26.
Thang, N. V., Thang, P. M., and Tan, T. D. (2012). The performance improvement of a lowcost ins/gps integration system using the street return algorithm. Vietnam Journal of Mechanics, 34(4):271–280.
Valero, B., Luis, J., Julián, A., Belén, A., Villén, G., and Natalia (2014). GNSS. GPS: Fundamentos y aplicaciones en geom´atica. Editorial de la Universidad Polit´ecnica de Valencia, Valencia.
Wikipedia (2015). Decimal Degrees. https://en.wikipedia.org/wiki/Decimal_degrees.
Zhang, Y. (2014). How do taxis work in beijing? Master of urban and regional planning, University of California, Los Angeles, Los Angeles.
Zheng, Z., Rasouli, S., and Timmermans, H. (2015). Two-regime Pattern in Human Mobility: Evidence from GPS Taxi Trajectory Data. Geographical Analysis.
Zhu, Y., Zheng, Y., Zhang, L., Santani, D., Xie, X., and Yang, Q. (2012). Inferring taxi status using gps trajectories. arXiv preprint arXiv:1205.4378.
Publicado
04/07/2016
Como Citar
MONTEIRO, Cristiano Martins; DA SILVA, Fábio Rocha; MURTA, Cristina Duarte.
Análise de Padrões Espaciais e Temporais da Mobilidade de Táxis em San Francisco e Roma. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 43. , 2016, Porto Alegre.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 1736-1747.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2016.9523.
